Want to pursue a career in data engineering but don’t know where to start? Then you’ve come to the right place. This article will tell you about the most important data engineering skills including the tech skills and the programs you should be familiar with.
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Tech Skills for Data Engineering
1. Data Warehousing
Data warehouses enable you to store large amounts of data for query and analysis. The data can come from multiple sources such as ERP software, accounting software, or a CRM solution. Organizations use this data to generate reports, perform analytics, and data mining to generate valuable insights.
You must be familiar with the basic concept of data warehousing and the tools related to this field, Amazon Web Services, and Microsoft Azure. Data warehousing is among the fundamental skills required for data engineering professionals.
2. Machine Learning
As a data engineer, you only need to be familiar with the basics of machine learning and its algorithms. Being familiar with machine learning will help you in understanding your organization’s requirements and collaborate with the data scientist more efficiently. Apart from these benefits, learning about machine learning will help you in building better data pipelines and produce better models.
3. Data Structures
Although a data engineer usually performs data optimization and filtering, it would benefit you to know about the basics of data structures. It would assist you in understanding the various aspects of your organization’s goals and help you to cooperate well with other teams and members.
4. ETL Tools
ETL stands for Extract, Transfer, Load, and denotes how you extract data from a source, transform it into a format, and store it into a data warehouse. ETL uses batch processing to ensure users can analyze relevant data according to their specific business problems.
It gets data from multiple sources, applies particular rules to the same, and then loads the data into a database where anyone in the organization can use or view it. As you may have realized, ETL tools are among the most important skills for data engineering professionals.
5. Programming Languages (Python, Scala, Java)
Python, Java, and Scala are some of the most popular programming languages. Python is a must-have for a data engineer as it helps you perform statistical analysis and modelling. On the other hand, Java helps you work with data architecture frameworks and Scala is simply an extension of the same.
You should note that nearly 70% of job descriptions for this field require Python as a skill. As a data engineer, you must have strong coding skills as you’d need to work with multiple programming languages. Apart from Python, other popular programming skills include .NET, R, Shell Scripting, and Perl.
Java and Scala are vital as they let you work with MapReduce, a vital Hadoop component. Similarly, Python helps you in performing data analysis. You must master at least one of these programming languages.
Another language to watch out for is C++. It can compute vast amounts of data in the absence of a predefined algorithm. Moreover, it’s the only programming language that lets you more than one GB of data within a second. Apart from these advantages, C++ lets you apply predictive analytics in real-time and retrain the algorithm. It’s among the most important skills required for data engineers.
6. Distributed Systems
Distributed systems have become widely popular as they reduce storage and operation costs for organizations. They let organizations store large amounts of data in a distributed network of smaller storages. Before the arrival of distributed systems, the cost of data storage and analysis was quite high as organizations had to invest in larger storage solutions.
Now, distributed systems such as Apache Hadoop are very popular and a data engineer needs to be familiar with them. You should know how a distributed system works and how you can use the same. Apart from the distributed system, you should know how to process information through the same.
Apache Hadoop is a widely popular distributed framework while Apache Spark is a programming tool for processing large amounts of data. You should be familiar with both of them as they are among the vital skills for data engineering professionals.
Frameworks for Data Engineering
1. Apache Hadoop
Apache Hadoop is an open-source framework that lets you store and manage Big Data applications. These applications run within-cluster systems and Hadoop helps you manage the same. One of the most important data engineering skills is to create Hadoop applications and manage the same effectively. Since its arrival in 2006, Hadoop has become one of the must-haves for any data professional. It has a wide collection of tools that make data implementations easier and effective.
Hadoop lets you perform distributed processing of large datasets by using simple programming implementations. You can use R, Python, Java, and Scala with this tool. This framework makes it affordable for companies to store and process large amounts of data as it lets them perform the tasks through a distributed network. Apache Hadoop is an industry staple and you should be well-acquainted with it.
2. Apache Spark
Apache Spark is another must-have tool you must be familiar with if you want to become a data engineer. Spark is an open-source distributed general-purpose framework for cluster computing. It offers an interface that lets you program clusters with fault tolerance and data parallelism. Spark uses in-memory caching and optimized query implementation to process queries quickly against any data size. It’s an essential tool for large-scale data processing.
Apart from its capabilities of processing large amounts of data quickly, it is compatible with Apache Hadoop, making it quite a useful tool. Apache Spark lets you perform steam processing which has constant data input and output. Spark is more efficient than Hadoop which is why it has become such a popular tool for data engineers.
AWS stands for Amazon Web Service and it’s the most popular tool for data warehousing. A data warehouse is a relational database focused on analysis and query to help you get a long-range view of the data. Data warehouses are the primary repositories of integrated data from one (or multiple) sources.
As a data engineer, you’ll have to work with a lot of data warehouses so it’s necessary to be familiar with the various data warehousing applications. AWS and Redshift are the two tools you must be acquainted with as most data warehouses are based on these two.
AWS is a cloud-based platform that lets you access your data engineering tools as well, so learning it will certainly help you with other tools. Almost every data engineering job description requires you to be familiar with AWS.
Azure is a cloud-based technology that can help you with building large-scale analytics solutions. Like AWS, it’s a must-have for any data engineer. Azure automates the support of applications and servers with a packaged analytics system. Primarily, Azure is popular for building, deploying, testing, and managing services and applications through data centres. It has various solutions available as Iaas (Infrastructure as a Service), SaaS (Software as a Service), and PaaS (Platform as a Service).
Azure helps you set up Windows-based server applications quickly and efficiently. As Windows is widely popular, the demand for this tool is quite high.
5. Amazon S3 and HDFS
Amazon S3 (Amazon Simple Storage Service) is a part of AWS which offers you a scalable storage infrastructure. HDFS is the Hadoop Distributed File System and is a distributed storage system for Apache Hadoop. Both of these tools let you store and scale easily.
With the help of these two solutions, an organization can store virtually an unlimited quantity of data. Moreover, it offers cloud-based storage so you can access the data from anywhere and work on it. These solutions are popular for offering storage to mobile applications, IoT applications, enterprise applications, websites, and many others.
6. SQL and NoSQL
SQL and NoSQL are must-haves for any data engineer. SQL is the primary programming language for managing and creating relational database systems. Relational database systems are tables that contain rows and columns and are widely popular. On the other hand, NoSQL databases are non-tabular and are of various kinds according to the data model. Common examples of NoSQL databases are documents and graphs.
You should know how to work with Database Management Systems (DBMS) and for that, you’d need to be familiar with SQL and NoSQL. Some additional SQL skills include MongoDB, Cassandra, Big Query, and Hive. By learning about SQL and NoSQL, you can work with all kinds of database systems.
How to Learn The Skills Required for Data Engineering?
As you can see, data engineering is quite an advanced field and requires learning a lot of skills. Learning all of these skills can be quite challenging and cumbersome. The best way to learn the various data engineering skills we discussed, you can check out upGrad’s data engineering course.
A course will help you get a structured and streamlined learning experience. Our data engineering course lets you learn from industry mentors who ensure you can get rid of your doubts quickly. The course will provide you with industry projects so you can test out your skills and see how far you’ve come.
Projects can be an excellent way to measure your progress and learn the applications of your skills. Our course comes with job placement assistance and learning support so you don’t face any issues.
If you’re interested in pursuing a career in data engineering, you should learn all the skills we listed in this article. They are the fundamental skills required for data engineering professionals.
We hope that you found our article on data engineering skills useful. If you have any questions or suggestions regarding this article, do let us know through the comment section below. We’d be happy to help you!