Developer Insights

Architecture of smart cities

Smart city services like smart water management, smart energy, smart mobility, smart buildings, etc., are based on a centralized system. A web of sensors spread across the city feeds data to this centralized system or control center by sensing activities in the physical environment. At the control center, the data is processed and stored as meaningful information. This information is shared across various departments of the city government for better coordination and data-driven decision-making.

This sounds pretty easy to implement, doesn't it?

But practically, it is not easy. Why?

Integrating various devices with different technologies with the existing communication infrastructure is one of the biggest challenges in developing a sustainable and efficient smart city.

ICT Architecture of a Smart City

The Information and Communication Technology (ICT) architecture of a smart city has four layers:

Sensing
Communication
Data
Service

Sensing Layer

This layer contains a varied set of IoT nodes deployed across an urban area. These nodes collect data about physical environment activities. An IoT node includes sensors, microchips, power supply, and network elements. Nodes are categorized as:

Constrained node: Operates in low-power environments with limited processing and data-transfer capabilities.
Unconstrained node: Has no constraints in power, processing, or data-transfer capabilities.

A node may function as constrained or unconstrained based on operational conditions. For example, a proximity sensor in a small office parking lot may be constrained, while the same in a large complex may be unconstrained.

Constrained nodes struggle with XML data due to overhead and parsing complexity. To address this, the W3C proposed the Efficient XML Interchange (EXI) format, which supports constrained devices.

EXI includes two encoding methods:

Schema-less encoding: Data is encoded directly and decoded without needing prior knowledge of the schema.
Schema-informed encoding: An XML schema is shared between processors, allowing optimized tag representation.

Schema-informed EXI enables constrained IoT nodes to become multipurpose by understanding and generating structured data formats efficiently.

Communication Layer

Every smart city system involves millions of IoT nodes. Each node requires a unique address, facilitated by IPv6 (128-bit address). However, IPv6 overhead is too high for constrained devices. Enter 6LoWPAN—a low-power protocol designed for these nodes.

Bridge routers convert IPv6 packets to 6LoWPAN and vice versa, enabling seamless communication.

Communication technologies include:

Unconstrained nodes: Wi-Fi, optic fiber, Ethernet, LTE, UMTS, and powerline communication.
Constrained nodes: IEEE 802.11 Low Power, Bluetooth Low Energy, IEEE 802.15.4, RFID, NFC, and PLC.

Data Layer

This is the intelligence layer of a smart city. It involves structured storage and processing of data from IoT nodes. Databases track:

All IoT nodes
Departments managing the nodes (e.g., water management)
Associated departmental data

Statistical models used include:

Predictive models: Analyze past and current data to forecast future events.
Descriptive models: Explain the relationship between events and their causes.
Decision models: Evaluate outcomes of decisions based on influencing factors.

ERP systems also play a role in managing interdepartmental data flows within this layer.

Service Layer

This cross-departmental layer integrates data from various city services (e.g., water, power, pollution, transport) through web and mobile applications. The layer supports not only internal government collaboration but also provides public access to subsets of data for transparency and innovation.

Many cities have addressed urban challenges successfully through smart technology. Notable examples include Barcelona, Tel Aviv, Amsterdam, Seoul, and Stockholm.

Developer Insights

Logging millions of requests every day and what it takes

HackerEarth's web servers handle millions of requests every day. These request logs can be analyzed to mine some really useful insights as well as metrics critical to the business, for example, the number of views per day, the number of views per sub product, most popular user navigation flow, etc.

Initial Thoughts

HackerEarth uses Django as its primary web development framework and a host of other components which have been customized for performance and scalability. During normal operations, our servers handle 80–90 requests/sec on an average and this surges to 200–250 requests/sec when multiple contests overlap in a time delta. We needed a system which could easily scale to a peak traffic of 500 requests/sec. Also, this system should add minimum processing overhead to the webservers, and the data collected needs to be stored for crunching and offline processing.

Architecture

Logging Architecture

The diagram above shows a high level architecture of our request log collection system. The solid connection lines represent the data flow between different components and the dotted lines represent the communications. The whole architecture is message based and stateless, so individual components can easily be removed/replaced without any downtime.

You can read a more detailed explanation about each component in the order of data flow.

Web Servers

On the web servers, we employ a Django Middleware that asynchronously retrieves required data for a given request and then forwards it to the Transporter Cluster servers. This is done using a thread and the middleware adds an overhead of 2 milli seconds to the Django request/response cycle.

class RequestLoggerMiddleware(object):
    """
    Logs data from requests
    """
    def process_request(self, request):
        if settings.LOCAL or settings.DEBUG:
            return None

        is_ajax = request.is_ajax()
        request.META['IS_AJAX'] = is_ajax

        before = datetime.datetime.now()

        DISALLOWED_USER_AGENTS = ["ELB-HealthChecker/1.0"]
        http_user_agent = request.environ.get('HTTP_USER_AGENT', '')

        if http_user_agent in DISALLOWED_USER_AGENTS:
            return None

        # this creates a thread which collects required data and forwards it to the transporter cluster
        run_async(log_request_async, request)
        after = datetime.datetime.now()

        log("TotalTimeTakenByMiddleware %s" % ((after - before).total_seconds()))
        return None

Transporter Cluster

The transporter cluster is an array of non-blocking Thrift servers for the sole purpose of receiving data from the web servers and routing them to any other component like MongoDB, RabbitMQ, Kafka, etc. Where a given message should be routed to is specified in the message itself from the webservers. There is only one-way communication from the webservers to the transporter servers, and this saves time spent in the acknowledgement of message reception by thrift servers. We may lose some request logs due to this, but we can afford to do so. The request logs are currently routed to the Kafka cluster. The communication between the webservers and the transporter servers takes 1–2 milli seconds on an average and can be horizontally scaled to handle an increase in load.

service DataTransporter {
    oneway void transport(1:map<string, string> message)
}

Kafka Cluster

Kafka is a high throughput distributed messaging system that supports the publish/subscribe messaging pattern. This messaging infrastructure enables us to build other pipelines that depend on this stream of request logs. Our Kafka cluster stores last 15 days' worth of logs, so we can make any new consumer that we implement start processing data 15 days back in time.

Useful reference for setting up a kafka cluster.

Pipeline Manager Server

This server manages the consumption of request log messages from the Kafka topics, storing them in MongoDB and then later moving them to Amazon S3 and Amazon Redshift. MongoDB acts merely as a staging area for the data consumed from the Kafka topics and this data is transferred to S3 at hourly intervals. Every file that is saved in S3 is loaded into Amazon Redshift, which is a data warehouse solution that can scale to petabytes of data. We use Amazon Redshift for analyzing/metrics calculation from request log data. This server works in conjunction with a RabbitMQ cluster which it uses to communicate about task completion and initiation.

Here is the script that loads data from S3 into Redshift. This script handles insertion of duplicate data first by removing any duplicate rows and then by inserting the new data.

def load_s3_delta_into_redshift(s3_delta_file_path):
    bigdata_bucket = settings.BIGDATA_S3_BUCKET

    attrs = {
        'bigdata_bucket': bigdata_bucket,
        's3_delta_file_path': s3_delta_file_path,
    }

    complete_delta_file_path = "s3://{bigdata_bucket}/{s3_delta_file_path}".format(**attrs)
    schema_file_path = "s3://{bigdata_bucket}/request_log/s3_col_schema.json".format(**attrs)

    data = {
        'AWS_ACCESS_KEY_ID': settings.AWS_ACCESS_KEY_ID,
        'AWS_SECRET_ACCESS_KEY': settings.AWS_SECRET_ACCESS_KEY,
        'LOG_FILE':  complete_delta_file_path,
        'schema_file_path': schema_file_path
    }

    S3_REDSHIFT_COPY_COMMAND = " ".join([
        "copy requestlog_staging from '{LOG_FILE}' ",
        "CREDENTIALS 'aws_access_key_id={AWS_ACCESS_KEY_ID};aws_secret_access_key={AWS_SECRET_ACCESS_KEY}'",
        "json '{schema_file_path}';"
    ]).format(**data)

    LOADDATA_COMMAND = " ".join([
        "begin transaction;",
        "create temp table if not exists requestlog_staging(like requestlog);",
        S3_REDSHIFT_COPY_COMMAND,
        'delete from requestlog using requestlog_staging where requestlog.row_id=requestlog_staging.row_id;',
        'insert into requestlog select * from requestlog_staging;',
        "drop table requestlog_staging;",
        'end transaction;'
    ])

    redshift_conn_args = {
        'host': settings.REDSHIFT_HOST,
        'port': settings.REDSHIFT_PORT,
        'username': settings.REDSHIFT_DB_USERNAME
    }

    REDSHIFT_CONNECT_CMD = 'psql -U {username} -h {host} -p {port}'.format(**redshift_conn_args)
    PSQL_LOADDATA_CMD = '%s -c "%s"' % (REDSHIFT_CONNECT_CMD, LOADDATA_COMMAND)

    returncode = subprocess.call(PSQL_LOADDATA_CMD, shell=True)
    if returncode != 0:
        raise Exception("Unable to load s3 delta file into redshift ", s3_delta_file_path)

What's next

Data is like gold for any web application. If done the right way, the insights that it can provide and the growth it can drive is amazing. There are dozens of features and insights that can be built with the requests logs, including recommendation engine, better content delivery, and improving the overall product. All of this is a step toward making HackerEarth better every day for our users.

