To secure data engineering jobs, it is essential to have Big Data skills. Data Engineering Professionals should know about how to build data infrastructure, databases, containers as well as hands-on experience in various tools like Hadoop, Scala, SAS, SPSS, R, and so on.
Here are some must-have skills a Data Engineer should possess to thrive in their career.
- Understanding of Database Tools
A deep understanding of database architecture and design is important for a job role in data engineering since they are dealing with the storage, organization, and management of a huge volume of data. Understand that the structured query language (SQL) based databases like MySQL are used for the storage of structured data and NoSQL technologies like MongoDB can store structured, unstructured, and semi-structured data.
- Tools for Data Transformation
The raw Big Data has to be converted into a consumable format which can be complex or simple based on the source, format, and output we require. Some of the major tools used for this purpose are Matillion, InfoSphere DataStage, Hevo Data, Talend, and so on.
- Tools for Data Ingestion
Data ingestion can be considered as one of the crucial parts of big data skills. It comprises of moving data from multiple sources to destinations to analyze. The common ingestion tools are Apache Storm, Wavefront, Apache Kafka, Apache Flume, and so on.
- Tools for Data Mining
Extraction of vital information for finding patterns and preparing it for analysis is dealt with by data mining. It also aids in predictions and data classification. Apache Mahout, Weka, KNIME, and so on are some of the data mining tools.
- Tools for Data Warehousing and ETL
Extract Transform Load or ETL fetches data from several sources, converts the same for analysis, and later loads it to the data warehouse. Some of the common ETL tools are Stitch, AWS Glue, Talend, and so on.
- Real-time Processing Frameworks
It is crucial to process the data in real-time and derives quick insight from it for further actions. Apache Spark, Hadoop, and Flink are some of the real-time processing frameworks.
- Tools for Data Buffering
An area that temporarily stores data when it is moved from one place to another is a data buffer. Kinesis, GCP Pub/Sub, Redis Cache, etc. are some of the commonly used data buffering tools.
- Tools for Cloud Computing
One of the major tasks of big data teams is to set up storage of data in cloud for the high availability. Companies can make use of cloud platforms like AWS, Azure, OpenStack, and so on depending on the storage requirement of data.
- Machine Learning Skills
Machine learning integration into the processing of big data can help in uncovering patterns and trends. Strong understanding of statistics and mathematics, knowledge of tools like R, SPSS, SAS, etc can help in enhancing machine learning skills
- Data Visualization Skills
Visualization tools like Tableau, Plotly, Qlik, etc. can help in presenting the learnings and insights for end-users in an understandable format. Data engineering professionals have to work with these data visualization tools most of the time.
Always the best way in learning these skills are certifications, hands-on practice, and integrating the same into real-life use cases.