10 must-have skills for Data Engineering jobs
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In today’s fast-paced business landscape, the ability to efficiently design, build, and manage data pipelines is crucial for enterprises aiming to extract valuable insights and make data-driven decisions. Due to its instrumental role in transforming raw data into actionable intelligence, Data Engineering has emerged as a high demand job. According to Glassdoor, the average salary of a Data Engineer in the US is $1,15,406 per year. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, and Pig to name a few.
From data transformation to machine learning integration, in this blog we’ll explore the tools and techniques that encompass the essential data engineer skill set required for a successful career. Here is a list of the important skills for data engineers that one should possess to build a successful career in big data.
- Database tools
Storing, organizing, and managing huge data volumes is critical for data engineering job roles, and therefore a deep understanding of database design & architecture is crucial. The two types of databases commonly used are structure query language (SQL) based, and NoSQL-based. While SQL-based databases such as MySQL and PL/SQL are used to store structured data, NoSQL technologies such as Cassandra, MongoDB, and others can store large volumes of structured, semi-structured & unstructured data as per application requirements.
- Data transformation tools
Big data is present in raw format and cannot be used directly. It needs to be converted to a consumable format based on the use case to process it. Data transformation can be simple or complex depending on the data sources, formats, and required output. Some of the data transformation tools are Hevo Data, Matillion, Talend, Pentaho Data Integration, InfoSphere DataStage, and more.
- Data ingestion tools
Data ingestion is one of the essential parts of big data skills and is the process of moving data from one or more sources to a destination where it could be analyzed. As the amount and formats of data increase, data ingestion becomes more complex, requiring the professionals to know data ingestion tools and APIs to prioritize data sources, validate them, and dispatch data to ensure an effective ingestion process. Some of the data ingestion tools to know are Apache Kafka, Apache Storm, Apache Flume, Apache Sqoop, Wavefront, and more.
- Data mining tools
Another important skill to handle big data is data mining which involves extracting vital information to find patterns in large data sets and prepare them for analysis. Data mining helps in carrying out data classification and predictions. Some of the data mining tools that big data professionals must have hands-on are Apache Mahout, KNIME, Rapid Miner, Weka, and more.
- Data warehousing and ETL tools
Data warehouse and ETL help companies leverage big data in a meaningful manner. It streamlines data that comes from heterogeneous sources. ETL or Extract Transform Load takes data from multiple sources, converts it for analysis, and loads that data into the warehouse. Some of the popular ETL tools are Talend, Informatica PowerCenter, AWS Glue, Stitch, and more.
- Real-time processing frameworks
Processing the data generated in real-time is essential to generate quick insights to act upon. Apache Spark is most popularly used as a distributed real-time processing framework to carry data processing. Some of the other frameworks to know are Hadoop, Apache Storm, Flink, and more.
- Data buffering tools
With increasing data volumes, data buffering has become a crucial driver to speed up the processing power of data. Essentially, a data buffer is an area that temporarily stores data while moving from one place to another. Data buffering becomes important in cases where streaming data is continuously generated from thousands of data sources. Commonly used tools for data buffering are Kinesis, Redis Cache, GCP Pub/Sub, etc.
- Machine Learning skills
Integrating machine learning into big data processing can accelerate the process by uncovering trends and patterns. Using machine learning algorithms can categorize the incoming data, recognize patterns and translate data into insights. Understanding machine learning requires a strong foundation in mathematics and statistics. Knowledge of tools such as SAS, SPSS, R, etc. can help in developing these skills.
- Cloud computing tools
Setting up the cloud to store and ensure the high availability of data is one of the key tasks of big data teams. It, therefore, becomes an essential skill to acquire while working with big data. Companies work with hybrid, public or in-house cloud infrastructure based on the data storage requirements. Some of the popular cloud platforms to know are AWS, Azure, GCP, OpenStack, Openshift, and more.
- Data visualization skills
Big data professionals work with visualization tools in and out. It is required to present the insights and learnings generated in a consumable format for the end-users. Some of the popularly used visualization tools that can be learned are Tableau, Qlik, Tibco Spotfire, Plotly, and more.
The best way to learn these data engineering skills is to get certifications and get hands-on practice by exploring new data sets and integrating them into real-life use cases. Good luck learning them!
About the Author
Srishti is Content Marketing Manager at Sigmoid with a background in tech journalism. She has extensively covered Data Science and AI space in the past and is passionate about technologies defining them.
