Tracing the origin of declarative data processing from SQL to Databricks DLT and dbt, exploring current tools, and looking ahead to the future of fully declarative data platforms.

A deep dive into the semantic layer in data architecture—what it is, who conceptualized it, whether it’s logical or implemented, how it’s used today, and how companies like Airbnb, Databricks, dbt,...

Understanding how data version control and data time travel differ, their features, and the tools that enable them in modern data architectures.

A deep dive into modern database architectures, from relational systems to open table formats, and how to choose the right one.

Just as Iceberg, Delta, and Hudi brought standardization to data lakes, the next frontier may be a true open standard for data quality, observability, and governance.

A deep dive into how leading tech companies evolved their data architectures, why those changes happened, and what trade-offs they faced.

Building on our last discussion, here we dive into the exhaustive KPIs and metrics needed to measure and monitor data health across quality, observability, and governance.

A deep dive into why multiple terms exist in the data space, how they differ, what tools implement them, and how together they form a holistic view of data health.

“As challenges in data evolve, so do the roles that tackle them."

“Tracing the evolution of Data Quality process and implementation"