Technology and Trends

What is Data Engineering and Why It is Important? A Guide for a career in Data Engineering

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What is a Data Engineer?

The general job of the engineer is to design and build things. In the field of software engineering, engineering design, and building software. When we relate engineering to the same definition, we can define data engineering as a field where data is formatted by designing and building data pipelines and transported to end-users or downstream. These people who follow the field are data engineers. Here the end-user or downstream can be an application, process, a team of data scientists, or another. This data pipeline takes data from various sources and collects them into a single repository that is represented as a single source of truth or System of Record(SOR).

What are the Roles and Responsibilities of a Data Engineer?

There is no typical role of a data engineer, as it varies in different organizations and needs But there are some generic tasks many data engineers do in their job. Following are some roles.

Data Engineer Resposnbility

Why do we need Data Engineering?

According to an article by Gartner in 2017, around 85% of big data failed because of a lack of reliable data infrastructure. When produced data is well derived, businesses can make decisions based on it. Many IT-based companies are trying to do a data science-based project. Most of them fail and never make it to production as they don’t have good data engineering teams to provide them with quality data.

When the field of Big data analytics started initially, data scientists had to work in both the roles of data engineer and data scientist. They were expected to build the necessary data pipelines to get the data, as well as set up the data. This caused an increase in turnover rates among data scientists in various companies, as they spent more time preparing the data rather than analyzing it. To avoid this scenario, organizations started forming a separate team of data and infrastructure engineers who would create the necessary data pipelines and infrastructure required to get the needed data. With a separate data engineering team. Data scientists can focus on core tasks like developing a Machine Learning Model or analyzing data.

Currently, most of the big technology firms have their data engineering teams.

Why is Data Engineering Important?

In the current day, data is critical to everyone, regardless of its size. Having the right data at the right time means organizations can use this knowledge to cater to client needs while disrupting the market. Hence, large corporations are ingesting, enriching, producing, and creating more data at a much faster speed and scale than ever before.

The process of converting the collected data into knowledge is complex. Data processing and analysis processes have several levels in every organization. Data engineers play a crucial role in handling this complex process by designing and operating data pipelines to extract information from the data. When the data travels through the data pipeline, it is transformed, enriched, and summarized before businesses can derive value from it.

When data is obtained from sources like ERP(Enterprise Resource Planning) systems, supply chains, third-party vendors, and internal lines of business, it can be unstructured and unformatted. Data engineers collect these data by using various ETL(Extract, Transform, and Load) processes while automating, optimizing, and transforming data so that it becomes a usable business asset to the organizations. In addition to this ETL-based process, different monitoring tools need to be set up to monitor the job and trigger any alerts when there is any failure. For different validations, the reconciliation process is set up which validates the output data against the collected inputs.

In today’s market, organizations collect all kinds of data at various speeds that can be categorized as below.

Batch data can be handled by tools like Apache Spark, Apache Hive, Teradata, Pentaho, and Ab Initio

This data can be handled by tools like Apache Spark, Apache Storm, Apache Kafka, Amazon Kinesis, Apache Flink, etc

This type of data can be handled by Apache Spark, Amazon Kinesis, Apache Kafka, etc

In addition to these tools, programming languages like Java, Scala, Python, and R can also be used to develop data pipelines to handle this data. Query language like SQL (Structured Query Language) can be used to extract the data from the RDBMS like Oracle Database, Oracle Exadata, MS SQL Server, etc.

There are different tools and software that can handle these data. Data engineers must be aware of all these tools and techniques. They need to make sure this software integrates seamlessly into the existing system of the organizations.

Skills Needed for a Data Engineer

With the right mindset, skills, and knowledge, one can begin a career in the field of data engineering. Although it’s not necessary, organizations expect one to have a bachelor’s degree in computer science. If you want to have more opportunities from a career perspective, consider having a master’s degree.

Data engineers need to have various levels of skills in finding solutions related to the problems about data. Furthermore, they have familiarity with different tools and technologies. These tools and techniques change from time to time. Problems that we saw 20 years ago will not be the same as today. Because of this reason, there are many software and skills one needs to have as part of their data engineering skills.

Foundational Software Engineering Skills

One needs to be familiarized with Software Development Life Cycle (SDLC) Agile, DevOps, and Service-oriented Architecture as part of this.

Big Data Technologies

When one works as a data engineer, one works on many problems that can be solved by distributing the data or computing power. To do that, data engineers need to learn about different software patterns like publish-subscribe, big data access, and real-time data access. Some of this software includes big data technologies like Apache Spark, Hadoop, Hive, MapReduce, NoSQL databases, and others.

Relational and Non-Relational Database

Databases are one of the most common solutions when it comes to data storage. One should be familiar with both relational and non-relational databases and their differences. In addition to that, one should learn about Structured Query Language (SQL) which is used for querying relational databases. Some of the popular databases are given below.

Programming and Scripting Language

One should be familiar with various programming languages when working as a data engineer to work with data pipelines and implement data-driven solutions. Python is one of the most favored languages among data scientists and engineers. Java has been popular in traditional software-based organizations, whereas it’s not so popular among data engineers and data scientists. One can also learn about Scala and R, which are popular in many organizations.

There are many cases where one needs to write shell scripts to automate tasks. Shell scripting is one of the common requirements in many of the top companies. so one must become familiar with the skills needed to write shell scripts to automate repetitive tasks.

Orchestration Framework

Modern software follows the orchestration pattern that favors a centralized application workflow. There is a lot of software that helps us to do that. Some of these software are Control-m, Airflow, TWS Scheduler, AutoSys, etc.

CICD Life Cycle

Modern software follows the Continuous Integration and Continuous Delivery (CICD) pattern that enables software to be delivered in a quick, safe, and repeatable manner. It enables software delivery workflows that involve multiple teams and functions spanning development, assurance, operations, security, and finance teams.

Data Storage and Formats

The nature of the data engineering projects determines the data storage type and the format to be used. It means that all data should not be stored in the same way. We can store data in different file systems depending on whether our target system is a database data warehouse or data lake. Data engineers should understand the scenario in which they should store the data in flat files like CSV or TSV format.

When we work in a data lake, we generally save data in either Parquet, Avro, or ORC format. These data formats also differ in different organizations.

Cloud Computing:

Top Information Technology companies are increasingly moving their infrastructure, data processing, and storage to the cloud as they gather massive amounts of data from their business. Therefore, one should have good knowledge of cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.

Data Warehouse

Data engineers should be familiar with data warehousing concepts, including ETL (Extract, Transform, Load) processes, data modeling, data integration, and Total Data Quality(TDQ).

Data Security

With the increase in data breaches and cyber-attacks, data engineers must be familiar with various data security concepts such as encryption, access control, and secure coding practices.

Popular ETL Tools

ETL stands for Extract, Transform, and Load. It is a process through which data can be moved from one system to another system, like a single repository or a data warehouse. Various transformations are applied to the data during the movement so that data in the target system is standardized. Many ETL tools can be used to perform this task. Some of these tools are given below.

Cloud Platforms

Data engineers need to know about various cloud platforms and their services. They need to understand the difference between the different cloud platforms and how they work. Many of the organizations that have on-premise clusters are planning to move to the cloud and need resources that know cloud platforms.

Some popular cloud platforms are given below.

Conclusion

So far, We have learned about data engineering, tools, and software needed for data engineering, and the importance of data engineering in on-prem and cloud platforms.

Do you think this is enough to learn about data engineering? Should we cover any more topics?

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