10 Must-Have Skills for Data Engineering Jobs
by Analytics Insight
June 7, 2021
Big Data skills are crucial to landing data engineering positions. From designing, creating, building, and maintaining data pipelines to collecting raw data from a variety of sources and optimizing performance, data engineering professionals perform a plethora of tasks. They are expected to know about large data frameworks, databases, building data infrastructure, containers, etc. It’s also important that they have hands-on exposure to tools like Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and so on. to name a few.
Here, we list some of the important skills that one must have to build a successful career in big data.
1. Database tools
Storing, organizing and managing huge volumes of data is essential for data engineering roles, and therefore a thorough understanding of database design and architecture is crucial. The two commonly used types of databases are based on Structural Query Language (SQL) and NoSQL. 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 and unstructured data. structured according to the requirements of the application.
2. Data transformation tools
Big Data is present in raw format and cannot be used directly. It should be converted to a consumable format depending 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, etc.
3. Data ingestion tools
Data ingestion is one of the essential parts of big data skills and involves moving data from one or more sources to a destination where it could be analyzed. As the quantity and formats of data increase, data ingestion becomes more complex, requiring professionals to know the data ingestion tools and APIs to prioritize data sources, validate them, and distribute data for ensure an efficient ingestion process. Some of the data ingestion tools you should know about are Apache Kafka, Apache Storm, Apache Flume, Apache Sqoop, Wavefront, etc.
4. Data mining tools
Another important skill for managing big data is data mining, which involves extracting vital information to find patterns in large datasets and prepare them for analysis. Data mining helps with data classification and predictions. Some of the data mining tools that big data professionals should master are Apache Mahout, KNIME, Rapid Miner, Weka, etc.
5. Data warehousing and ETL tools
Data Warehouse and ETL help businesses leverage big data in meaningful ways. It streamlines data 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, etc.
6. Real-time processing frames
Processing the generated data in real time is essential to generate rapid information on which to act. Apache Spark is most often used as a distributed real-time processing framework to perform data processing. Some of the other frameworks you should know about are Hadoop, Apache Storm, Flink, etc.
7. Data buffering tools
With the increase in data volumes, data buffering has become a critical driver for speeding up data processing power. 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.
8. Machine learning skills
Incorporating machine learning into big data processing can speed up the process by uncovering trends and patterns. The use of machine learning algorithms can categorize incoming data, recognize patterns, and translate the data into information. Understanding machine learning requires a solid foundation in math and statistics. Knowledge of tools such as SAS, SPSS, R, etc. can help develop these skills.
9. Cloud computing tools
Cloud implementation storing and ensuring 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 cloud infrastructure, public or internal depending on data storage requirements. Some of the popular cloud platforms you should know about are AWS, Azure, GCP, OpenStack, Openshift, etc.
10. Data visualization skills
Big Data professionals work with visualization tools inside and out. There is a need to present the ideas and learnings generated in a consumable format for end users. Some of the commonly used visualization tools that can be learned are Tableau, Qlik, Tibco Spotfire, Plotly, etc.
The best way to acquire these data engineering skills is to obtain certifications and gain hands-on practice by exploring new datasets and integrating them into real-world use cases. Good luck learning them!
Srishti is Content Marketing Manager at Sigmoid with a background in tech journalism. She has covered the data science and AI space extensively in the past and is passionate about the technologies that define them.