Team Apr 10, 2026 No Comments
Team Oct 11, 2022 No Comments
Guess how much data engineers earn? An average salary of ₹7,44,500 per year in India. And senior data engineers earn between ₹11 lakhs to ₹24 lakhs per year.
And if you search for data engineering jobs on Indeed or LinkedIn, you will find over 20,000 vacancies in India alone. But why am I telling you all this?
Because I want to show how in-demand data engineers are in the market. With more and more companies relying on data to make smart decisions, data engineering has become a necessity.
Now, if you want to start a career in data engineering, you have to learn some technical and soft skills. In this post, I will tell you what those essential data engineering skills are and how you can start your career in this field.
You can think of data engineers as the architects of the data world. They build and maintain systems and architecture to collect, store, and manage data.
For example, they work with ETL (extract, transform, load) processes to combine data from multiple sources into a central repository. Similarly, they build data pipelines, work with databases, and manage data warehouses.
They basically prepare the raw data for analysis so that data analysts and data scientists can do their jobs. This way, they work together to help the company find valuable insights and make decisions that lead to business growth.
To perform all those tasks, data engineers need to gain expertise in various processes, tools, and technologies. They also need soft skills to work together as a team and communicate effectively. Here are all the skills you would need to become a pro data engineer:
Aspiring data engineers often ask me, “Is coding important?” And I always tell them that coding is one of the most important skills for data engineers. It is what will help you optimize and automate data workflows as well as improve data quality and reliability. Here are some programming languages often used in data engineering:
Python: It’s good for handling big data, automating tasks, and working with different data formats. It has a range of libraries that make data processing easier and faster.
SQL: It helps manage and query large databases. It’s also needed for data handling tasks like extracting, organizing, and updating data stored in databases.
Java: This language helps you build scalable, high-performance data pipelines. It helps you develop large, reliable systems that handle big data processing efficiently.
Scala: It helps you work with distributed data systems like Apache Spark. It’s specifically designed to handle large datasets while maintaining performance.
R: It helps in data analysis and statistical tasks. You will need it to perform complex data manipulation and generate insights from data sets.
You can go through this data engineering syllabus to learn more about technical skills that are valued in the present market.
One of the most important roles of data engineers is to store and organize raw data in data warehouses. Data warehouses are simply central repositories that allow access to real-time data for analysis and decision-making.
Without this skill, you won’t be able to manage the high volume and complex data most companies handle today. So, you need to know about data warehousing solutions like Panopoly or Amazon Redshift. This way, you can make data storage, retrieval, and processing more efficient.
Knowing programming languages isn’t enough. You also need an understanding of operating systems to design, develop, and troubleshoot systems. As a data engineer, you will work with operating systems like Linux, UNIX, macOS, and Windows because data infrastructure often runs on these platforms. For example, Linux is widely used in data engineering because of its stability, flexibility, and performance.
This data engineering skill helps you design, maintain, and optimize databases. SQL is the most widely used language for managing relational databases, allowing you to query, update, and manipulate data efficiently. You also need to learn NoSQL databases like Cassandra or Bigtable, which are better suited for handling unstructured data.
You can learn basic SQL queries, cleaning and modifying data, aggregating and analyzing data, working with multiple data tables, troubleshooting and error handling, advanced filters in SQL, data definition language, data manipulation language, using subqueries, creating user-defined functions, etc.
This is an important data engineering skill because you will often have to work with big datasets that traditional databases can’t handle. It will make you an expert at managing and processing data on a large scale.
For this, you can learn Hadoop, which includes topics like MapReduce, YARN, HDFS, data spilling, data replication, Daemons, etc. You have to learn Apache Hive to query large datasets using HiveQL. You also need to know Apache Spark, how to optimize it, and how to process data in real time. A good understanding of real-time data processing with Kafka and its integration with Spark is also important.
Microsoft Azure is a cloud platform that provides scalable, secure, and cost-effective data storage and processing solutions. So, this skill helps you build and maintain data pipelines, store data, and run large-scale analytics in the cloud.
Here, you will learn about Azure services like virtual machines, storage, and database services. Next, you can understand advanced data engineering with Azure and real-time data streaming and processing. Learning hybrid cloud scenarios, governance, and compliance is also necessary.
