How to Become a Big Data Engineer

Data storage and processing have been leaps and bounds in the last two decades. Whether finance, manufacturing, or any other business, industries continuously generate a large quantity of data. They have realized that data is one of their valuable assets and can be used to bring positive outcomes to their businesses. This is because if you process and analyze a massive amount of data correctly, it will show wonders for you. That's why industries focus on data processing and analyzing to retrieve valuable insights and information

Undoubtedly, the pace of generating data is very high in many businesses, mainly social media companies such as Twitter, Facebook, etc. As a result, they constantly accumulate a large quantity of data daily. Know that it is not an easy deal to manage this big data and retrieve helpful information from the data. You need to effectively process the raw data to make it ready for analysis before retrieving helpful information from the data. Only qualified professionals can process the raw big data by applying suitable techniques.

Now, the role of big data engineers comes into the picture. Essentially, big data engineers transform raw data into well-organized and valuable data and help businesses to reap plenty of benefits from big data. There is a huge demand for big data engineers nowadays all over the world. That’s why a career in big data is considered one of the fast-growing and highly-paid careers.

On that note, this blog is prepared for aspirants who wish to become big data engineers. This blog is a quick guide that unpacks the concept of big data, data engineering, and the skills needed to become a big data engineer.

Here we go!

Table of Content: How to become a Big data Engineer?

What is Big Data? What is Data Engineering? What is a Big Data Engineer? What does a Big Data Engineer do Why Big Data Engineer? What are the Skills required for a Big Data Engineer? Roadmap to becoming a Big Data Engineer Salary and Future Prospects for Big Data Engineers

What is Big Data?

Big data is not only a large quantity of data but an ever-growing entity. No surprise that according to the recent survey of Statista, the volume of data generated worldwide is 79 zettabytes up to the year 2021. It is expected that it will reach 181 zettabytes by 2025.

Big data consists of data collected from many sources in different formats. Three types of data are structured, unstructured, and semi-structured. The data can be customer data, transactions, operational data, or others. Credit card data, e-commerce, POS transactions, social media, and sensor reading of IoT devices are a few examples of data. This data would usually be in petabytes and terabytes. Big data engineers use many tools to process this raw data into organized and actionable data. In this regard, they use tools such as Hadoop, Cassandra, Spark, Apache Storm, etc.

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What is Data Engineering?

It is one of the engineering domains that deal with collecting, processing, storing, and organizing big data effectively. Not just that, it also includes developing systems for processing big data. When it comes to storage, big data storage is entirely different from standard data storage. For instance, if you want to store big data, you need to use a dynamic schema in databases – not a fixed schema. Another thing is that you have to use distributed databases for storing big data – not centralized databases. 

Scaling is yet another crucial thing in managing big data because it needs dynamic storage systems to keep pace with the ever-increasing data volume. Cloud storage offers the best solution to meet dynamic scaling requirements.

What is a Big Data Engineer?

Okay! What does actually a big data engineer mean? Let’s find the answer in this section. A typical big data engineer builds, verifies, and maintains data processing systems. They usually have many crucial roles and responsibilities in managing big data and data processing systems. Here, we will list them all one by one.

• Big data engineers convert unorganized data into accurate, clean, and actionable data with the help of data processing systems.
• They must work with many big data frameworks and interact with different relational databases to efficiently store and retrieve the data.
• They should improve the quality of data to higher levels and increase the reliability and efficiency of data as well.
• They need to design dataset processes with which they can simplify data mining and modeling processes.
• They must ensure that the data processing systems accurately meet all the compliance requirements.
• On top of all, they will transform the raw data into formats that will help others quickly extract valuable information and insights.

What does a Big Data Engineer do?

As mentioned earlier, a big data engineer transforms the raw data into organized data. Do you want to know how they perform this? Read ahead to know the answer.

Essentially, a big data engineer performs the ETL process. It means that they extract data from multiple sources, transform the raw data into the quality and actionable data using data processing systems, and finally load the data in warehouses and data lakes where it will be in the ready state for downstream processing.

Let’s see how a big data engineer does this in the following step-by-step.

•  Building an Environment with Systems and Tools

At first, a big data engineer builds and maintains a big data environment. This is where they build large-scale data processing systems and other related resources. This environment includes the required data architecture and tools to process big data. Generally, the data architecture is built based on business needs. Big data engineers build robust processing systems for data ingestion as well as data processing.

• Transforming and Storing Data

At this stage, big data engineers convert the collected raw data into organized, high-quality, and actionable data. It allows them to apply data transformation methods or algorithms to retrieve valuable information from the data. After that, they store data in data warehouses and data lakes for downstream processing. It is important to note that the transformed data will undoubtedly satisfy the quality and compliance standards for further analysis.

• Optimizing Performance

Next, big data engineers collaborate with data scientists and analysts to improve the quality of transformed data using different algorithms and advanced tools. They use their expertise by integrating different programming languages and tools to create the best data processing models. As a result, they build efficient data processing models and support to produce high-quality and organized data.

Why Big Data Engineer?

Are you wondering why big data engineers are one of the inevitable resources for any business? 