This post was originally written for the HackerEarth Engineering blog by Praveen Kumar.

Developer Insights

Data Visualization packages in Python - Pandas

In the previous article, we saw how dplyr and tidyr packages in R can be used to visualize and plot data to gain a better understanding of the underlying properties. Now let’s explore the data visualization techniques in Python and see how similar tasks can be performed.

Pandas:

Pandas is a Python package aimed toward creating a platform to carry out practical data analysis and manipulation tasks. It has data structures that are equivalent to dataframe in R, and it can handle multiple types of data like SQL or Excel data, information present in the form of matrices, time series data, and labeled/unlabeled data. Here’s a preview of the tasks that can be carried out using Pandas:

groupby(..) function that is used to aggregate data through split-apply-combine operations
Merging, joining, slicing, reshaping and subsetting of data
Flexibility to change the dimension of data: addition and removal of columns
Handling of missing data similar to libraries in R

You can import Pandas just like you would import any other library in Python.

@ import pandas as pd

First step of dealing with Pandas involves reading data from a csv file.

@ data = pd.read_csv(file_path, header)

File_path: the location of the csv file to be read

Header: Can be None if you want the column heading to be Null. If column names are needed, then pass them as a list to the header argument.

After reading the data, placing it into a dataframe gives us the flexibility to perform various operations on it. To convert data into dataframe format use:

@ data_df = pd.DataFrame(data, column_list)

We are going to use the following dataframe in our later examples:

>>> marks_df

      names   marks

0     Ross      90
1     Joey      72
2     Monica    81
3     Phoebe    80
4     Chandler  45
5     Rachel    78

It is always important to have an estimate about the extreme values of the data. It is also convenient to have the data in a sorted format. To accomplish this, data can be sorted based on a column value in the dataframe using the sorting function:

@ df_sort = dataframe.sort_values(column, ascending)

column : column object of the dataframe

ascending: default value is True. If set to False, the data is sorted in descending order.

>>> list_sort = marks_df.sort_values(['marks'])
>>> list_sort

      names    marks
4    Chandler   45
1    Joey       72
5    Rachel     78
3    Phoebe     80
2    Monica     81
0    Ross       90

To get the entity with the maximum value (which is the last value in the sorted dataframe), tail(n) function can be used. n is the number of values from the last elements that need to be taken into consideration:

@ df_sort.tail(1)
>>> list_sort.tail(1)

   names  marks
0  Ross     90

Similarly, head() collects values from the top:

>> list_sort.head(2)

      names  marks
4   Chandler  45
1   Joey      72

Both head and tail, by default, will display 5 values from the top and bottom, respectively.

To get the information about the dataframe, use info():

@ marks_df.info()

>>> marks_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5

Data columns (total 2 columns):
names    6 non-null object
marks    6 non-null int64

dtypes: int64(1), object(1)
memory usage: 168.0+ bytes

In the examples that follow, we are going to use the following dataframe that contains the complete exam results of all the 6 students (there are 6 subjects):

>>> allmarks_df

When there are multiple entries for each object, the aggregate option comes into play. We use the groupby() function to accomplish it. To get the total marks for each student, we need to aggregate all the name objects using the sum function:

@ agg_object = dataframe.groupby(column_name, as_index)

column_name: takes the list of columns based on which grouping needs to be done.

as_index: default value is True and means that the columns mentioned in list will be considered as indices for the new dataframe formed. When set to False, numerical numbering starting from 0 is given as the index.

>>> marks_agg = allmarks_df.groupby('Name')
>>> total_df = marks_agg.sum()
>>> total_df

Name      Marks      
Ross       495
Chandler   404
Rachel     422
Monica     443
Joey       475
Phoebe     395


>>> total_df = allmarks_df.groupby('Name', as_index=False).sum()
>>> total_df

      Name     Marks
0     Ross      495
1     Chandler  404
2     Rachel    422
3     Monica    443
4     Joey      475
5     Phoebe    395

Data can also be plotted using Pandas, but it requires pyplot from matplotlib:

>>> import matplotlib.pyplot as plt
>>> total_df['Marks'].plot(title="Total marks of all students")
<matplotlib.axes.AxesSubplot object at 0x10cde0d10>
>>> plt.show()

Data Visualization - Pandas

>>> total_df['Marks'].plot.bar()
<matplotlib.axes.AxesSubplot object at 0x10c2d1e90>
>>> plt.show()

Data Visualization in Python - Pandas

To get the frequencies of the values in a particular column, use value_counts():

@ dataframe[column_name].value_counts()
>>> allmarks_df['Name'].value_counts()

Chandler   6
Ross       6
Rachel     6
Phoebe     6
Monica     6
Joey       6

Name: Name, dtype: int64

To get the unique values in a column:

@ dataframe[column_name].unique()
>>> allmarks_df['Name'].unique()
array(['Ross', 'Joey', 'Monica', 'Phoebe ', 'Chandler', 'Rachel'], dtype=object)

Dataframes can be accessed using the index too. ix() function is used to extract data using the index in numerical values:

@ dataframe.ix(index_range, columns_range)
>>> allmarks_df.ix[0:6,:]

      Name   Marks
0     Ross     77
1     Joey     73
2     Monica   80
3     Phoebe   58
4     Chandler 54
5     Rachel   51
6     Ross     98

>>> allmarks_df.ix[0:6,0]

0    Ross
1    Joey
2    Monica
3    Phoebe
4    Chandler
5    Rachel
6    Ross

Name: Name, dtype: object

>>> allmarks_df.ix[0:6,0:1]

       Name
0      Ross
1      Joey
2      Monica
3      Phoebe
4      Chandler
5      Rachel
6      Ross

Adding a column to data is quite easy in case of dataframe in Pandas:

@ dataframe[new_column] = value
>>> total_df['Pass'] = [total_df['Marks'][i] >= 420 for i in range(6)]
>>> total_df

Name      Marks   Pass             
Ross       495    True
Chandler   404    False
Rachel     422    True
Monica     443    True
Joey       475    True
Phoebe     395    False

loc() can be used to extract subset of a dataframe:

@ dataframe.loc[index / index_range]
>>> total_df.loc['Monica']

Marks     443
Pass     True

Name: Monica, dtype: object

>>> total_df.loc['Monica':'Phoebe ']

Name        Marks   Pass
Monica      443     True
Joey        475     True
Phoebe      395     False

iloc() is similar to loc() but here the index can be represented as numerals rather than as actual object names:

Subset of the dataframe can also be extracted by imposing a condition over the column values using logical operators:

>>> total_pass = total_df[total_df['Pass'] == True]
>>> total_pass

In the above example, all the rows with ‘Pass’ column value as True are separated out using the logical equality condition.

You can use the del function to delete a column.

@ del dataframe[column_name]
>>> del total_df['Pass']
>>> total_df

Data can be changed into different storage formats. stack() and unstack() functions are used for this. stack() is used to bring down the column names into index values and unstack() is used to revert the stacking action. Give it a try and see the output.

@ dataframe.stack()
>>> total_df.stack()
>>> total_df.unstack()

The rows and columns interchange positions after unstacking. We can revert this using the transpose function.

>>> total_df = total_df.T 
>>> total_df

Name     Ross  Chandler Rachel Monica Joey  Phoebe
Marks    495   404      422    443    475   395
Pass     True  False    True   True   True  False

>>> total_df = total_df.T
>>> total_df

Name     Marks   Pass
Ross      495    True
Chandler  404    False
Rachel    422    True
Monica    443    True
Joey      475    True
Phoebe    395    False

Subset of the dataframe can also be extracted by imposing a condition over the column values using logical operators:

>>> total_pass = total_df[total_df['Pass'] == True]
>>> total_pass

In the above example, all the rows with ‘Pass’ column value as True are separated out using the logical equality condition.

You can use the del function to delete a column.

@ del dataframe[column_name]
>>> del total_df['Pass']
>>> total_df

@ dataframe.stack()
>>> total_df.stack()
>>> total_df.unstack()

The rows and columns interchange positions after unstacking. We can revert this using the transpose function.

>>> total_df = total_df.T 
>>> total_df

Name     Ross  Chandler Rachel Monica Joey  Phoebe
Marks    495   404      422    443    475   395
Pass     True  False    True   True   True  False

>>> total_df = total_df.T
>>> total_df

Name     Marks   Pass
Ross      495    True
Chandler  404    False
Rachel    422    True
Monica    443    True
Joey      475    True
Phoebe    395    False

Subset of the dataframe can also be extracted by imposing a condition over the column values using logical operators:

>>> total_pass = total_df[total_df['Pass'] == True]
>>> total_pass

In the above example, all the rows with ‘Pass’ column value as True are separated out using the logical equality condition.

You can use the del function to delete a column.

@ del dataframe[column_name]
>>> del total_df['Pass']
>>> total_df

@ dataframe.stack()
>>> total_df.stack()
>>> total_df.unstack()

The rows and columns interchange positions after unstacking. We can revert this using the transpose function.

