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How to Become a Big Data Engineer [Ultimate Guide 2022]

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.

All of these activities are performed by a group of highly skilful big data engineers. In engineering big data is a relatively emerging field and with its growing popularity,  big data engineering is also gaining the ineterest of many aspiring candidates. 

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. 

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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. 

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What is the Difference Between Data Engineer and Data Scientist?

Data scientists develop solutions, while data engineers create systems for implementing them. 

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. 

Thus to conclude, it can be said that a big data engineer basically builds and maintains systems that further help in collecting and extracting data, whereas a data scientist helps analyse those pieces of data and draw insightful conclusions from them.

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. 

What is Big data and What makes it different from the traditional types of data?

Even though there is no hard and bound definition of Big data, simply put, it can be said, Big data is a larger, more complex and rapidly expanding type of data that is collected from multiple sources in multiple forms. Based on its nature, it can be categorised into three groups that are structured data, unstructured data and semi-structured data. 

The term “Big” isn’t used just because it is comparatively some amount bigger than the traditional type of data, but due to its problematic nature. To be more precise, our storage systems have been accustomed to storing a certain amount of data in a certain flow. 

However, now that we have understood the importance of unstructured data and how insightful it can be for businesses and industries, we are trying to store every bit of unstructured data that is generated every day and every second. This is making it more and more difficult for conventional storage systems to manage and thus the name “Big data” was invented. 

Big data engineering is different from traditional data engineering in several ways. The first and foremost is the database schema that is sued. For traditional types of data, the databases use a fixed schema, whereas big data uses a dynamic schema. Secondly, with the introduction of big data, the type of data analytics has massively changed. Earlier, data was collected and then analyzed but now for big data, analysis takes place in real-time. 

Along with that, traditional data is based on centralized database architecture, whereas engineering big data needs distributed architecture to support the massive amount of data that is generated and thus uses multiple computers n a network. This feature is the future as it solves the scalability issue of traditional data and promotes the use of cloud storage, open-source software and other commodity hardware. 

The main difference is also the type of sources that these two types of data use. In the case of traditional data, sources are very limited and often not that easy to access. Whereas, social media platforms, readings from medical equipment, embedded devices in vehicles, and crow density calculators are just some of the examples from which big data is sourced. 

All these elements combined made the biggest difference between traditional data and big data and that is the ability to perform exploratory analysis. Big data changes the entire approach of data analytics and made it more iterative and exploratory. The earlier approach was only helpful in curating monthly reports, customer survey findings and other types of records that are just full of straightforward pieces of information. Whereas, with the help of big data, businesses were able to make sentiment analyses, product strategies and perform asset utilizations. 

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:

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Algorithms

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. 

Data Structures

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:

  • Array
  • Heap
  • Binary Tree
  • Graph
  • Queue
  • Matrix

Once you get familiar with basic data structures, you can move onto abstract data structures.

SQL

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. 

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Distributed Systems

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.

Data Pipelines

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. 

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Conclusion

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 interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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What is Apache Kafka?

Apache Kafka is an open-source, community-based, event-streaming platform that can handle a massive quantity of data on a daily basis. It has found widespread use due to the many advantages it offers. Many companies use it for tasks like streaming analytics, data integration, and operational monitoring data, and providing durable data storage to companies. It offers many benefits to its users, such as reliability, scalability, durability, and guaranteed, zero data loss.

What is Data Modelling?

Data modelling involves creating data models or simplified diagrams of data associations and data structures using texts, symbols, etc. The primary purpose of data modelling is to use the data to derive meaningful insights that help the companies operate smoothly. Data modelling organises and documents the data in a meaningful and logical manner. The presentation of data visually helps enhance the understanding of the data. There are 3 types of data models: Conceptual model, a visual representation of relationships between database concepts; Logical model, which defines the relationship between various data entities; and Physical model, which represents related data objects such as tables and columns.

What is the difference between structured and unstructured data?

Data is the foundation on which the whole analysis is conducted. Any company derives insights utilising these very data and uses them to make appropriate business decisions. There are many forms and varieties of data. These data can be classified into two categories: structured and unstructured data. Structured data is specific and is stored in a predefined structure. On the other hand, unstructured data is stored in the original format and is not processed until used. Structured data is quantitative, whereas unstructured data is qualitative. You can quickly analyse structured data, while unstructured data needs to be processed to derive insights.

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