Let’s find the reasons below:

• As you know, big data engineers collect and convert them into meaningful data efficiently. It allows data analysts to retrieve valuable insights and information and track the patterns in the data. By using this information, organizations can boost their productivity and performance significantly.
• They can support designing complex and large-scale data analysis projects by closely working with data architects.
• They help optimize business use cases, predict operational and market risks, and identify channels to create new revenue streams.
• They assist businesses in predicting market trends, tracking KPIs, and many more.

Related Article: Big Data in AWS

What are the Skills Required for a Big Data Engineer?

To become a competent big data engineer, you must acquire the skills listed below.

Let’s take a look at them:

• Programming Languages

You must be able to code in the languages such as C, C++, Java, and Python.

• Data Structures

Understanding various data types such as arrays, binary trees, heaps, graphs, queues, and matrices is essential to become a big data engineer. So, you must thoroughly know structured, unstructured, and semi-structured data. Besides, you must be able to use libraries and gather data from various sources.

• Algorithms

Knowledge of predictive modeling, text analysis, and Natural Language Processing algorithms is required for big data engineers. You should be able to use robust algorithms to manage data in databases. Additionally, you must be familiar with Machine Learning (ML) algorithms. With this ML knowledge, you can build and automate data processing streams as well as pipelines.

• Databases

You should deeply understand various data repository structures, APIs, parallel processing databases, relationship diagrams, and cloud storage. Mainly, you should seamlessly be able to work in relational databases such as MySQL, SQL servers, and Oracle.

• Data Transformation

You must acquire skills in various data transformation algorithms, techniques, and tools.

• Data Pipelines

Managing data pipelines is yet another skill required for big data engineers. So, you must be able to build automated pipelines based on machine learning algorithms. It will help you to transform the data and feed them into downstream processes quickly.

• Data Management Tools

You must be familiar with the tools such as HDFS, Apache Pig, MapReduce, Apache HBase, and Hive. Also, you must be able to use Business Intelligence tools such as Power BI, QlikView, etc.

• Real-time Processing Frameworks

You should thoroughly know many real-time data processing tools such as Beam, Apache Spark, Kafka, etc.

• Data Mining

You must acquire sound knowledge of data mining, modeling, and wrangling techniques. In this regard, you need to learn the tools such as Rapid Miner, KNIME, and Weka in-depth.

Related Article: Data Engineer Interview Questions

Roadmap to becoming a Big Data Engineer

Are you wondering how to become a competent data engineer?

No worries! We help you by providing a helpful roadmap to achieve that.

Let’s have a look at the steps as follows:

Step 1: Education

You must have a bachelor’s degree at the least. And you must have decent computer science, physics, mathematics, and statistics knowledge. You must have gained solid knowledge in functional decomposition, solution engineering, problem resolution, abstraction, etc.

Step 2: Certification

You will need to achieve a professional certificate to sharpen your skills. This is because certifications escalate you to the expertise level in the chosen domain. In a way, it becomes an added advantage to be outstanding among others in the job market.

Let’s see some familiar courses that will significantly elevate your knowledge.

Cloudera Certified Professional (CCP) Data Engineer: You can complete this course to sharpen your skills in data analysis, data staging and storage, data ingestion, and data transformation.

Certified Big Data Professional (CBDP): This certification will help to acquire skills in Business Intelligence tools and data science. This program demands a bachelor's degree and a minimum of one year of working experience as entry qualifications.

Google Cloud Certified Professional Data Engineer: Completing this course will help to polish your skills in data structures, machine learning methods, and data streams.

Step 3: Taking Internships

You need to earn some working experience through internships on noteworthy projects that include data processing, Data warehousing, BI tools, Cloud computing, Data Science, and Data lakes. This experience will assist you in becoming a confident and competent big data engineer.

Step 4: Developing Soft Skills

Of course! Apart from earning technical skills, you must acquire essential soft skills. To achieve this, you will need to:

• Develop your communication, analytical, problem-solving, and logical thinking skills.
• Be curious about new tools and willing to learn them on the go.
• Constantly collaborate with SMEs, data science teams, and business analysts; always be part of the entire team.
• Make continuous research on data engineering, which will allow you to improve data processing quality.
•  Have the enthusiasm to resolve complex problems with innovative solutions

Related Article: Big Data Analytics Tools

Salary and Future Prospects for Big Data Engineers

According to the latest survey of Statista, the Big data analytics industry will have reached up to 15.75 billion US dollars by 2021. And it is expected that it will reach 68 billion US dollars by 2025. These statistics indicate that big data engineers have solid prospects in their careers without a doubt

Next, we will move on to the salary details of a big data engineer in the job market.

According to a Forbes advisor, the salary of a big data engineer comes to around 93,000 USD per year worldwide. And at the same time, according to a survey, the average salary of a big data engineer comes to around 9.5 LPA in India. These salaries are no less than the salaries of software developers or data scientists. So, there are a lot of merits to becoming a big data engineer.

Conclusion

To sum up, you might have gone through what is a big data engineer, the roles and responsibilities of a big data engineer, and the skills required for a big data engineer. We hope the roadmap you have come across in the blog will guide you to gain the required skills and capabilities. Okay! Start your journey to becoming a competent big data engineer at the earliest.