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What Skills are Required to become Data Engineer?
What is Data Engineering?
Broadly, data science and data engineering complement each other for optimal utilization of unstructured and raw data. Data scientists and data engineers, in turn, complement each other as well in building pipelines transforming the format of the raw data that data scientists can use. The data recovery processes are optimized by data engineers as well as create reports and dashboards.
Furthermore, it depends on the company as to whether or not data engineers would also be responsible for relaying data trends. The role of a data engineer requires a broad spectrum of skills in data engineering, inclusive of a sound knowledge of programming and database languages as well as a wide-ranging business acumen for working in different departments.
The investment of big corporates is typically both in data engineering and data science teams, however, when it comes to SMEs data engineers play a dual role. Regardless of an organization’s size where data engineers might be working, there are certain compulsory skills that data engineers ought to have for a successful career. This article aims at exploring those essential skills of data engineering.
Data Engineering Skills In-Demand
Listed below are a few of the in-demand skills in data engineering that employers seek the most. Let’s peruse the crucial skills in data engineering.
Machine Learning (ML) Algorithms – The preceding data used in ML algorithms enable predictions. As data engineers and data scientists collaboratively work, they must be equipped with the fundamental knowledge and understanding of the algorithms to ensure that the models are in production thereby improving accuracy while building data pipelines.
ETL Tools – The full form of ETL is Extract, Transfer, Load, and ETL is a well-known technology category for collecting data from varied sources, reading, and transferring unprocessed data into a business intelligence(BI) platform or a database. This procedure aids and abets extracting the relevant data for analysis and solving a certain business issue, therefore this technology is crucial in data engineering.
Data Warehouse Solutions – Data engineers typically manage sourcing volumes and volumes of information extensively, data engineers must be knowledgeable about data warehouse solutions including RedShift, Amazon, Oracle, MarkLogic, and so on.
Database Systems – NoSQL and SQL, among database systems, for example, are the most in-demand technical skills that data engineers are expected to be equipped with. They ought to be knowledgeable about manipulating database management systems (DBMS) to store and retrieve data.
Programming languages – The mandatory skills of data engineers are being familiar with programming languages, including Scala, Java, and Python.
- Scala – It’s a Java extension that goes hand in hand with Java
- Java – Widely adopted in data architecture frameworks
- Python – For statistical analysis and ETL assignments Python is the most useful and well-known language.
Scripting and Automation – Since data engineers ought to manage huge volumes of information there might be recurring tasks. Therefore, writing scripts for the automation of recurring tasks is a skill that is in demand as well.
Big Data – Big data means massive volumes of raw as well as structured data. Due to big data being an essential tool for AI and data science teams, data engineers must be equipped with the know-how of working with big data in terms of storing, processing, cleaning, and extracting relevant data.
Algorithms and Data structures– Algorithms and Data structures enable arranging and storing information for quick access and tweaking, which is an essential skill that data engineers ought to be equipped with.
Kafka – An open-source processing software platform Kafka is equipped to manage data feeds in real-time. Data engineers oftentimes use Kafka and Hadoop to complement each other for processing in real-time, reporting, and monitoring data.
Apache Hadoop – An open-source framework, Apache Hadoop is a repository of tools supporting the integration of data. Hadoop enables the analysis and storing of massive portions of data and is an important aspect of data engineering for seamless functioning.
Amazon Web Services (AWS) – AWS is in use in data engineering for designing and automating the data flows for ensuring scalability, innovation, and agility.
Data Engineering SIMPLIFIED! | Skills required in 2022 to become a Data Engineer
This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.
© 2022 Avik Chakravorty