>>> total_df = total_df.T 
>>> total_df

Name     Ross  Chandler Rachel Monica Joey  Phoebe
Marks    495   404      422    443    475   395
Pass     True  False    True   True   True  False

>>> total_df = total_df.T
>>> total_df

Name     Marks   Pass
Ross      495    True
Chandler  404    False
Rachel    422    True
Monica    443    True
Joey      475    True
Phoebe    395    False

Mean and standard deviation for a particular value of the data can be calculated using standard functions. Mean: mean() and standard deviation: std()

@ dataframe[column_name].mean()
@ dataframe[column_name].std()

>>> total_df['Marks'].mean()
439.0

>>> total_df['Marks'].std()
39.744181964156716

>>> total_df['dis-Mean'] = total_df['Marks'] - total_df['Marks'].mean()
>>> total_df

      Name    Marks  dis-Mean
0     Ross      495      56.0
1     Chandler  404     -35.0
2     Rachel    422     -17.0
3     Monica    443       4.0
4     Joey      475      36.0
5     Phoebe    395     -44.0

The above example adds a column to the dataframe containing the deviation from the mean value of Marks.

Generating a time series data:

>>> time = pd.date_range('1/1/2012', periods=48, freq='MS')
>>> time

DatetimeIndex(['2012-01-01', '2012-02-01', '2012-03-01', '2012-04-01',
               '2012-05-01', '2012-06-01', '2012-07-01', '2012-08-01',
               '2012-09-01', '2012-10-01', '2012-11-01', '2012-12-01',
               '2013-01-01', '2013-02-01', '2013-03-01', '2013-04-01',
               '2013-05-01', '2013-06-01', '2013-07-01', '2013-08-01',
               '2013-09-01', '2013-10-01', '2013-11-01', '2013-12-01',
               '2014-01-01', '2014-02-01', '2014-03-01', '2014-04-01',
               '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
               '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01',
               '2015-01-01', '2015-02-01', '2015-03-01', '2015-04-01',
               '2015-05-01', '2015-06-01', '2015-07-01', '2015-08-01',
               '2015-09-01', '2015-10-01', '2015-11-01', '2015-12-01'],
              dtype='datetime64[ns]', freq='MS')

>>> stock = pd.DataFrame([np.random.randint(low=0, high=50) for i in range(48)], index=time, columns=['Value'])
>>> stock['dev'] = stock['Value'] - stock['Value'].mean()
>>> stock

             Value       dev
2012-01-01     37   10.104167
2012-02-01     48   21.104167
2012-03-01     41   14.104167
2012-04-01      5  -21.895833
2012-05-01     13  -13.895833
2012-06-01      7  -19.895833
2012-07-01     37   10.104167
2012-08-01     31    4.104167
2012-09-01     32    5.104167
2012-10-01     46   19.104167
2012-11-01     40   13.104167
2012-12-01     18   -8.895833
2013-01-01     38   11.104167
2013-02-01     23   -3.895833
2013-03-01     17   -9.895833
2013-04-01     21   -5.895833
2013-05-01     12  -14.895833
2013-06-01     40   13.104167
2013-07-01      9  -17.895833
2013-08-01     47   20.104167
2013-09-01     42   15.104167
2013-10-01      3  -23.895833
2013-11-01     24   -2.895833
2013-12-01     38   11.104167
2014-01-01     33    6.104167
2014-02-01     41   14.104167
2014-03-01     25   -1.895833
2014-04-01     11  -15.895833
2014-05-01     44   17.104167
2014-06-01     47   20.104167
2014-07-01      6  -20.895833
2014-08-01     49   22.104167
2014-09-01     11  -15.895833
2014-10-01     14  -12.895833
2014-11-01     23   -3.895833
2014-12-01     35    8.104167
2015-01-01     23   -3.895833
2015-02-01      1  -25.895833
2015-03-01     46   19.104167
2015-04-01     49   22.104167
2015-05-01     16  -10.895833
2015-06-01     25   -1.895833
2015-07-01     22   -4.895833
2015-08-01     36    9.104167
2015-09-01     30    3.104167
2015-10-01      3  -23.895833
2015-11-01     12  -14.895833
2015-12-01     20   -6.895833

Plotting the value of stock over 4 years using pyplot:

>>> stock['Value'].plot()
<matplotlib.axes.AxesSubplot object at 0x10a29bb10>
>>> plt.show()

Data visualization in Python Pandas

>>> stock['dev'].plot.bar() 
<matplotlib.axes.AxesSubplot object at 0x10c3e09d0>
>>> plt.show()

Plot.bar() Data Visualization in Python

There are more plotting tools like the seaborn library that can create more sophisticated plots. With these data visualization packages in R and Python, we are ready to advance to the core concepts of Machine Learning.

We have our Machine Learning practice section coming soon. Stay tuned.

Developer Insights

Components and implementations of Natural Language Processing

What is NLP?

If you walk to an intersection of computational linguistics, artificial intelligence, and computer science, you are more than likely to see Natural Language Processing (NLP) there as well. NLP involves computers processing natural language—human-generated language and not math or programming languages like Java or C++.

Famous examples of NLP include Apple’s SIRI (speech recognition/generation), IBM Watson (question answering), and Google Translate (machine translation). NLP extracts meaning from human language despite its inherent ambiguity.

Recall HAL from Stanley Kubrick’s film 2001: A Space Odyssey? HAL performed information retrieval, extraction, inference, played chess, displayed graphics, and engaged in conversation—tasks that modern NLP systems like Microsoft Cortana, Palantir, and Facebook graph search now perform.

NLP consists of Natural Language Generation (NLG) and Natural Language Understanding (NLU). NLG enables computers to write like humans. NLU involves comprehending text, managing ambiguities, and producing meaningful data.

What makes up NLP?

Entity Extraction

Entity extraction identifies and segments entities such as people, places, and organizations from text. It clusters variations of the same entity.

Entity type: Person, place, organization, etc.
Salience: Relevance score of the entity in context (0 to 1)

For example, variations like "Roark", "Mr. Roark", and "Howard Roark" are clustered under the same entity.

Google NLP API can analyze sentences for such entities. For instance, in a paragraph about Karna from the Mahabharata, the API might assign a salience score of 0.5 to Karna.

Syntactic Analysis

Syntactic analysis checks sentence structure and parts of speech. Using parsing algorithms and dependency trees, it organizes tokens based on grammar.

Semantic Analysis

Semantic analysis interprets sentence meaning in a context-free way, often using lexical and compositional semantics.

For instance, “Karna had an apple” may be interpreted as “Karna owned an apple,” not “ate.” World knowledge is essential for true understanding.

Sentiment Analysis

Sentiment analysis identifies emotions, opinions, and attitudes—subjective content. Scores range from -1 (negative) to +1 (positive), and magnitude reflects intensity.

Pragmatic Analysis

Pragmatic analysis considers the context of utterances—who, when, where, and why—to determine meaning. For instance, “You are late” could be informative or critical.

Linguists and NLP systems approach pragmatics differently, as noted here.

A Few Applications of NLP

AI chatbots helping with directions, bookings, and orders
Paraphrasing tools for marketing and content creation
Sentiment analysis for political campaigns
Analyzing user reviews on e-commerce platforms
Customer feedback analytics in call centers

Different APIs offer customized NLP features. Advanced NLP uses statistical machine learning and deep analytics to manage unstructured data.

Despite natural language's complexity, NLP has made impressive strides. Alan Turing would surely be proud.

Developer Insights

5 medical algorithms that are transforming the healthcare industry

This post focuses on the impact that medical algorithms have in the field of healthcare where you must be 100% right at all times. There is no room for errors because even the trivial errors can create a major impact. However, even the smartest and best-trained professionals are prone to errors. Tragedies due to human error are common in the medical industry.

Today, by using algorithms, doctors and care providers are able to determine exactly where to point the lasers for maximum impact with minimum collateral damage. Algorithms and genetic algorithms have made the way we treat patients more effective.

Here we list medical algorithms used in the healthcare industry:

Sampling
Fourier transform
Probabilistic data-matching
Proportional integral derivative
Predictive algorithm

Algorithms play a major role in the area of medical technology from large equipment to simple microcontrollers. Let’s look at the top algorithms that are used in the medical industry.

Sampling

The medical industry generates large amounts of data, which must be mined and sorted. Some facts include:

Every year, almost a million medical studies are published.
Additionally, 150,000 cancer-related studies are published annually.

The human brain is brilliant, but it has limits in processing. Computers help increase the number of lives saved by leveraging sampling algorithms in cognitive medical systems like IBM Watson Health.

IBM Watson Health uses AI and ML to infer treatment insights from patient data using sampling algorithms. Sampling involves selecting a few items from a large population for study. Techniques include:

Simple random sampling: Randomly selects members, each having equal probability.
Systematic sampling: Selects members at a fixed interval from a random starting point.
Stratified sampling: Divides population into groups (strata) and samples from each.
Clustering sampling: Divides large groups into smaller natural groups and applies sampling.

Fourier Transform

Fourier Transform is used in numerous medical imaging techniques like MRIs and ultrasounds. It breaks signals into sinusoidal components for analysis and reconstruction.

It transforms signals from the time domain into the frequency domain and back. This helps isolate and interpret components of a signal for accurate image construction.

via GIPHY

How MRI uses Fourier Transform:

MRI relies on water molecules in the body which respond to magnetic fields. The signals measured during scanning are a combination of sine waves. Fourier Transform decodes these into usable images.

