Data engineers have to deal with a range of complex tasks and they need significant technical skills to do their job. Having said that, it’s not easy to create a comprehensive and detailed list of knowledge and skills required to achieve success in data engineering jobs as the whole field is evolving at a rapid pace with new systems and technologies appearing constantly. These constant changes also mean that data engineers need to keep learning to remain knowledgeable about the latest breakthroughs. With this background, here’s what should be a part of any data engineer’s repertoire
Database management — A large part of the day-to-day working of a data engineer requires dealing with databases. It could be part cleaning, transporting, storing, collecting or consulting data. This is why these professionals need to have a good grip on database management. They need to be fluent in Structured Query Language. It is the language that is used to interact with databases.
They should also have the necessary expertise to interact with some of the most popular database types including SQL Server, MySQL, and PostgreSQL. While these are some examples of relational databases, the data engineers are also required to have experience with Not Only SQL(NoSQL) databases as these databases are quickly gaining popularity in Big Data and real-time applications. Even though the types of NoSQL engines are increasing, data engineers need to be aware of the difference between different database types and applications of each type. If you want to learn about NoSQL and its differences from SQL, check out our NoSQL concepts course.
Programming languages — Data engineers need to have coding skills which is the case with various other roles in data science. These professionals also deal with various other programming languages to do their job. A variety of programming languages are used in data engineering but Python remains the most popular option.
It won’t be wrong to label it as the lingua franca when it comes to data science. It is the perfect programming language for writing data pipelines and executing ETL jobs. Also, it integrates with many popular frameworks and tools used in data engineering including Apache Spark and Apache Airflow. Several of these open-source frameworks can be run on Java Virtual Machine. You need to learn Java or Scala in case your company uses these frameworks.
Distributed computing frameworks — Distributed systems have gained a lot of popularity in data science in recent years. It’s a computing environment where components are part of a network which is also known as a cluster. The work is distributed across the computers that are part of a cluster and the efforts are coordinated to do the work more efficiently.
Popular distributed computing frameworks including Apache Spark and Apache Hadoop are capable of processing a huge amount of data and they are used as the foundation for some really interesting applications of Big Data. If you are an aspiring data engineer, you need to gain expertise in at least one of these frameworks.
Cloud computing — It is one of the most popular topics in data science. There is a lot of demand for cloud-based solutions. These days, a data engineer is expected to connect the business systems of the company to cloud-based solutions. The rise of Google Cloud, Azure and Amazon Web services has made sure that almost all the work can be done within the cloud. This is why a good data engineer needs to know how to use cloud services as well as their advantages and limitations in Big Data projects. As a data engineer, you are expected to be familiar with Azure and AWS platforms.
ETL frameworks — One of the main jobs of a data engineer is to build data pipelines with ETL technologies and orchestration frameworks. A wide range of technologies are used in the space but a budding data engineer needs to be familiar with some of the most popular frameworks including Apache NiFi and Apache AirFlow. Apache AirFlow is an open source orchestration framework for generating, planning, and tracking data pipelines. The Apache NiFi is the perfect choice for a basic and repeatable big data ETL process.
Stream processing frameworks — There are many data science applications but the most innovative ones make use of real-time data. This is why there is a huge demand for engineers who are familiar with stream processing frameworks. Data engineers who want to take their career to the next level should learn how to use various streaming processing tools including Spark streaming, Kafka streams, or Flink.
Shell — Almost all the routines and jobs in the cloud and various other big data tools and frameworks make use of shell commands and scripts. Any data engineer worth their salt should be comfortable using the terminal to navigate the system, run commands and edit files.
Good communication skills — In addition to technical proficiency, data engineers should also develop good communication skills to work with various departments to understand the requirements of data scientists and data analysts along with business leaders. Professionals in this field may be called upon to develop reports, dashboards as well as various other visualizations to communicate with stakeholders.