The key to understanding data engineering is in the “Engineering” section. Engineers design and build objects. “Data” engineers design pipelines so that data can be converted and transported to a format that is most useful until it reaches data scientists or other end users. These pipelines must take data from many different sources and integrate them into one warehouse that represents the data equally as a single source of truth.
How did data engineering come about?
The term “data engineering” originated to describe a role that has shifted away from traditional ETL tools and developed its tools to handle growing data. As big data grew, “data engineering” grew up as that branch of software engineering that focused in-depth on data infrastructure, warehousing, mining, modeling, and metadata management.
Relation and point of difference between data scientists and data engineers
It is now widely recognized that companies in the Advanced Analytics team need data scientists and data engineers. There is often collaboration between data engineers and data scientists; however, the preferred skills and knowledge of the devices are different. Data scientists focus on the advanced analysis of data generated and stored in the company database.
What are the skills incorporated in Data Engineering?
Some important areas of skill are:
- Foundation Software Engineering:Agile, DevOps, Architecture Design, Service-Oriented Architecture.
- Open Framework:Apache Spark, Hadoop, Probably Hive, Mapreduce, Kafka etc.
- Programming:Python has become the preferred language.
- Pandas:It is meant for cleaning and modifying data.
- Cloud Platforms, Analytics and Data Modeling:Data modeling knowledge is important because data engineers need to design table partitions, generalize and denormalize data in the warehouse, and feature specific content.
Data engineers design, manage and optimize data flow across those databases throughout the organization. Data engineers are highly proficient in mathematics and statistics, algorithms and machine learning techniques. Data engineers are very well aware of SQL, MySQL, NoSQL, architecture and cloud technology, and frameworks such as Agile and Scrum. Both may be familiar with Python visualization techniques and other coding languages