Without Fourier Transform, modern imaging techniques would not be possible.

Probabilistic Data-Matching

This algorithm compares patient data against large databases to find the most likely matches, helping doctors make more informed diagnoses.

Probabilistic algorithms like Naive Bayes Classifier and PAIRS (Physician Assistant AI System) are commonly used to assist in accurate medical diagnosis.

Proportional Integral Derivative (PID)

PID is a feedback mechanism used in medical devices. For example, in Alabama Hospital, it helps manage blood pressure post-surgery by automatically adjusting medication levels.

PID works by reducing the difference between a desired outcome and the measured result using present, past, and predicted error values.

Predictive Algorithm

Predictive algorithms use historical and real-time data to forecast future medical events like cardiac arrests.

Examples include:

Time Series algorithm
Regression algorithm
Association algorithm
Clustering algorithm
Decision Tree algorithm

Predictive analytics helps doctors anticipate health conditions early and recommend preventive steps.

As algorithms grow in intelligence, they will play an even bigger role in healthcare. Doctors will consult with algorithms to provide precise, predictive care.

Want to learn more about algorithms?

Read how Mark Zuckerberg used the Elo Rating Algorithm in Facemash: Elo Algorithm: Common link between Facemash and Chess

Developer Insights

What is a fitness tracker and how does it work?

I’m waiting for the bus outside this store that’s displaying fantastic workout clothes. I sigh and then turn my head to see a billboard about a new gym in town. I’m torn between guilt, for not exercising ever, and surprise at the shiny pennies people are apparently willing to shell out to get be fit and stylish. The bus comes by, I manage to squeeze into a seat, and then I open my magazine to a page where the article is titled “This Is the Compelling Science behind Fitness Trackers.” The universe is telling me something, isn’t it?

I’m inspired enough to pen this article where I’ll talk about different sensors that make activity trackers tick.

Mini labs juggling complex data—fitness trackers

It looks like a watch. It looks like a smartphone. It is so much more…

Then there is an Apple Watch vs. Fitbit Blaze debate going on.

Fitbit Blaze Apple Watch
Fitbit and Apple are two of the most popular fitness tracker manufacturers (Source)

This wearable is a wrist-based monitor with sensors that tell you if you’ve been walking enough, sleeping and eating enough, jogging or sprinting, staying out too long in the sun, and it tracks a whole lot of other stuff to keep you as healthy as you can be.

Research might scare you into buying one

Do you know what WHO says? Every year, 38 million people die from noncommunicable diseases globally and cardiovascular diseases account for the most. It’s a wonder we don’t wear an activity band on each hand.

Although you can’t peg heart rate monitors as indicators of potentially fatal diseases, ensuring that you’ve lowered your resting heart rate is a valuable wellness/fitness metric. (Source)

These words—obesity, diabetes, physical inactivity, smoking, alcohol, salt, blood pressure, cholesterol, and sleep patterns—figure largely in reports related to health and articles about the changing lifestyles of millennials. So, do we need these wearable digital monitors? Hell, yeah!

Unravelling the mystery of these tiny marvels

Some people think that the system complexity of fitness trackers is much lesser than a full-blown smart watch. But I disagree. A fitness tracker is some sort of a genius companion you ought to have.

Getting down to the details...

Sensing layer: Collects data like footsteps, heart rate, temperature, etc., and sends it via GSM/GPRS/LTE.
MAC Layer: Manages device control, quality-of-service, and power.
Network layer: Handles transmission using IPV6.
Processing and storage layer: Analyzes and stores sensor data with security control.
Service layer: Delivers processed data to apps and services.

Now, let's take a close look at a few of these sensors...

Accelerometers

They measure acceleration forces to track motion, orientation, and direction—used in smartphones, rockets, and fitness bands.

How does Accelerometer work

Here’s a great video on how your iPhone knows up from down.

GPS

Fitness tracker GPS
The GPS plays an important role in a fitness tracker (Source)

Used for location tracking via satellite signals and trilateration. Essential for route tracking and emergency alerts.

Galvanic Skin Response Sensor

Measures electrical conductance of the skin. Tracks emotion-based sweating, aiding stress and fitness insights.

Output-1 of Galvanic Skin Response Sensor Output -2 of Galvanic Skin Response Sensor Output-3 of Galvanic Skin Response Sensor

Optical Heart Rate Monitor (OHRM)

Uses photoplethysmography to detect heart rate by shining light on skin and measuring changes in light absorption.

Optical Heart Rate Monitor
Source

Bioimpedance Sensors

working of a bio-impedance sensor

Measures resistance to current to determine heart rate, respiration, hydration, and more.

How does Bioimpedance sensors works

Temperature Sensors

Monitor body temperature for health insights. Crucial for athlete recovery and early detection of anomalies.

UV and Ambient Light Sensors

Help track sun exposure and adjust brightness/time metrics for user interface and circadian data.

Finding the right fitness tracker

Choose based on features—heart rate, sleep tracking, calorie counting, and more. Here are top options:

Garmin Vivosmart HR+ – Heart rate, sleep, steps, waterproof.

Garmin Vivosmart HR+ App

Fitbit Charge 2 – Tracks wellness, breathing, and activity.

Fitbit Charge 2 App

Jawbone UP4 – Measures heart rate, breath, sweat via Bioimpedance.

Samsung Gear Fit2 – Built-in GPS, activity auto-detection.

Other good bands: Withings Go, Microsoft Band 2, Basis Peak, Moov Now, Misfit Ray.

While no sensor is perfect, fitness trackers are getting smarter and more accurate. Use them right and they’ll lead you to a healthier life.

Authors

Meet our Authors

Get to know the experts behind our content. From industry leaders to tech enthusiasts, our authors share valuable insights, trends, and expertise to keep you informed and inspired.

Ruehie Jaiya Karri

Kumari Trishya

Hiring Strategies

Forecasting Tech Hiring Trends For 2023 With 6 Experts

2023 is here, and it is time to look ahead. Start planning your tech hiring needs as per your business requirements, revamp your recruiting processes, and come up with creative ways to land that perfect “unicorn candidate”!

Right? Well, jumping in blindly without heeding what this year holds for you can be a mistake. So before you put together your plans, ask yourselves this—What are the most important 2023 recruiting trends in tech hiring that you should be prepared for? What are the predictions that will shape this year?

We went around and posed three important questions to industry experts that were on our minds. And what they had to say certainly gave us some food for thought!

Before we dive in, allow me to introduce you to our expert panel of six, who had so much to say from personal experience!

Meet the Expert Panel

Radoslav Stankov has more than 20 years of experience working in tech. He is currently Head of Engineering at Product Hunt. Enjoys blogging, conference speaking, and solving problems.

Mike “Batman” Cohen is the Founder of Wayne Technologies, a Sourcing-as-a-Service company providing recruitment data and candidate outreach services to enhance the talent acquisition journey.

Pamela Ilieva is the Director of International Recruitment at Shortlister, a platform that connects employers to wellness, benefits, and HR tech vendors.

Brian H. Hough is a Web2 and Web3 software engineer, AWS Community Builder, host of the Tech Stack Playbook™ YouTube channel/podcast, 5-time global hackathon winner, and tech content creator with 10k+ followers.

Steve O'Brien is Senior Vice President, Talent Acquisition at Syneos Health, leading a global team of top recruiters across 30+ countries in 24+ languages, with nearly 20 years of diverse recruitment experience.

Patricia (Sonja Sky) Gatlin is a New York Times featured activist, DEI Specialist, EdTechie, and Founder of Newbies in Tech. With 10+ years in Higher Education and 3+ in Tech, she now works part-time as a Diversity Lead recruiting STEM professionals to teach gifted students.

Overview of the upcoming tech industry landscape in 2024

Continued emphasis on remote work and flexibility: As we move into 2024, the tech industry is expected to continue embracing remote work and flexible schedules. This trend, accelerated by the COVID-19 pandemic, has proven to be more than a temporary shift. Companies are finding that remote work can lead to increased productivity, a broader talent pool, and better work-life balance for employees. As a result, recruiting strategies will likely focus on leveraging remote work capabilities to attract top talent globally.

Rising demand for AI and Machine Learning Skills: Artificial Intelligence (AI) and Machine Learning (ML) continue to be at the forefront of technological advancement. In 2024, these technologies are expected to become even more integrated into various business processes, driving demand for professionals skilled in AI and ML. Companies will likely prioritize candidates with expertise in these areas, and there may be an increased emphasis on upskilling existing employees to meet this demand.

Increased focus on cybersecurity: With the digital transformation of businesses, cybersecurity remains a critical concern. The tech industry in 2024 is anticipated to see a surge in the need for cybersecurity professionals. Companies will be on the lookout for talent capable of protecting against evolving cyber threats and ensuring data privacy.

Growth in cloud computing and edge computing: Cloud computing continues to grow, but there is also an increasing shift towards edge computing – processing data closer to where it is generated. This shift will likely create new job opportunities and skill requirements, influencing recruiting trends in the tech industry.

Sustainable technology and green computing: The global emphasis on sustainability is pushing the tech industry towards green computing and environmentally friendly technologies. In 2024, companies may seek professionals who can contribute to sustainable technology initiatives, adding a new dimension to tech recruiting.

