Engineering refers to designing and building things. Similarly, data engineering refers to designing and building pipelines to transform data into a usable format. The term surfaced around 2011, especially within data-driven enterprises like Airbnb and Facebook. Such companies house vast amounts of potentially valuable real-time data. Earlier, software engineers used to develop appropriate tools to handle this data. The requirement soon evolved into data engineering skills for handling big data.
In the last ten years, the majority of companies have transformed digitally. Digitization has resulted in unprecedented volumes of a variety of data types that is much more complex. While data scientists are required to work with this data, there is a need for people who would organize and complete it, ensuring its quality is preserved. This is where data engineers step in and organize the data so that data scientists or other users can utilize it for further analysis. These data pipelines collect data from multiple sources and represent it as a single source of information.
The process may sound easy, but it requires many data literacy skills to engineer data. This article discusses some of the top data engineering skills necessary if you want to upscale your data career.
Top Data Engineering Skills
Here is a list of some highly appreciated data engineering skills. Have a look and master them to advance your career as a data engineer.
- Machine learning and AI
Machine learning and artificial intelligence have become one of the most prominent technologies over the past few years. These technologies use specific algorithms to predict future states based on the input data. Data engineers need prior knowledge of these algorithms to work and organize data based on the company’s requirements. Additionally, a robust foundation in mathematics and statistics will give you an edge in understanding how these algorithms work.
Data engineers are simply software engineers who specialize in data pipelines and workflows. Consequently, machine learning becomes a must-have skill because it aids you in building better pipelines and models, making it one of the most necessary data engineering skills.
- Data Engineering Skills for structures and algorithms (DSA)
Data engineers are responsible for organizing and handling unstructured data and transforming it into a usable format. This data must be stored somewhere accessible so that it can be used conveniently. Data structures are simple storage units that come in handy for the same. Not just for storing, data in these structures are also readily available for processing and cleaning. Secondly, DSA goes through several common problems and tells you how efficient they are. Although data engineers are mainly responsible for organizing and filtering data, they should know how to see if an algorithm is working efficiently.
- Database technology – SQL and NoSQL
As a data engineer, you cannot find your way around SQL-based schemas and their syntax, given SQL is a widely used language. Many cloud-based systems like Amazon QuickSight and Athena are compatible with SQL-like interfaces. Additionally, NoSQL database technologies have gained popularity over the last few years, especially for storing unstructured or semi-structured data. These databases store data in key-value pairs and object formats like JSON or Parquet. A basic understanding of manipulating these key-value pairs is necessary if your data is stored in open-source ecosystems like Hadoop, MongoDB, or Cassandra (NoSQL frameworks). Data engineers should know both to have a holistic view of database technologies and be remarkable at showcasing their data engineering skills.
- Scripting in Programming languages
You must be an efficient developer if you wish to be a data engineer. Almost all data engineer positions require you to know programming languages like Python. As a data engineer, you will also have to write scripts and glue codes because everything nowadays involves coding. There is infrastructure as code, pipelines as code, etc. Hence, having a robust programming background and interest in finding data patterns is vital. In addition to scripting, a data engineer must have an “operations mindset” to ensure that your infrastructure is reliable. Having some DevOps experience as a part of data engineering skills will help here.
- Hyper Automation
Hyper Automation is one of the very obvious data engineering skills and refers to a business-driven approach to automating as much as possible. The process incorporates several technologies, tools, and platforms to perform value-added tasks like scheduling events, running jobs, etc. Hyper Automation has grown over the last few years in tandem with data pipelines and specialized scripting for moving data into the cloud. It leads to enhanced work quality, faster business processes, and decision-making agility. A data engineer should, thus, know how hyper-automation works.
- Data Visualization as One of the Data Engineering Skills
Data engineers are often expected to perform exploratory data analysis (EDA) while working with data to ensure that the required ETL/ELT task is completed. EDA analyzes specific trends, patterns, or graphical representations of the data. A data engineer should know how to visually present and analyze data using statistical knowledge with tools like SSRS, Tableau, Azure Synapse, Excel, Power BI, etc. visualization also ensures that the data quality is maintained as data engineers process it. Therefore, it is an essential skill if you want to proceed in data engineering.
- Multi-cloud computing
The model is called multi-cloud computing when an enterprise utilizes many cloud combinations simultaneously. It could be two or more public or private clouds or a combination of public, private, and edge-based clouds. Shifting to a multi-cloud model enables companies to avail themselves of better data security features and cost savings simultaneously. Given its rising trends, data engineers are expected to understand the requisite technologies that go into cloud computing. Besides having other data engineering skills, they are also expected to have experience with Iaas (infrastructure-as-a-service), PaaS (platform-as-a-service), and SaaS (software-as-a-service).
- Data APIs
Data engineers must have experience with application programming interfaces (APIs) in addition to standard data engineering skills because part of their job is to build APIs (application user interfaces) in databases to enable data analysts and scientists to send queries. These interfaces share data infrastructure for real-time analysis. Furthermore, data engineers must be proficient in programming languages like Scala or Python to create APIs and facilitate other data engineering tasks.
- Understanding ETL Tools as Data Engineering Skills
ETL stands for extract, transform, and load, and the term describes the entire process of extracting data from a source system, transforming it into a required format, and loading it to the desired location. Since data engineers are responsible for organizing and processing data, it is relevant to know ETL tools. When handling vast amounts of data, engineers perform batch processing and ensure that the data remains relevant to the specific requirement. Many ETL tools, like Fivetran, Hadoop, IBM DataStage, Hevo, Talend, etc., are very efficient in batch processing. A data engineer should be able to work through these tools while organizing data.
- Apache Hadoop
Apache Hadoop is an open-source library that enables distributed procession of large datasets. It is a cluster of frameworks that support data integration, making it suitable for big data analytics. Hadoop is a popular framework as it is compatible with gigabytes to petabytes of data by strategic clustering. Data engineers who are acquainted with such frameworks (Hadoop, Kafka, etc.) would have the edge over others in terms of better efficiency for real-time data processing, monitoring, and reporting, making it one of the most necessary data engineering skills.