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.

In Netenrich

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 cybersecurity realm, data engineering has become increasingly important for modern organizations and is critical for making sense of security telemetry from diverse sources and formats (end points, servers, clouds, applications, and more). Without it, the sheer volume of data generated daily can overwhelm internal teams and hinder business progress.

By proactively engineering data at ingestion, Netenrich Adaptive MDR™, powered by Resolution Intelligence Cloud™, contextualizes and correlates data to derive smarter, customized analyses. With this contextual understanding, organizations can differentiate between benign and malicious activities and thus, improve threat detection and response capabilities and make faster, more informed decisions.