Emphasis on soft skills: While technical skills remain paramount, soft skills like adaptability, communication, and problem-solving are becoming increasingly important. Companies are recognizing the value of these skills in fostering innovation and teamwork, especially in a remote or hybrid work environment.

Diversity, Equity, and Inclusion (DEI): There is an ongoing push towards more diverse and inclusive workplaces. In 2024, tech companies will likely continue to strengthen their DEI initiatives, affecting how they recruit and retain talent.

6 industry experts predict the 2023 recruiting trends

#1 We've seen many important moments in the tech industry this year...

Rado: In my opinion, a lot of those will carry over. I felt this was a preparation year for what was to come...

Mike: I wish I had the crystal ball for this, but I hope that when the market starts picking up again...

Pamela: Quiet quitting has been here way before 2022, and it is here to stay if organizations and companies...

Pamela Ilieva, Director of International Recruitment, Shortlister

Also, read: What Tech Companies Need To Know About Quiet Quitting

Brian: Yes, absolutely. In the 2022 Edelman Trust Barometer report...

Steve: Quiet quitting in the tech space will naturally face pressure as there is a redistribution of tech talent...

Patricia: Quiet quitting has been around for generations—people doing the bare minimum because they are no longer incentivized...

Patricia Gatlin, DEI Specialist and Curator, #blacklinkedin

#2 What is your pro tip for HR professionals/engineering managers...

Rado: Engineering managers should be able to do "more-with-less" in the coming year.

Radoslav Stankov, Head of Engineering, Product Hunt

Mike: Well first, (shameless plug), be in touch with me/Wayne Technologies as a stop-gap for when the time comes.

Mike “Batman” Cohen, Founder of Wayne Technologies

It's in the decrease and increase where companies find the hardest challenges...

Pamela: Remain calm – no need to “add fuel to the fire”!...

Brian: We have to build during the bear markets to thrive in the bull markets.

Companies can create internal hackathons to exercise creativity...

Also, read: Internal Hackathons - Drive Innovation And Increase Engagement In Tech Teams

Steve: HR professionals facing a hiring freeze will do well to “upgrade” processes, talent, and technology aggressively during downtime...

Steve O'Brien, Senior Vice President, Talent Acquisition at Syneos Health

Patricia: Talk to hiring managers in all your departments. Ask, what are the top 3-5 roles they are hiring for in the new year?...

Also, watch: 5 Recruiting Tips To Navigate The Hiring Freeze With Shalini Chandra, Senior TA, HackerEarth

#3 What top 3 skills would you like HR professionals/engineering managers to add to their repertoire in 2023 to deal with upcoming challenges?

6 industry experts predict the 2023 recruiting trends

Rado: Prioritization, team time, and environment management.

I think "prioritization" and "team time" management are obvious. But what do I mean by "environment management"?

A productive environment is one of the key ingredients for a productive team. Look at where your team wastes most time, which can be automated. For example, end-to-end writing tests take time because our tools are cumbersome and undocumented. So let's improve this.

Mike: Setting better metrics/KPIs, moving away from LinkedIn, and sharing more knowledge.

Metrics/KPIs: Become better at setting measurable KPIs and accountable metrics. They are not the same thing—it's like the Square and Rectangle. One fits into the other but they're not the same. Hold people accountable to metrics, not KPIs. Make sure your metrics are aligned with company goals and values, and that they push employees toward excellence, not mediocrity.
Freedom from LinkedIn: This is every year, and will probably continue to be. LinkedIn is a great database, but it is NOT the only way to find candidates, and oftentimes, not even the most effective/efficient. Explore other tools and methodologies!
Join the conversation: I'd love to see new names of people presenting at conferences and webinars. And also, see new authors on the popular TA content websites. Everyone has things they can share—be a part of the community, not just a user of. Join FB groups, write and post articles, and comment on other people's posts with more than 'Great article'. It's a great community, but it's only great because of the people who contribute to it—be one of those people.

Pamela: Resilience, leveraging data, and self-awareness.

Resilience: A “must-have” skill for the 21st century due to constant changes in the tech industry. Face and adapt to challenges. Overcome them and handle disappointments. Never give up. This will keep HR people alive in 2023.
Data skills: Get some data analyst skills. The capacity to transfer numbers into data can help you be a better HR professional, prepared to improve the employee experience and show your leadership team how HR is leveraging data to drive business results.
Self-awareness: Allows you to react better to upsetting situations and workplace challenges. It is a healthy skill to cultivate – especially as an HR professional.

Also, read: Diving Deep Into The World Of Data Science With Ashutosh Kumar

Brian: Agility, resourcefulness, and empathy.

Agility: Allows professionals to move with market conditions. Always be as prepared as possible for any situation to come. Be flexible based on what does or does not happen.
Resourcefulness: Allows professionals to do more with less. It also helps them focus on how to amplify, lift, and empower the current teams to be the best they can be.
Empathy: Allows professionals to take a more proactive approach to listening and understanding where all workers are coming from. Amid stressful situations, companies need empathetic team members and leaders alike who can meet each other wherever they are and be a support.

Steve: Negotiation, data management, and talent development.

Negotiation: Wage transparency laws will fundamentally change the compensation conversation. We must ensure we are still discussing compensation early in the process. And not just “assume” everyone’s on the same page because “the range is published”.
Data management and predictive analytics: Looking at your organization's talent needs as a casserole of indistinguishable components and demands will not be good enough. We must upgrade the accuracy and consistency of our data and the predictions we can make from it.

Also, read: The Role of Talent Intelligence in Optimizing Recruitment

Talent development: We’ve been exploring the interplay between TA and TM for years. Now is the time to integrate your internal and external talent marketplaces. To provide career experiences to people within your organization and not just those joining your organization.

Patricia: Technology, research, and relationship building.

Technology: Get better at understanding the technology that’s out there. To help you speed up the process, track candidate experience, but also eliminate bias. Metrics are becoming big in HR.
Research: Honestly, read more books. Many great thought leaders put out content about the “future of work”, understanding “Gen Z”, or “quiet quitting.” Dedicate work hours to understanding your ever-changing field.
Relationship Building: Especially in your immediate communities. Most people don’t know who you are or what exactly it is that you do. Build your personal brand and what you are doing at your company to impact those closest to you. Create a referral funnel to get a pipeline going. When people want a job you and your company ought to be top of mind. Also, tell the stories of the people that work there.

Hiring Strategies

7 Tech Recruiting Trends To Watch Out For In 2024

The last couple of years transformed how the world works and the tech industry is no exception. Remote work, a candidate-driven market, and automation are some of the tech recruiting trends born out of the pandemic.

While accepting the new reality and adapting to it is the first step, keeping up with continuously changing hiring trends in technology is the bigger challenge right now.

What does 2024 hold for recruiters across the globe? What hiring practices would work best in this post-pandemic world? How do you stay on top of the changes in this industry?

The answers to these questions will paint a clearer picture of how to set up for success while recruiting tech talent this year.

7 tech recruiting trends for 2024

6 Tech Recruiting Trends To Watch Out For In 2022

Recruiters, we’ve got you covered. Here are the tech recruiting trends that will change the way you build tech teams in 2024.

Trend #1—Leverage data-driven recruiting

Data-driven recruiting strategies are the answer to effective talent sourcing and a streamlined hiring process.

Talent acquisition leaders need to use real-time analytics like pipeline growth metrics, offer acceptance rates, quality and cost of new hires, and candidate feedback scores to reduce manual work, improve processes, and hire the best talent.

The key to capitalizing on talent market trends in 2024 is data. It enables you to analyze what’s working and what needs refinement, leaving room for experimentation.

Trend #2—Have impactful employer branding

98% of recruiters believe promoting company culture helps sourcing efforts as seen in our 2021 State Of Developer Recruitment report.

Having a strong employer brand that supports a clear Employer Value Proposition (EVP) is crucial to influencing a candidate’s decision to work with your company. Perks like upskilling opportunities, remote work, and flexible hours are top EVPs that attract qualified candidates.

A clear EVP builds a culture of balance, mental health awareness, and flexibility—strengthening your employer brand with candidate-first policies.

Trend #3—Focus on candidate-driven market

The pandemic drastically increased the skills gap, making tech recruitment more challenging. With the severe shortage of tech talent, candidates now hold more power and can afford to be selective.

Competitive pay is no longer enough. Use data to understand what candidates want—work-life balance, remote options, learning opportunities—and adapt accordingly.

Recruiters need to think creatively to attract and retain top talent.

Recommended read: What NOT To Do When Recruiting Fresh Talent

Trend #4—Have a diversity and inclusion oriented company culture

Diversity and inclusion have become central to modern recruitment. While urgent hiring can delay D&I efforts, long-term success depends on inclusive teams. Our survey shows that 25.6% of HR professionals believe a diverse leadership team helps build stronger pipelines and reduces bias.

McKinsey’s Diversity Wins report confirms this: top-quartile gender-diverse companies see 25% higher profitability, and ethnically diverse teams show 36% higher returns.

It's refreshing to see the importance of an inclusive culture increasing across all job-seeking communities, especially in tech. This reiterates that D&I is a must-have, not just a good-to-have.

