How to Develop Your Data Engineering Skills and Become a Professional
The data engineer role is gaining more and more traction within the data science ecosystem. DICE’s 2020 Tech Job Report revealed that Data Engineer was the fastest-growing tech career. Additionally, the job was ranked 15th among the most outstanding emerging jobs in the LinkedIn Emerging Jobs Report of 2020, with an increase in hiring of 35% since 2015.
Have you considered becoming a data engineer? We’re here to help. Our goal in this blog is to explain what a data engineer does and why it is such a great career path today. Also, we will discuss the skills and qualifications typically required of data engineers.
Data Engineering — 101
An organization’s data engineers lay the foundation for acquiring, storing, transforming, and managing data. Their responsibilities include designing, creating, and maintaining database architectures and data processing systems, developing machine learning models, analysis, visualization, and continuous, seamless, secure, and effective data processing.
In other words, the role of data engineers in data science is to bridge the gap between traditional data science positions and software and application developers.
Traditional data science workflows begin with the collection and storage of data, which is the responsibility of data engineers. Other data science specialists, such as data analysts and scientists, can use large volumes of data collected from many sources.
On the one hand, this requires creating and maintaining scalable data infrastructures that are highly available, performant, and capable of integrating new technologies. A data engineer must also monitor the status and movement of data through these systems.
Skills Required to Become a Professional Data Engineer
To address their highly complex tasks, data engineers need a wide range of technical skills. It isn’t easy to compile a complete list of skills and knowledge required for success in a data engineering role since the data science ecosystem constantly evolves.
Therefore, data engineers must constantly learn to stay on top of technological advancements. That said, here are some skills that any data engineer would benefit from.
Data engineers spend a significant portion of their day collecting, storing, transferring, cleaning, or consulting databases. Therefore, a good understanding of database management is essential for data engineers.
To accomplish this, you need to be fluent in SQL (Structured Query Language), the primary language for interacting with databases, and you must have expertise in MySQL, SQL Server, and PostgreSQL, which are among the most popular SQL dialects.
Besides relational databases, data engineers need to know about NoSQL (“Not only SQL”) databases, which are being adopted rapidly for Big Data and real-time applications. Data engineers are therefore recommended to at least understand the different types of NoSQL databases and their use cases.
Like other data science roles, data engineers must be proficient in coding. A wide range of tasks can be performed by data engineers using different programming languages besides SQL. Python is undoubtedly one of the best programming languages for data engineering.
Performing ETL jobs and writing data pipelines is easy with Python, a lingua franca in data science. In addition to its excellent integration with data engineering tools, Python enables easy access to Apache Airflow and Spark frameworks.
Java Virtual Machine is a popular platform for running these open-source frameworks, so Scala and Java are other programming languages you might like to learn.
Mastery of Distributed Systems
Data science has increasingly relied on distributed computing frameworks in recent years. These computing environments use multiple computers (also called clusters) on a network to distribute various components.
A distributed system works by splitting the workload across the cluster and coordinating the efforts to get the job done as quickly and efficiently as possible. Some of the most impressive Big Data applications are based on distributed computing frameworks, such as Apache Hadoop and Apache Spark.
Anyone aspiring to work in data engineering must be familiar with one of these frameworks. If you want to learn more about data engineering Companies, check out this business listing.
Familiarity With Cloud Computing
Data science is increasingly focused on cloud computing. There has been a rapid shift towards cloud-based solutions as the demand for them grows. Nowadays, one of the primary responsibilities of a data engineer is connecting a company’s business systems to the cloud.
In today’s cloud-based world, everything from the data supply chain to data processing can be done within the Cloud, with services such as Google Cloud, Azure, and Amazon Web Services (AWS).
To be an effective data engineer, one needs to understand cloud services, their advantages and disadvantages, and how they can be applied to Big Data projects. Most people are familiar with AWS and Azure because they are the most widely used platforms.
Using ETL Technologies to Create Data Pipelines
Creating data pipelines with ETL technologies and orchestration frameworks is one of the primary duties of data engineers. Although many technologies can be listed in this section, the data engineer should at least be familiar with two of the most well-known: Apache NiFi and Airflow.
The Airflow framework enables data engineers to plan, generate, and track data pipelines. It is effectively an orchestration tool. When it comes to a basic, repeatable ETL process for big data, NiFi is the perfect solution.
Stream Processing of Real-Time Data
Data science applications using real-time data are among the most innovative. Consequently, candidates familiar with stream processing frameworks are in high demand. Streaming processing tools such as Kafka Streams, Flink, or Spark Streaming are excellent options for data engineers keen on advancing their careers.
Script and Shell Command Knowledge
Most tasks and routines in the Cloud and other Big Data frameworks and tools are executed with shell commands and scripts. Data engineers must be comfortable using the terminal to navigate the system, run commands, and edit files.
Lastly, data engineers must possess communication skills to work across departmental boundaries and to understand the needs of business leaders, data analysts, and data scientists. It may be necessary for data engineers to develop dashboards, reports, and other visuals to communicate with stakeholders in their particular organizations.
Aspiring data professionals can choose from many exciting career paths in data science, including data engineering. If you’re determined to become a data engineer but aren’t sure where to begin, this article has hopefully given you some idea about the specific practical knowledge you need to succeed.