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List of Indian IT Companies


Top IT Companies In India

The following is the list of top IT companies in India that offer many users secure, reliable, user-friendly, and robust IT services.

Tata Consultancy Services (TCS)

TCS is one of the top 10 IT companies in India, a Tata Group company, and is well known across the globe. The company offers various IT services to many of its clients. TCS was started in the year 1968 by Tata Sons. Currently, TCS is headed by Mr. Rajesh Gopinathan, but soon Mr. K. Krithivasan will take over the position of MD and CEO of TCS by Jun ’23.

Tata Consultancy Services (TCS) is an Indian Multinational company headquartered in Mumbai. The company operates worldwide, covering 46 countries with 150 locations under the Tata Group. Many leading news reports informed that TCS is recognized for its employee-friendly workplace, developing the best talents across the company through multiple skill development programs and initiatives. It is an equal-opportunity employer and offers the best salary packages.

TCS (Tata Consultancy Services) currently holds around 5,56,000 employees in 150 locations. It also includes more than 2 Lakh women employees contributing to its growth. Moreover, the company’s net margin by the March ’23 quarter stood at 19.3%, and the revenue increased to Rs. 59,162 crores. The total market cap of TCS is around Rs. 11.52 trillion.

A skilled Software Engineer in TCS company earns a decent salary of Rs. 3.6 to 13.5 lakhs p.a. with an annual average salary of Rs. 6.3 lakhs, according to AmbitionBox.

Infosys

Another one among the top list of IT companies in India is Infosys. It was founded in 1981 in Pune and is HQ in Bengaluru. Infosys emerged as a global leader in providing digital and consulting services, business outsourcing, and IT services for next-generation. Mr. N.R. Narayana Murty, Nandan Nilekani, and others started it. Moreover, Infosys is an NYSE-listed IT company with over 3 lakh employees. It generated a revenue of USD 18.21 billion in the fiscal year 2023 with a market cap of USD 72.35 billion.

It is the first IT company from India which is listed on NASDAQ. Infosys has spread its wings to more than 190 companies across the globe. The company primarily provides various IT services in cloud computing, data analytics, IoT, testing, app development, cyber security, and more.

A software engineer at Infosys in India earns between Rs. 3.5 to 20 lakhs per year with an average annual salary of Rs. 7.7 lakhs, according to Ambitionbox.

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Wipro Limited

Wipro Limited is among India’s top 10 IT companies, leading in offering in-demand digital transformation services. Also, it looks after various customer needs across the globe. However, Wipro is the short form of Wester India Products, established in 1945 by MD Premji. Initially, it was established as a manufacturing company of vegetable and refined oils, gradually entering into diversified businesses. Now, it is one of the best IT companies in India, with a market cap of more than Rs. 2 lakh Cr and possesses more than 2 lakh employees.

Wipro offers multiple IT services in Data Analytics, Cyber Security, Digital Operations, AI, Consulting, and more.

Further, the company’s net worth stood at 24.21 Billion USD in 2023. It offers equal employment opportunities across different sectors, including IT. A software engineer at Wipro earns a salary of Rs. 3.6 to 12 lakhs per year with an average annual salary of Rs. 7 lakhs p.a.

HCL Technologies

Hindustan Computers Limited, or HCL Technologies, is among India’s top 10 IT companies. It was established in 1976 by Shiv Nadar and is headquartered in Noida. HCL Tech has 180 global locations and nearly 1,70,000 employees working here.

The market cap of HCL Tech is around Rs. 2.85 trillion. By the end of March ’23, the company has crossed the revenue of Rs. 1 lakh crores by growing its industry-leading services by 16%. Moreover, HCL Tech helps in business transformation with a wide range of services like digital workplace, networking, hybrid cloud services, cyber security, etc. Apart from IT services, it also offers BPO, infrastructure, etc. It contributes more towards the country’s economic growth by providing an industrial and startup ecosystem for new IT services.

The salary of an SDE (Software Engineer) at HCL Tech ranges between Rs. 4.8 lakhs to Rs. 17 Lakhs p.a. The average annual salary of an SDE at this company is around Rs. 9 lakhs per annum. However, the pay may vary with the position and experience level.

Tech Mahindra

It is another Indian MNC that offers IT consulting and services. Tech Mahindra provides a wide range of services through its diverse businesses like farm equipment manufacturing, financial services, utility vehicles, IT services, etc. Further, it offers customer-centric, innovative digital services. It was founded in 1986 by Anand Mahindra, with headquarters in Pune as a part of the Mahindra Group.

The total revenue of Tech Mahindra is more than Rs. 38,600 Crores. Moreover, Tech Mahindra offers a wide range of IT services such as cloud services, IT consulting, AI, data analytics, cyber security, next-gen managed services, 4G/5G services, networking services, BPS, etc.

The salary of a Software engineer at Tech Mahindra ranges between Rs. 4.2 to 9.5 lakhs per year with an average pay of Rs. 5.4 lakhs p.a.