This data engineering skill helps you better analyze and evaluate a situation. You need this to identify problems related to data collection, storage, or analysis and then develop effective solutions. You have to come up with innovative solutions to improve the performance of the systems and the quality of the data. This is where critical thinking helps you.
As a data engineer, you will collaborate with other team members and business leaders with and without any technical expertise. So, better communication skills help you explain data processes and systems and share updates without any misunderstandings. For example, you may have to work with data scientists or analysts and share findings and suggestions. And you know, this skill not only helps you in data engineering but also in your entire life.
Now, let’s understand how to become a data engineer:
Even though formal education may not be that important these days, most employers need data engineers to hold at least a bachelor’s degree. You should hold a degree in something like computer science, computer engineering, information technology, software engineering, applied math, statistics, physics, or a related area.
You just saw the important data engineering skills in the above section. You need to master them. And one of the best ways to do it is to take a comprehensive course.
For example, Ivy Professional School’s IIT-certified Data Engineering Certification course helps you learn all the in-demand skills like SQL for data engineering, Python essentials for data, Big data processing, and Azure cloud engineering.
This 45-week live online course will make you an expert at building a complete ETL data pipeline on the cloud with tools like Azure, Hive, MongoDB, Spark, Hadoop, etc. The really interesting thing about this program is that you will learn from IIT Guwahati professors and expert professionals at Amazon, Google, Microsoft, etc.
Just gaining theoretical knowledge isn’t enough. You also need to know how to implement your knowledge and solve real business problems. And you can do this by working on data engineering projects and doing internships.
Again, Ivy Professional School’s Data Engineering Certification course helps you work on 30+ real-life projects. This way, you not only gain practical experience but also build an effective portfolio that showcases your skills. Besides, you will earn a valuable certificate from E&ICT Academy, IIT Guwahati, after completing the course. This way, you can become a credible data engineer.
Related: 6 Best Data Engineering Courses
The demand for data engineer roles has increased astronomically. Organizations are actively searching for data engineers to enhance their data processes and ultimately make smart business decisions. The above data engineering skills are in demand, and those who can master these skills will have a good chance of earning high salaries. The question is, will you be one of those?
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 24, 2022 No Comments
Before a model is created, before the existing data is cleaned and made ready for exploration, even before the responsibilities of a data scientist start – this is where data engineers come into the frame. In this article, we are going to have a look at what is data engineering.
Every data-driven business requires a framework in place for the flow of data science, otherwise, it is a setup for failure. Most people enter the data science niche with the focus of becoming a data scientist, without ever knowing what is data engineering and analytics are and what the role of a data engineer is. They are crucial parts of any data science venture and their demand in the sector is evolving exponentially in the present data-rich scenario.
There is presently no coherent or official path available for data engineers. Most people in this role reach there by learning on the job, rather than abiding by a detailed avenue.
A data engineer is responsible for constructing and maintaining the data frame of a data science project. These engineers have to make sure that there is an uninterrupted flow of data between applications and servers. Some of the responsibilities of a data engineer involve enhancing data foundational procedures, including the latest data management technologies and also software into the prevailing mechanism, and constructing data collection pipelines among various other things.
One of the most crucial skills in data engineering is the potential to design and construct data warehouses. This is where all the raw data is collected, kept, and retrieved. Without data warehouses, all the activities that a data scientist does will become either too pricey or too big to scale.
Extract, Transform, and Load (ETL) are the steps that are followed by a data engineer to construct the data pipelines. ETL is crucially a blueprint for how the assembled data is processed and changed into data ready for the purpose of analysis.
Data engineers usually have an engineering background. Unlike data scientists, there is not much scientific or academic evaluation needed for this role. Engineers or developers who are interested in constructing large-scale frameworks and architecture are ideal for this role.
It is crucial to know the difference between these 2 roles. Broadly speaking, a data scientist formulates models using a combination of statistics, machine learning, mathematics, and domain-based knowledge. He or she has to code and construct these structures using similar tools or languages and also structures that the team supports.
A data engineer on the contrary has to maintain and build data frameworks and architectures for the purpose of data ingestion, processing, and deploying of large-scale data-heavy applications. Construct a pipeline for data storage and collection, funnel the data to the data scientist, to put the structure into production – these are just some of the activities a data engineer has to do.
