Topic

Data Engineering

Pipelines, transformation, orchestration and reliable delivery.

Data Engineering

Databricks Auto CDF: deriving changes at query time

Change-data processing is becoming less dependent on configuring every table correctly before changes occur.

19 Sept 20261 min read

Source: Databricks — Runtime 19 release notes

Data Engineering

Resilience features are becoming increasingly important as data pipelines take on expensive AI-processing workloads.

19 Sept 20261 min read

Source: Google Cloud — Dataflow enhancements

Data Engineering

Data transformation is starting to move from run everything on a schedule toward run what actually needs to change.

19 Sept 20261 min read

Source: dbt Labs — dbt State GA

Data Engineering

Low-latency analytical workloads increasingly do not require teams to create and maintain another specialised copy of their data.

19 Sept 20261 min read

Source: Snowflake — Zero-Copy Interactive

Data Engineering

Snowflake’s AI Function Studio evaluates quality and inference cost together. A vendor benchmark is a starting point for your own test.

23 Jul 20261 min read

Source: Snowflake Blog

Data Engineering

Partial change events can erase valid values if a current-state pipeline treats missing fields as replacements.

23 Jul 20261 min read

Source: Snowflake Blog