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MindTree Ltd

Among the best IT companies in India, Mindtree Ltd. comes under the top 10 IT companies. It is also an IT services and consulting company with headquarters in Bangalore. It was established in 1999 by a group of IT professionals. Further, it was acquired by L&T in 2019, and later, by merging with L&T Infotech, it became LTIMindTree in 2022.

LTIMindtree has nearly 40,000 employees in India. After merging with LTI, it became the 5th largest IT company with USD 5.25 Billion in revenue in 2022. Currently, it is headed by Debashish Chatterjee (CEO).

LTIMindTree offers various IT services, such as digital transformation, IT outsourcing, cloud services, consultancy, analytics, AI/ML, IoT, and many others. A skilled Software Developer at LTIMindTree earns an annual salary of Rs. 4 lakhs to 11 lakhs p.a. with an average pay of Rs. 6.9 lakhs p.a.

MPhasis

MPhasis is one of the top IT services and consulting companies which is an Indian Multinational founded in 1998. Its current CEO is Nitin Rakesh since 2017. It is headquartered in Bengaluru, with nearly 30,000 employees working there. Further, Mphasis holds a total of 65 branches across the globe. This company also offers various IT and consulting services, including cloud, blockchain, cyber security, automation, DevOps, BPS, and more.

The company MPhasis generated a total revenue of Rs. 9,700 crores in 2021. It’s a mid-cap company in the IT sector with Rs. 42,149 Cr. The salary of a Software Engineer at Mphasis is between Rs. 4 to 12 lakhs per year, with an average annual salary of Rs 6.3 lakhs.

Hexaware Technologies

Hexaware Technologies is one of the Top IT companies HQ in Navi Mumbai and was founded in 1990. The company is into a wide range of IT and BPS companies, where 30,000 people work to scale digital transformation quickly.

It is creating great value for its customers with innovative technology services. It helps its employee stay productive and engaged with happiness. The company has crossed its first 1 billion US$ in revenue in 2022.

The various IT services it offers customers include IoT, cloud services, autonomous testing, data visualization, app security, and more. Moreover, the average annual salary of a Software Engineer at Hexaware Tech is around Rs. 5 LPA, ranging between Rs. 4 to 10 lakhs p.a.

Quess Corp

Quess Corp was founded in 2007 as a leading business services provider which offers a wide range of tech-enabled services like staffing and managed outsourcing. It provides services for multiple processes such as sales and marketing, telecom operations, security management, IT services, HR operations, etc. The company is HQ in Bengaluru, and CBSS (Conneqt Business Solutions) is its subsidiary.

It operates with more than 25 branches globally, with over 3 lakh employees. The revenue of Quess Corp is around Rs. 11K crores. It offers multiple roles and responsibilities to individuals with relevant skills and qualifications.

eSparkBiz

eSparkBiz is one of the top IT companies in India that offers various IT and digital transformation services. It provides services to multiple companies and unique software and IT outsourcing services.

It was founded in 2010 and emerged as a web and app development company HQ in Ahmedabad, Gujarat. It is a popular web designing company with a great team of 300+ employees. Compared to other giant IT companies, the salaries paid by eSparkBiz are low. The average annual salary of a professional at eSparkBiz ranges between Rs. 3 to 4 lakhs p.a.

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Bottom Line

We have discussed the top 10 IT companies in India that are into a wide range of IT and consulting services. Many other best IT companies are operating in India. These include IBM, Accenture, Cognizant, Genpact, etc. These companies also provide a variety of IT and Non-IT services to their clients across the globe. We can see multifold growth in the IT industry in the last decade. Also, with the growing number of internet users, IT services may increase. However, the top IT companies in India dominate the global IT sector.

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Python Serialization | A Complete Guide on Python Serialization


Python Serialization – Table of Content

Serialization in Python

Serialization in python is a process to serialize data in a species that is user-friendly, human-readable, and easily inspected. There are two very common python serialization libraries that serialize data objects in python. They are ‘HDF5’ and ‘Pickle’ which take dictionaries as well as Tensorflow models for storage purposes and transmission.

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Why Python Serialization?

The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. It can also be used to transmit data on a server network and the user can access it on any system later on.

The process of serialization is also very helpful for projects related to data science. For instance, the process of dataset preprocessing can be very time-consuming, hence preprocessing is done just once that too before saving the data on the disk. It is preferred that the user performs preprocessing each time he uses it. It also eliminates memory limitation problems for big data too which is heavy for loading in the memory as a single piece. So when the data is split into smaller chunks, the user is able to load every single chunk for preprocessing, and he can then save the outputs to the disk, removing all the data chunks from the memory.

Python Serialization: Text Based

The process of textual serialization means serializing the data in some specific format that is easy to understand, human-readable as well as easily inspected. Formats which are text-based are mainly language agnostic and they can be formed with the help of any language related to programming.

JSON is a standard format that is used to exchange data between servers and web clients. JSON is known to serialize the objects in a plain text file format and allow for easy visual identification to the user. JSON stores the objects in the form of key-value pairs, just like a dictionary in Python. JSON is a built-in library in python which makes it a breeze for the user to work with JSON. 

