What is Data Engineering?
Data engineering is the process of designing, building, managing, and optimizing the infrastructure that enable the collection, storage, and processing of large volumes of data. This infrastructure can include databases, Big Data repositories, storage systems, cloud platforms, and data pipelines. Data engineering is an essential component of the data lifecycle and plays a crucial role in ensuring that data is reliable, accessible, and ready for analysis. Data engineering involves tasks such as data ingestion, data transformation, data integration, and data quality management.
A data engineer is the “plumber” of data, making sure data is highly available, consistent, secure, and recoverable. In other words, the data can get to the people who need it when they need it. For example, they make the data available “upstream” to data analysts and data scientists. A data analyst will analyze data to derive and report on insights and a data scientist will perform even deep analysis on data in order to develop predictive models that can solve more complex data problems.
At Netenrich, we approach everything from the perspective of data. In general, data engineering means building systems to enable the collection and usage of data. In the context of the threat hunting and cybersecurity — and the services Netenrich offers and helps facilitate with our Resolution Intelligence Cloud™ platform — data engineering is about building systems to make sense of security telemetry that comes from many diverse sources in multiple formats (end points, servers, clouds, applications, etc.); information that provides necessary context as to whether detected activity is malicious or not; and external threat intelligence about threats in the wild.