Do you wonder how companies use the data they collect? why does it matter?
How do they convert their collected data into useful information? How do they develop solutions for using this data?
If such questions pique your curiosity, then the field of big data engineering will undoubtedly interest you.
It’s a vast field with a bright scope in India, that covers data collection, data processing, and many other areas.
In this article, we’ll discuss the field of data engineering and help you find out how to become a big data engineer.
Ready? Let’s get started.
What is Data Engineering?
Data engineering is the branch of data science that focuses on practical applications of data analysis and collection.
Like other branches of engineering, data engineering deals with applying data science in the real world.
Data engineering isn’t related to experimental design. It is more focused on developing systems for better flow and access to the information.
What is the Difference Between Data Engineer and Data Scientist?
This is the most significant point of difference between the two. Data scientists work on the abstract, but data engineers work on practical projects.
Both of them are important. Without a data scientist, the engineer wouldn’t have anything to work with.
Similarly, without a data engineer, the work of data scientists wouldn’t have any value. From solving business problems to converting code into a project, data engineers perform a variety of valuable tasks.
What Does a Data Engineer Do?
A data engineer has to develop and maintain data architectures (such as a database). They look after the collection of data and the conversion of raw data into usable data.
Without a data engineer, you can’t collect data. Companies require their data engineers to be familiar with SQL, Java, AWS, Scala, etc.
Data engineering requires a background in backend development or programming.
If you’re a data engineer, you’ll have to manage the collection of data and handle its storage, and process it for further use.
Some of the skills companies look for in data engineers are:
- Knowledge of Java
- Data Structuring
- Big Data (Hadoop and Kafka)
The requirements can vary mainly according to the company. Some companies don’t require much data engineering at all, while some (IT giants) require multiple applications of data engineers.
How to Become a Data Engineer
To become a data engineer, you will need to get familiar with all of its concepts.
Data engineering consists of collecting, managing, and processing the data. While data scientists are experts in Maths and Statistics, data engineers are experts in Computer Science and Programming.
However, you don’t necessarily need to have a computer science background to enter this field. Like other data-related fields, you’ll find people from various backgrounds in this sector too.
To become a data engineer, you should learn the following things:
Algorithms are instructions for a series of actions to perform in a specific order. Usually, algorithms are independent of the programming language.
This means you can use an algorithm irrespective of the programming language you’re using.
In data structures, you’ll be using algorithms for the following tasks:
- Finding an item in a database
- Inserting an item in a database
- Sorting the items in a particular order
- Deleting an item
It is a fundamental concept of data engineering. So you should put in considerable time in mastering it.
A data structure is a way of organizing data for better management. While handling data, you have to keep it in an efficient order so you can access it easily.
Data structures (also known as databases) are of different types. You will have to get familiar with each one of them.
Some of them are:
- Binary Tree
Once you get familiar with basic data structures, you can move onto abstract data structures.
SQL stands for Structured Query Language). It has been present in the market since the 70s and has become the first choice for many developers, engineers, and analysts.
No matter what anyone says, SQL is here to stay. A data engineer must know this language.
There were rumors that SQL is dying or losing popularity, but they are all fake. SQL isn’t dying. It is one of the most popular programming languages among data professionals.
Why is SQL essential, and why do so many data professionals use it?
Well, SQL is the primary language one uses to generate queries to the database from a client program. In other words, it allows your database servers to edit and store data on them.
Without SQL, you can’t perform those tasks.
Moreover, it is used almost everywhere, so learning it will help ensure that you can work with any organization required.
Python and Java (or Scala)
Python is present everywhere. It is a must-have for any data enthusiast. It is widely popular because of its versatility and ease of working.
You can find a Python library for any task you want to perform. Java and Scala are equally crucial for you to learn.
That’s because most of the data storage tools are written in these languages, including Hadoop, HBase, Apache Spark, and Apache Kafka.
You can’t use these tools without learning these languages. It will help you in understanding how these tools work and what you can do with them.
Each of these languages has its qualities. Scala is fast, Java is vast, and Python is versatile.
Big Data Tools
There are tools popular in this field. They include:
- Apache Hadoop
- Apache Spark
- Apache Kafka
Try to learn about them as much as you can. Learning about these big data tools and technology is necessary because they make the task of data storage and management more effortless.
For example, professionals use Hadoop for solving problems related to vast amounts of data and collection. It is a group of open-source software solutions and frameworks.
Similarly, Spark provides you with an interface for programming clusters.
Many companies require candidates to be familiar with these tools.
The tools we’ve mentioned above are the most popular ones in the big data industry. However, they aren’t the only tools data engineers use for their tasks. You will need to learn about more tools as you get deeper into the subject.
Data is present in clusters, which function independently. A large cluster would have a higher chance of developing problems in comparison to a smaller one due to the presence of more member nodes.
For becoming a data engineer, you will have to learn about data clusters and their systems.
You will also have to learn about the various kinds of problems data clusters face and how to solve them.
A data pipeline is a software solution that creates a pathway for data flow and removes multiple manual steps from the transfer of data from one point to another.
Although a data pipeline can transfer data to data warehouses, the destination doesn’t always have to be that.
You can use data pipelines to transfer chunks of data to applications, as well.
As a data engineer, you’ll be spending a lot of time in building and managing data pipelines. Data pipelines help in generating abundant sources of data, storing the data in the cloud, and performing data analysis.
How to learn all this?
The topics we discussed in the previous section were only the fundamentals. There are many sections present in this field, including real-time data processing and big data analytics.
To become a data engineer, you should check our PG Certification in Big Data Engineering.
This course covers all the basics while teaching you about the advanced concepts as well.
Whether you’re a student or a working professional, you will not face any difficulty while studying this course.
It has the following advantages:
- Over 400 hours of study material
- BITS Pilani alumni status
- More than 7 case studies and projects
- Quick doubt resolution
Developed with BITS Pilani, this course also comes with job placement assistance. So you don’t face any difficulties in getting a job as a data engineer later on.
You will also get to develop a network of Big Data professionals with the help of this course.
The field of data engineering is big. And there’s a lot of demand for people skilled in this area. All it takes is one step, so start your learning journey today.
If you are curious to learn about big data, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.