It is very easy to perform JSON serialization just like creating a JSON file and dumping the object. This is done with the help of the dump() method. This method has two arguments which are:  

  • The object user is serializing
  • File which will store the serialized object.

Python JSON has two main functions which it works with:

  • dump(): This function helps to convert a Python object into JSON format
  • Loads(): This function helps to convert the JSON string back into a Python object.

The table below will show the conversion of the python data type into a JSON type:

dict-object
List, tuple- array
str- String
True- true
Int, float- Number
False- false
None- null

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YAML

YAML is not a Markup Language but it is actually a parent set of JSON made in a way to be more comprehensible to the user. The most important and distinguishing feature of YAML is the capacity to create references for other objects in the same file. Another most important advantage is that it is possible to write comments in python. This feature has proved very useful to work with the configuration files also.

Python Serialization: Binary Formats

It is not possible for binary formats in serialization to be human-readable; however they are faster in general and also require much lesser space than text-based counterparts. Let us see some very popular binary formats below:

Pickle

It is a very popular format for python serialization. It is used to serialize almost all the Python object types. Pickle is considered to be an original serialization format used for Python, hence when a user plans to serialize objects in python that he expects to share and he must use with many other languages used for programming, he has to be mindful of the issues such as cross-compatibility. Similarly, pickle works in the same way for various Python versions. The user cannot unpickle a file present in the XXX version, which he picked in the python ZZZ version. So by doing such unnecessary changes, the execution of malicious code gets tough.

Let us see an example below and understand how pickling is performed in python:


import pickle
 
class example_class:
    x_number = 10
    x_string = "Welcome to the tutorial"
    x_list = [10, 20, 30]
    x_dict = {"Heya": "x", "How": 5, "you": [10, 20, 30]}
    x_tuple = (2, 3)
 
my_object = example_class()
 
my_pickled_object = pickle.dumps(my_object)  
print(f"This would be pickled object:\n{my_pickled_object}\n")
 
my_object.a_dict = None
 
my_unpickled_object = pickle.loads(my_pickled_object) 
print(
    f"The dictionary of unpickled object is:\n{my_unpickled_object.a_dict}\n")
 
 Output
This would be pickled object:
b'\x80\x04\x95!\x00\x00\x00\x00\x00\x00\x00\x8c\x08__main__\x94\x8c\rexample_class\x94\x93\x94)\x81\x94.'
 
Traceback (most recent call last):
  File "", line 19, in 
AttributeError: 'example_class' object has no attribute 'a_dict'
> 

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Module Interface for Pickling and Unpickling

The data format is always Python-specific for the pickle module. That is why it is always important to write the essentially required code when the user is performing the process of serialization or deserialization. dumps() is the Python function that is used to serialize an object hierarchy whereas loads() is the function that is used to de-serialize the same.

Pickle Protocols

Protocols in pickle act like the convention measures to deconstruct and construct the python objects. There are in total of 5 protocols that a user can use in pickling. Whenever a user uses a higher protocol version, he will need the latest version of Python to obtain the highly compatible as well as readable pickle.

Protocol version 0: This version is readable by humans. It is compatible to use with data and interfaces from the older python versions.
Protocol version 1: It is known to be an old binary format. Just like protocol version 0, it is also compatible with older python versions.
Protocol version 2: It came into effect during the release of python version 2.3. This version is well known for providing new styles in picking.
Protocol version 3: This version was discovered during the release of python version 3.0. It is famous for supporting byte objects however the major drawback with this version is it gets unpicked by python version 2.0
Protocol version 4: This version was discovered during the release of python version 3.4. This is able to support large objects and various different objects can be picked too. It is also famous for supporting data optimization.

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Numpy

It is a very popular python library used by the user to work with large and multidimensional arrays as well as matrices. It stands for numerical python. They are open source and free to use but slow to process. NumPy arrays can be stored in one continuous place in the memory; however this same is not possible for lists. Processes can therefore access as well as manipulate the arrays very efficiently.

Let us see an example below and understand how the Numpy library is used in python:


import numpy as np
arr = np.array( [[ 10, 20, 30],
[ 40, 20, 50]] )
 
print("The type of array is: ", type(arr))
 
print("The no of dimensions are: ", arr.ndim)
 
print("The shape of the array is: ", arr.shape)
 
print("The size of the array is: ", arr.size)
 
print("Array stores elements of the type: ", arr.dtype)
 
 Output
The type of array is:  <class 'numpy.ndarray'>
The no of dimensions are:  2
The shape of the array is:  (2, 3)
The size of the array is:  6
Array stores elements of the type:  int64
> 

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Conclusion

Serialization is a process that aims at simplifying the data storage methods for a data scientist. Serialization in Python is one of the most important features that ease the data conversion interface of the data. In this article, we have talked about why we need serialization. The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. 

We have also discussed JSON and YAML in python. Then we talked about binary formats of python serialization which are pickle and NumPy. In this sub-topic, we will also have a glance at module instances of pickling and unpickling along with pickle protocols. Now we will be discussing some frequently asked questions by the developers and will give solutions for them.

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