Now that you know what is data engineering, let us have a look at the roles of a data engineer.
Here are some of the skills that every data engineer should be well versed in.
After this guide on what is data engineering, you must have known that becoming a data engineer is not an easy job. It needs a deep evaluation of tools, technologies, and a solid work ethic to become one. This data engineering job role is presently in huge demand in the industry because of the recent data boom and will prevail to be a rewarding career choice for anyone who is willing to adopt it.
Team Sep 17, 2022 No Comments
Keras is an easy-to-use, strong, free open-source Python library for evaluating and developing deep learning frameworks. This is a part of the TensorFlow library and enables you to illustrate and train neural network structures in just some lines of code. In this article, we will be talking about what is Keras and TensorFlow.
Let us start this Keras tutorial. Keras was formulated to be quite user-friendly, easy to extend, modular, and to work with Python. The API was “designed for human beings, not machines” and “follows best practices for reducing cognitive load.”
Neural layers, optimizers, cost operations, activation operations, initialization schemes, and regularization schemes are all standalone structures that one can combine to formulate a new module. New modules are easy to add, as new functions and classes. Models are illustrated in Python code, not different structure configuration models.
In this article on what is Keras in Python, let us have a look at the key features of Keras:
Being a high-level library and is a convenient interface, Kears certainly boosts as one of the deep learning libraries that is available. There are several features of itself, which makes it more convenient to use and also offers more features.
– If Keras is compared with and Theano, it tries to offer a better “user Experience” and this zones Keras above the other two libraries.
– As this is a Python library, it is more available to the general public because of the inherent simplicity of the Python programming language.
– Lasagne is a library that is very similar to Kears. But using both the libraries I can say Keras is much more convenient.
Now that you know the advantages of using the Keras library, you should also be aware of the disadvantages of Kears.
– Since this Python library has its dependency on low-level languages such as TensorFlow and Theano, so this performs as a double-edged sword for Keras. This is the primary reason why Keras cannot move beyond the realms of these libraries. For instance, both TensorFlow and Theano presently do not support GPUs except Nvidia. That is the reason Keras also does not have the corresponding support.
– Similar to Lasagne, eras also wholly abstract the low, level languages. So this is less flexible when it comes to creating custom functions.
– The final point is that this is new in this niche. Its very first version was launched in late 2015, and it has gone through various alterations since then. Then even though Keras is already used in the process of production, one should always think twice before they deploy Keras models for production.
Models are the primary entity one will be working with when using Keras. The structures are used in defining TensorFlow neural networks by mentioning the attributes, operations, and layers you wish.
Keras provides a range of APIs you can employ to illustrate your neural network, involving:
– Sequential API allows one to formulate a model structure by structure for most issues. It is straightforward but restricted to single-input, single-output stacks of layers.
– Functional API is mainly a full-feature API that supports arbitrary framework architecture. It is more complicated and flexible in comparison to the sequential API.
– Model Subclassing allows one to enact everything from scratch. Ideal for research and highly complicated use scenarios, but it is hardly used in practice.
ML (Machine Learning) algorithms use a special form of an algorithm that is known as a Neural Network. Same as every machine learning algorithm, it also abides by the general ML workflow of data preprocessing and model evaluation. For ease of use, here are some of the to-dos on how to approach a Neural Network problem.
– See if there are issues where a neural network offers you an uplift over traditional algorithms.
– Conduct a survey for which neural network architecture is the most ideal required issue.
– Illustrate neural network architecture via language or library as per your choice.
– Change data into the ideal format and divide it into varied batches.
– As per your requirements, pre-process the data.
– Augmented data is utilized to magnify size and make better-trained frameworks.
– Batches are nourished by the neural network.
– Track alterations in training and validation data sets, and also train them.
– Test your frameworks.
After this article on what is Keras model, you come to know about how this is useful in the area of deep learning along with its benefits and also limitations. We have also seen how this Python library depends on low-level languages such as Theanos and TensorFlow. Also if you were wondering what is Keras vs TensorFlow, then you must have got your answer.
Career prospects in data science and data analytics have increased over time and this has become a much-acclaimed niche presently. If you wish to start your career in data science then there can be no better place than Ivy Professional School. It offers a complete course on data science conducted by industry experts. For more details, you can visit their website.