—Swetha Harikrishnan, Sr. HR Director, HackerEarth

Trend #5—Embed automation and AI into your recruitment systems

With the rise of AI tools like ChatGPT, automation is being adopted across every business function—including recruiting.

Manual communication with large candidate pools is inefficient. In 2024, recruitment automation and AI-powered platforms will automate candidate nurturing and communication, providing a more personalized experience while saving time.

Trend #6—Conduct remote interviews

With 32.5% of companies planning to stay remote, remote interviewing is here to stay.

Remote interviews expand access to global talent, reduce overhead costs, and increase flexibility—making the hiring process more efficient for both recruiters and candidates.

Trend #7—Be proactive in candidate engagement

Delayed responses or lack of updates can frustrate candidates and impact your brand. Proactive communication and engagement with both active and passive candidates are key to successful recruiting.

As recruitment evolves, proactive candidate engagement will become central to attracting and retaining talent. In 2023 and beyond, companies must engage both active and passive candidates through innovative strategies and technologies like chatbots and AI-powered systems. Building pipelines and nurturing relationships will enhance employer branding and ensure long-term hiring success.

—Narayani Gurunathan, CEO, PlaceNet Consultants

Recruiting Tech Talent Just Got Easier With HackerEarth

Recruiting qualified tech talent is tough—but we’re here to help. HackerEarth for Enterprises offers an all-in-one suite that simplifies sourcing, assessing, and interviewing developers.

Our tech recruiting platform enables you to:

Tap into a 6 million-strong developer community
Host custom hackathons to engage talent and boost your employer brand
Create online assessments to evaluate 80+ tech skills
Use dev-friendly IDEs and proctoring for reliable evaluations
Benchmark candidates against a global community
Conduct live coding interviews with FaceCode, our collaborative coding interview tool
Guide upskilling journeys via our Learning and Development platform
Integrate seamlessly with all leading ATS systems
Access 24/7 support with a 95% satisfaction score

Recommended read: The A-Zs Of Tech Recruiting - A Guide

Staying ahead of tech recruiting trends, improving hiring processes, and adapting to change is the way forward in 2024. Take note of the tips in this article and use them to build a future-ready hiring strategy.

Ready to streamline your tech recruiting? Try HackerEarth for Enterprises today.

Developer Insights

Code In Progress - The Life And Times Of Developers In 2021

Developers. Are they as mysterious as everyone makes them out to be? Is coding the only thing they do all day? Good coders work around the clock, right?

While developers are some of the most coveted talent out there, they also have the most myths being circulated. Most of us forget that developers too are just like us. And no, they do not code all day long.

We wanted to bust a lot of these myths and shed light on how the programming world looks through a developer’s lens in 2021—especially in the wake of a global pandemic. This year’s edition of the annual HackerEarth Developer Survey is packed with developers’ wants and needs when choosing jobs, major gripes with the WFH scenario, and the latest market trends to watch out for, among others.

Our 2021 report is bigger and better, with responses from 25,431 developers across 171 countries. Let’s find out what makes a developer tick, shall we?

Developer Survey

“Good coders work around the clock.” No, they don’t.

Busting the myth that developers spend the better part of their day coding, 52% of student developers said that they prefer to code for a maximum of 3 hours per day.

When not coding, devs swear by their walks as a way to unwind. When we asked devs the same question last year, they said they liked to indulge in indoor games like foosball. In 2021, going for walks has become the most popular method of de-stressing. We’re chalking it up to working from home and not having a chance to stretch their legs.

Staying ahead of the skills game

Following the same trend as last year, students (39%) and working professionals (44%) voted for Go as one of the most popular programming languages that they want to learn. The other programming languages that devs are interested in learning are Rust, Kotlin, and Erlang.

Programming languages that students are most skilled at are HTML/CSS, C++, and Python. Senior developers are more comfortable working with HTML/CSS, SQL, and Java.

How happy are developers

Employees from middle market organizations had the highest 'happiness index' of 7.2. Experienced developers who work at enterprises are marginally less happy in comparison to people who work at smaller companies.

However, happiness is not a binding factor for where developers work. Despite scoring the least on the happiness scale, working professionals would still like to work at enterprise companies and growth-stage startups.

What works when looking for work

Student devs (63%), who are just starting in the tech world, said a good career growth curve is a must-have. Working professionals can be wooed by offers of a good career path (69%) and compensation (68%).

One trend that has changed since last year is that at least 50% of students and working professionals alike care a lot more about ESOPs and positive Glassdoor reviews now than they did in 2020.

To know more about what developers want, download your copy of the report now!

We went a step further and organized an event with our CEO, Sachin Gupta, Radoslav Stankov, Head of Engineering at Product Hunt, and Steve O’Brien, President of Talent Solutions at Job.com to further dissect the findings of our survey.

Tips straight from the horse’s mouth

Steve highlighted how the information collated from the developer survey affects the recruiting community and how they can leverage this data to hire better and faster.

The insight where developer happiness is correlated to work hours didn’t find a significant difference between the cohorts. Devs working for less than 40 hours seemed marginally happier than those that clocked in more than 60 hours a week.

“This is an interesting data point, which shows that devs are passionate about what they do. You can increase their workload by 50% and still not affect their happiness. From a work perspective, as a recruiter, you have to get your hiring manager to understand that while devs never say no to more work, HMs shouldn’t overload the devs. Devs are difficult to source and burnout only leads to killing your talent pool, which is something that you do not want,” says Steve.

Roughly 45% of both student and professional developers learned how to code in college was another insight that was open to interpretation.

“Let’s look at it differently. Less than half of the surveyed developers learned how to code in college. There’s a major segment of the market today that is not necessarily following the ‘college degree to getting a job’ path. Developers are beginning to look at their skillsets differently and using various platforms to upskill themselves. Development is not about pedigree, it’s more about the potential to demonstrate skills. This is an interesting shift in the way we approach testing and evaluating devs in 2021.”

Rado contextualized the data from the survey to see what it means for the developer community and what trends to watch out for in 2021.

Node.js and AngularJS are the most popular frameworks among students and professionals.

“I was surprised by how many young students wanted to learn AngularJS, given that it’s more of an enterprise framework. Another thing that stood out to me was that the younger generation wants to learn technologies that are not necessarily cool like ExtJS (35%). This is good because people are picking technologies that they enjoy working with instead of just going along with what everyone else is doing. This also builds a more diverse technology pool.” — Rado

22% of devs say ‘Zoom Fatigue’ is real and directly affects productivity.

“Especially for younger people who still haven’t figured out a routine to develop their skills, there is something I’d like you to try out. Start using noise-canceling headphones. They help keep distractions to a minimum. I find clutter-free working spaces to be an interesting concept as well.”

The last year and a half have been a doozy for developers everywhere, with a lot of things changing, and some things staying the same. With our developer survey, we wanted to shine the spotlight on skill-based hiring and market trends in 2021—plus highlight the fact that developers too have their gripes and happy hours.

Uncover many more developer trends for 2021 with Steve and Rado below:

View all

Hiring Strategies

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

In today's competitive talent market, attracting and retaining top performers is crucial for any organization's success. However, traditional hiring methods like relying solely on resumes and interviews may not always provide a comprehensive picture of a candidate's skills and potential. This is where pre-employment assessments come into play.

What is Pre-Employement Assessment?

Pre-employment assessments are standardized tests and evaluations administered to candidates before they are hired. These assessments can help you objectively measure a candidate's knowledge, skills, abilities, and personality traits, allowing you to make data-driven hiring decisions.

By exploring and evaluating the best pre-employment assessment tools and tests available, you can:

Improve the accuracy and efficiency of your hiring process.
Identify top talent with the right skills and cultural fit.
Reduce the risk of bad hires.
Enhance the candidate experience by providing a clear and objective evaluation process.

This guide will provide you with valuable insights into the different types of pre-employment assessments available and highlight some of the best tools, to help you optimize your hiring process for 2024.

Why pre-employment assessments are key in hiring

While resumes and interviews offer valuable insights, they can be subjective and susceptible to bias. Pre-employment assessments provide a standardized and objective way to evaluate candidates, offering several key benefits:

Improved decision-making:

By measuring specific skills and knowledge, assessments help you identify candidates who possess the qualifications necessary for the job.
Reduced bias:

Standardized assessments mitigate the risks of unconscious bias that can creep into traditional interview processes.
Increased efficiency:

Assessments can streamline the initial screening process, allowing you to focus on the most promising candidates.
Enhanced candidate experience:

When used effectively, assessments can provide candidates with a clear understanding of the required skills and a fair chance to showcase their abilities.

Types of pre-employment assessments

There are various types of pre-employment assessments available, each catering to different needs and objectives. Here's an overview of some common types:

1. Skill Assessments:

Technical Skills: These assessments evaluate specific technical skills and knowledge relevant to the job role, such as programming languages, software proficiency, or industry-specific expertise. HackerEarth offers a wide range of validated technical skill assessments covering various programming languages, frameworks, and technologies.
Soft Skills: These employment assessments measure non-technical skills like communication, problem-solving, teamwork, and critical thinking, crucial for success in any role.

2. Personality Assessments:

These employment assessments can provide insights into a candidate's personality traits, work style, and cultural fit within your organization.

