Ep 1: What Is Data Engineering, and Why It Matters More Than Ever

Data engineering has become one of the most talked about terms in technology, yet ask ten people what it means and the answers rarely agree. In the opening episode of Data Engineering Unplugged, Geopits brings together host Saranya and Thiru to unpack what data engineering really involves, moving past the assumption that it is simply ETL or a byproduct of tools like Databricks or Snowflake.

Thiru explains data engineering as the discipline of building systems that collect, transform, and deliver data in a form businesses can actually use. The conversation traces how reporting once ran directly on transactional databases such as CRM and ERP systems, and why that approach breaks down at scale, since transactional and analytical workloads behave very differently under load.

The episode also breaks data engineering into its core layers, starting with data ingestion, the process of pulling structured, semi-structured, and unstructured data from multiple sources into a single layer that downstream systems can rely on.

Whether the audience is a DBA exploring a shift into data engineering or a business leader trying to make sense of the term, this episode lays the groundwork for the rest of the series.

• What data engineering actually means, beyond ETL and specific tools
• Why reporting directly on transactional databases stops scaling as businesses grow
• The difference between transactional and analytical system design, and why combining them fails at scale
• The role of the data ingestion layer in bringing together structured, semi-structured, and unstructured data
• How data engineering prepares data for both analytics and AI use cases
• How DBAs can build on their existing skills to move into data engineering roles

Data engineering is not a single tool or process. It is the foundation that makes data reliable, scalable, and ready for analytics and AI. Organisations that treat it as a dedicated discipline, rather than an extension of transactional database management, are better positioned to scale their data initiatives over the long term.