Team Sep 13, 2022 No Comments
Updated in May, 2024
Do you know Netflix and Spotify use the Scikit-learn library for content recommendations?
Scikit-learn is a powerful machine learning library in Python that’s primarily used for predictive analytics tasks such as classification and regression.
If you are a Python programmer or aspiring data scientist, you must master this library in depth. It will help you with projects like building content-based recommendation systems, predicting stock prices, analyzing customer behavior, etc.
In this blog post, we will explain what is Scikit-learn and what it is used for. So, let’s get started…
Scikit-learn is an open-source library in Python that helps us implement machine learning models. This library provides a collection of handy tools like regression and classification to simplify complex machine learning problems.
For programmers, AI professionals, and data scientists, Scikit-learn is a lifesaver. The library has a range of algorithms for different tasks, so you can easily find the right tool for your problem.
Now, there is often a slight confusion between “Sklearn” and “Scikit-learn.” Remember, both terms refer to the same thing: an efficient Python library.
Although Scikit-learn is specifically designed to build machine learning models, it’s not the best choice for tasks like data manipulation, reading, or summary generation.
Scikit-learn is built on the following Python libraries:
Scikit-learn was developed with real-world problems in mind. It’s user-friendly with a simple and intuitive interface. It improves your code quality, making it more robust and optimizing the speed.
Besides, the Scikit-learn community is supportive. With a massive user base and great documentation, you can learn from others and get help when you need it. You can discuss code, ask questions, and collaborate with developers.
Scikit-learn was created by David Cournapeau as a “Google Summer Of Code” project in 2007. It quickly caught the attention of the Python scientific computing community, with others joining to build the framework.
Since it was one of many extensions built on top of the core SciPy library, it was called “scikits.learn.”
Matthieu Brucher joined the project later, and he began to use it as a part of his own thesis work.
Then, in 2010, INRIA stepped in for a major turning point. They took the lead and released the first public version of Scikit-learn.
Since then, its popularity has exploded. A dedicated international community drives its development, with frequent new releases that improve functionality and add cutting-edge algorithms.
Scikit-learn development and maintenance is currently supported by major organizations like Microsoft, Nvidia, INRIA foundation, Chanel, etc.
The Scikit-learn library has become the de facto standard for ML (Machine Learning) implementations thanks to its comparatively easy-to-use API and supportive community. Here are some of the primary uses of Scikit-learn:
Here’s a small example of how Scikit-learn is used in Python for Logistic Regression:
from sklearn.linear_model import LogisticRegression; model = LogisticRegression().fit(X_train, y_train)
Explanation:
Now, you must have understood what is Scikit-learn in Python and what it is used for. Scikit-learn is a versatile Python library that is widely used for various machine learning tasks. Its simplicity and efficiency make it a valuable tool for beginners and professionals.
If you want to learn machine learning with the Scikit-learn library, you can join Ivy’s Data Science with Machine Learning and AI certification course.
This online course teaches everything from data analytics, data visualization, and machine learning to Gen AI in 45 weeks with 50+ real-life projects.
The course is made in partnership with E&ICT Academy IIT Guwahati, IBM, and NASSCOM to create effective and up-to-date learning programs.
Since 2008, Ivy has trained over 29,000+ students who are currently working in over 400 organizations, driving the technology revolution. If you want to be the next one, visit this page to learn more about Ivy’s Data Science with ML and AI Certification course.
UsernamE Jul 30, 2021 No Comments
Artificial intelligence(AI) and Data Science are ready to transform our unimaginable lives years ago. For that reason, Data Science and Artificial Intelligence have become the “trending topics” in every article of 2021. While everyone is able to interpret the actual reason for the wave of such a lucrative job
UsernamE Jul 30, 2021 No Comments
SQL (often pronounced as ‘sequel’) is the acronym for Structured Query Language. To pioneer in the field of data analytics, data science or data engineering, SQL becomes one of the most vital building blocks. When people start getting into data science, the biggest problem that they face is coding.
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“India is today among the top 10 destinations for analytics and our aspiration is to be among the top three in the
Anubinda Oct 28, 2020 No Comments
Undoubtedly, data science has taken down most industries, making data scientists the most sought job of the 21st ce