3. Cognitive Ability Tests:

These tests measure a candidate's general mental abilities, such as reasoning, problem-solving, and learning potential.

4. Integrity Assessments:

These employment assessments aim to identify potential risks associated with a candidate's honesty, work ethic, and compliance with company policies.

By understanding the different types of assessments and their applications, you can choose the ones that best align with your specific hiring needs and ensure you hire the most qualified and suitable candidates for your organization.

Leading employment assessment tools and tests in 2024

Choosing the right pre-employment assessment tool depends on your specific needs and budget. Here's a curated list of some of the top pre-employment assessment tools and tests available in 2024, with brief overviews:

HackerEarth:

A comprehensive platform offering a wide range of validated skill assessments in various programming languages, frameworks, and technologies. It also allows for the creation of custom assessments and integrates seamlessly with various recruitment platforms.
SHL:

Provides a broad selection of assessments, including skill tests, personality assessments, and cognitive ability tests. They offer customizable solutions and cater to various industries.
Pymetrics:

Utilizes gamified assessments to evaluate cognitive skills, personality traits, and cultural fit. They offer a data-driven approach and emphasize candidate experience.
Wonderlic:

Offers a variety of assessments, including the Wonderlic Personnel Test, which measures general cognitive ability. They also provide aptitude and personality assessments.
Harver:

An assessment platform focusing on candidate experience with video interviews, gamified assessments, and skills tests. They offer pre-built assessments and customization options.

Remember: This list is not exhaustive, and further research is crucial to identify the tool that aligns best with your specific needs and budget. Consider factors like the types of assessments offered, pricing models, integrations with your existing HR systems, and user experience when making your decision.

Choosing the right pre-employment assessment tool

Instead of full individual tool reviews, consider focusing on 2–3 key platforms. For each platform, explore:

Target audience: Who are their assessments best suited for (e.g., technical roles, specific industries)?
Types of assessments offered: Briefly list the available assessment categories (e.g., technical skills, soft skills, personality).
Key features: Highlight unique functionalities like gamification, custom assessment creation, or seamless integrations.
Effectiveness: Briefly mention the platform's approach to assessment validation and reliability.
User experience: Consider including user reviews or ratings where available.

Comparative analysis of assessment options

Instead of a comprehensive comparison, consider focusing on specific use cases:

Technical skills assessment:

Compare HackerEarth and Wonderlic based on their technical skill assessment options, focusing on the variety of languages/technologies covered and assessment formats.
Soft skills and personality assessment:

Compare SHL and Pymetrics based on their approaches to evaluating soft skills and personality traits, highlighting any unique features like gamification or data-driven insights.
Candidate experience:

Compare Harver and Wonderlic based on their focus on candidate experience, mentioning features like video interviews or gamified assessments.

Additional tips:

Encourage readers to visit the platforms' official websites for detailed features and pricing information.
Include links to reputable third-party review sites where users share their experiences with various tools.

Best practices for using pre-employment assessment tools

Integrating pre-employment assessments effectively requires careful planning and execution. Here are some best practices to follow:

Define your assessment goals:

Clearly identify what you aim to achieve with assessments. Are you targeting specific skills, personality traits, or cultural fit?
Choose the right assessments:

Select tools that align with your defined goals and the specific requirements of the open position.
Set clear expectations:

Communicate the purpose and format of the assessments to candidates in advance, ensuring transparency and building trust.
Integrate seamlessly:

Ensure your chosen assessment tool integrates smoothly with your existing HR systems and recruitment workflow.
Train your team:

Equip your hiring managers and HR team with the knowledge and skills to interpret assessment results effectively.

Interpreting assessment results accurately

Assessment results offer valuable data points, but interpreting them accurately is crucial for making informed hiring decisions. Here are some key considerations:

Use results as one data point:

Consider assessment results alongside other information, such as resumes, interviews, and references, for a holistic view of the candidate.
Understand score limitations:

Don't solely rely on raw scores. Understand the assessment's validity and reliability and the potential for cultural bias or individual test anxiety.
Look for patterns and trends:

Analyze results across different assessments and identify consistent patterns that align with your desired candidate profile.
Focus on potential, not guarantees:

Assessments indicate potential, not guarantees of success. Use them alongside other evaluation methods to make well-rounded hiring decisions.

Choosing the right pre-employment assessment tools

Selecting the most suitable pre-employment assessment tool requires careful consideration of your organization's specific needs. Here are some key factors to guide your decision:

Industry and role requirements:

Different industries and roles demand varying skill sets and qualities. Choose assessments that target the specific skills and knowledge relevant to your open positions.
Company culture and values:

Align your assessments with your company culture and values. For example, if collaboration is crucial, look for assessments that evaluate teamwork and communication skills.
Candidate experience:

Prioritize tools that provide a positive and smooth experience for candidates. This can enhance your employer brand and attract top talent.

Budget and accessibility considerations

Budget and accessibility are essential factors when choosing pre-employment assessments:

Budget:

Assessment tools come with varying pricing models (subscriptions, pay-per-use, etc.). Choose a tool that aligns with your budget and offers the functionalities you need.
Accessibility:

Ensure the chosen assessment is accessible to all candidates, considering factors like language options, disability accommodations, and internet access requirements.

Additional Tips:

Free trials and demos: Utilize free trials or demos offered by assessment platforms to experience their functionalities firsthand.
Consult with HR professionals: Seek guidance from HR professionals or recruitment specialists with expertise in pre-employment assessments.
Read user reviews and comparisons: Gain insights from other employers who use various assessment tools.

By carefully considering these factors, you can select the pre-employment assessment tool that best aligns with your organizational needs, budget, and commitment to an inclusive hiring process.

Remember, pre-employment assessments are valuable tools, but they should not be the sole factor in your hiring decisions. Use them alongside other evaluation methods and prioritize building a fair and inclusive hiring process that attracts and retains top talent.

Future trends in pre-employment assessments

The pre-employment assessment landscape is constantly evolving, with innovative technologies and practices emerging. Here are some potential future trends to watch:

Artificial intelligence (AI):

AI-powered assessments can analyze candidate responses, written work, and even resumes, using natural language processing to extract relevant insights and identify potential candidates.
Adaptive testing:

These assessments adjust the difficulty level of questions based on the candidate's performance, providing a more efficient and personalized evaluation.
Micro-assessments:

Short, focused assessments delivered through mobile devices can assess specific skills or knowledge on-the-go, streamlining the screening process.
Gamification:

Engaging and interactive game-based elements can make the assessment experience more engaging and assess skills in a realistic and dynamic way.

Conclusion

Pre-employment assessments, when used thoughtfully and ethically, can be a powerful tool to optimize your hiring process, identify top talent, and build a successful workforce for your organization. By understanding the different types of assessments available, exploring top-rated tools like HackerEarth, and staying informed about emerging trends, you can make informed decisions that enhance your ability to attract, evaluate, and hire the best candidates for the future.

Hiring Strategies

Tech Layoffs: What To Expect In 2024

Layoffs in the IT industry are becoming more widespread as companies fight to remain competitive in a fast-changing market; many turn to layoffs as a cost-cutting measure. Last year, 1,000 companies including big tech giants and startups, laid off over two lakhs of employees. But first, what are layoffs in the tech business, and how do they impact the industry?

Tech layoffs are the termination of employment for some employees by a technology company. It might happen for various reasons, including financial challenges, market conditions, firm reorganization, or the after-effects of a pandemic. While layoffs are not unique to the IT industry, they are becoming more common as companies look for methods to cut costs while remaining competitive.

The consequences of layoffs in technology may be catastrophic for employees who lose their jobs and the firms forced to make these difficult decisions. Layoffs can result in the loss of skill and expertise and a drop in employee morale and productivity. However, they may be required for businesses to stay afloat in a fast-changing market.

This article will examine the reasons for layoffs in the technology industry, their influence on the industry, and what may be done to reduce their negative impacts. We will also look at the various methods for tracking tech layoffs.

What are tech layoffs?

The term "tech layoff" describes the termination of employees by an organization in the technology industry. A company might do this as part of a restructuring during hard economic times.

In recent times, the tech industry has witnessed a wave of significant layoffs, affecting some of the world’s leading technology companies, including Amazon, Microsoft, Meta (formerly Facebook), Apple, Cisco, SAP, and Sony. These layoffs are a reflection of the broader economic challenges and market adjustments facing the sector, including factors like slowing revenue growth, global economic uncertainties, and the need to streamline operations for efficiency.

Each of these tech giants has announced job cuts for various reasons, though common themes include restructuring efforts to stay competitive and agile, responding to over-hiring during the pandemic when demand for tech services surged, and preparing for a potentially tough economic climate ahead. Despite their dominant positions in the market, these companies are not immune to the economic cycles and technological shifts that influence operational and strategic decisions, including workforce adjustments.

This trend of layoffs in the tech industry underscores the volatile nature of the tech sector, which is often at the mercy of rapid changes in technology, consumer preferences, and the global economy. It also highlights the importance of adaptability and resilience for companies and employees alike in navigating the uncertainties of the tech landscape.

Causes for layoffs in the tech industry

Why are tech employees suffering so much?

Yes, the market is always uncertain, but why resort to tech layoffs?

Various factors cause tech layoffs, including company strategy changes, market shifts, or financial difficulties. Companies may lay off employees if they need help to generate revenue, shift their focus to new products or services, or automate certain jobs.

In addition, some common reasons could be:

Financial struggles

Currently, the state of the global market is uncertain due to economic recession, ongoing war, and other related phenomena. If a company is experiencing financial difficulties, only sticking to pay cuts may not be helpful—it may need to reduce its workforce to cut costs.

Also, read: 6 Steps To Create A Detailed Recruiting Budget (Template Included)

Changes in demand

The tech industry is constantly evolving, and companies would have to adjust their workforce to meet changing market conditions. For instance, companies are adopting remote work culture, which surely affects on-premises activity, and companies could do away with some number of tech employees at the backend.

Restructuring

Companies may also lay off employees as part of a greater restructuring effort, such as spinning off a division or consolidating operations.

Automation

With the advancement in technology and automation, some jobs previously done by human labor may be replaced by machines, resulting in layoffs.

Mergers and acquisitions

When two companies merge, there is often overlap in their operations, leading to layoffs as the new company looks to streamline its workforce.

But it's worth noting that layoffs are not exclusive to the tech industry and can happen in any industry due to uncertainty in the market.

Will layoffs increase in 2024?

It is challenging to estimate the rise or fall of layoffs. The overall state of the economy, the health of certain industries, and the performance of individual companies will play a role in deciding the degree of layoffs in any given year.

But it is also seen that, in the first 15 days of this year, 91 organizations laid off over 24,000 tech workers, and over 1,000 corporations cut down more than 150,000 workers in 2022, according to an Economic Times article.

The COVID-19 pandemic caused a huge economic slowdown and forced several businesses to downsize their employees. However, some businesses rehired or expanded their personnel when the world began to recover.

So, given the current level of economic uncertainty, predicting how the situation will unfold is difficult.

Also, read: 4 Images That Show What Developers Think Of Layoffs In Tech

What types of companies are prone to tech layoffs?

Tech layoffs can occur in organizations of all sizes and various areas.

Following are some examples of companies that have experienced tech layoffs in the past:

Large tech firms

Companies such as IBM, Microsoft, Twitter, Better.com, Alibaba, and HP have all experienced layoffs in recent years as part of restructuring initiatives or cost-cutting measures.

Market scenarios are still being determined after Elon Musk's decision to lay off employees. Along with tech giants, some smaller companies and startups have also been affected by layoffs.

Startups

Because they frequently work with limited resources, startups may be forced to lay off staff if they cannot get further funding or need to pivot due to market downfall.

Small and medium-sized businesses

Small and medium-sized businesses face layoffs due to high competition or if the products/services they offer are no longer in demand.

Companies in certain industries

Some sectors of the technological industry, such as the semiconductor industry or automotive industry, may be more prone to layoffs than others.

Companies that lean on government funding

Companies that rely significantly on government contracts may face layoffs if the government cuts technology spending or contracts are not renewed.

How to track tech layoffs?

You can’t stop tech company layoffs, but you should be keeping track of them. We, HR professionals and recruiters, can also lend a helping hand in these tough times by circulating “layoff lists” across social media sites like LinkedIn and Twitter to help people land jobs quicker. Firefish Software put together a master list of sources to find fresh talent during the layoff period.

Because not all layoffs are publicly disclosed, tracking tech industry layoffs can be challenging, and some may go undetected. There are several ways to keep track of tech industry layoffs:

Use tech layoffs tracker

Layoff trackers like thelayoff.com and layoffs.fyi provide up-to-date information on layoffs.

In addition, they aid in identifying trends in layoffs within the tech industry. It can reveal which industries are seeing the most layoffs and which companies are the most affected.

Companies can use layoff trackers as an early warning system and compare their performance to that of other companies in their field.

News articles

Because many news sites cover tech layoffs as they happen, keeping a watch on technology sector stories can provide insight into which organizations are laying off employees and how many individuals have been affected.

Social media

Organizations and employees frequently publish information about layoffs in tech on social media platforms; thus, monitoring companies' social media accounts or following key hashtags can provide real-time updates regarding layoffs.

Online forums and communities

There are online forums and communities dedicated to discussing tech industry news, and they can be an excellent source of layoff information.

Government reports

Government agencies such as the Bureau of Labor Statistics (BLS) publish data on layoffs and unemployment, which can provide a more comprehensive picture of the technology industry's status.

How do companies reduce tech layoffs?

Layoffs in tech are hard – for the employee who is losing their job, the recruiter or HR professional who is tasked with informing them, and the company itself. So, how can we aim to avoid layoffs? Here are some ways to minimize resorting to letting people go:

Salary reductions

Instead of laying off employees, businesses can lower the salaries or wages of all employees. It can be accomplished by instituting compensation cuts or salary freezes.

Implementing a hiring freeze

Businesses can halt employing new personnel to cut costs. It can be a short-term solution until the company's financial situation improves.

Also, read: What Recruiters Can Focus On During A Tech Hiring Freeze

Non-essential expense reduction

Businesses might search for ways to cut or remove non-essential expenses such as travel, training, and office expenses.

Reducing working hours

Companies can reduce employee working hours to save money, such as implementing a four-day workweek or a shorter workday.

These options may not always be viable and may have their problems, but before laying off, a company owes it to its people to consider every other alternative, and formulate the best solution.

Tech layoffs to bleed into this year

While we do not know whether this trend will continue or subside during 2023, we do know one thing. We have to be prepared for a wave of layoffs that is still yet to hit. As of last month, Layoffs.fyi had already tracked 170+ companies conducting 55,970 layoffs in 2023.

So recruiters, let’s join arms, distribute those layoff lists like there’s no tomorrow, and help all those in need of a job! :)

Hiring Tools

What is Headhunting In Recruitment?: Types & How Does It Work?

In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.

What is Headhunting in recruitment?

In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.

How do headhunting and traditional recruitment differ from each other?

Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.

In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.

AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams

All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.

Types of headhunting in recruitment

Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.

Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.

How does headhunting work?

Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.

Identifying the role

Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.

Defining the job

Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.

Candidate identification and sourcing

Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.

Approaching candidates

Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.

Assessment and Evaluation

In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.

Interviews and negotiations

Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.

Finalizing the hire

Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.

The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.

Common challenges in headhunting

Despite its advantages, headhunting also presents certain challenges:

Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.

Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.

Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.

Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.

Advantages of Headhunting

Headhunting offers several advantages over traditional recruitment methods:

Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.

Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.

Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.

Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.

Conclusion

Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.

View all

Developer Insights

Architecture of smart cities

ICT Architecture of a Smart City

Sensing Layer

Communication Layer

Data Layer

Service Layer

Logging millions of requests every day and what it takes

Initial Thoughts

Architecture

Web Servers

Transporter Cluster

Kafka Cluster

Pipeline Manager Server

What's next

Data Visualization packages in Python - Pandas

Components and implementations of Natural Language Processing

What makes up NLP?

Entity Extraction

Syntactic Analysis

Semantic Analysis

Sentiment Analysis

Pragmatic Analysis

A Few Applications of NLP

5 medical algorithms that are transforming the healthcare industry

Sampling

Fourier Transform

Probabilistic Data-Matching

Proportional Integral Derivative (PID)

Predictive Algorithm

What is a fitness tracker and how does it work?

Mini labs juggling complex data—fitness trackers

Research might scare you into buying one

Unravelling the mystery of these tiny marvels

Accelerometers

GPS

Galvanic Skin Response Sensor

Optical Heart Rate Monitor (OHRM)

Bioimpedance Sensors

Temperature Sensors

UV and Ambient Light Sensors

Finding the right fitness tracker

Technical Screening Guide: All You Need To Know

Explore HackerEarth’s top products for Hiring & Innovation

Meet our Authors

Forecasting Tech Hiring Trends For 2023 With 6 Experts

Meet the Expert Panel

Overview of the upcoming tech industry landscape in 2024

6 industry experts predict the 2023 recruiting trends

#1 We've seen many important moments in the tech industry this year...

#2 What is your pro tip for HR professionals/engineering managers...

#3 What top 3 skills would you like HR professionals/engineering managers to add to their repertoire in 2023 to deal with upcoming challenges?

7 Tech Recruiting Trends To Watch Out For In 2024

7 tech recruiting trends for 2024

Trend #1—Leverage data-driven recruiting

Trend #2—Have impactful employer branding

Trend #3—Focus on candidate-driven market

Trend #4—Have a diversity and inclusion oriented company culture

Trend #5—Embed automation and AI into your recruitment systems

Trend #6—Conduct remote interviews

Trend #7—Be proactive in candidate engagement

Recruiting Tech Talent Just Got Easier With HackerEarth

Code In Progress - The Life And Times Of Developers In 2021

“Good coders work around the clock.” No, they don’t.

Staying ahead of the skills game

How happy are developers

What works when looking for work

Tips straight from the horse’s mouth

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

What is Pre-Employement Assessment?

Why pre-employment assessments are key in hiring

Improved decision-making:

Reduced bias:

Increased efficiency:

Enhanced candidate experience:

Types of pre-employment assessments

1. Skill Assessments:

2. Personality Assessments:

3. Cognitive Ability Tests:

4. Integrity Assessments:

What is Headhunting In Recruitment?: Types & How Does It Work?