BI & Analytics
BigQuery augmented analytics: investigating metrics in SQL
Analytical workflows that previously required separate statistical notebooks or specialised tooling are increasingly becoming callable warehouse functions.
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Explore changes in data engineering, AI, analytics and cloud platforms, with practical implications for your team.
Latest article: 19 September 2026
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Showing 9 of 19 insights
Modern Data Stack
BigQuery Graph: connected data in the warehouse
Graph workloads no longer necessarily require organisations to move warehouse data into a separate graph database.
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.
BI & Analytics
Databricks Genie: more context needs more curation
Conversational analytics products are beginning to encounter the same scale, context and governance questions as conventional BI.
Data Engineering
Dataflow Pause/Resume: planning for batch recovery
Resilience features are becoming increasingly important as data pipelines take on expensive AI-processing workloads.
Data Engineering
dbt State: change-aware pipelines
Data transformation is starting to move from run everything on a schedule toward run what actually needs to change.
BI & Analytics
Fabric Warehouse Monitor: query observability in practice
Warehouse monitoring is becoming a first-class part of the analytical development experience rather than something teams assemble afterwards.
Data Governance
Iceberg Read Restrictions: portable policy needs trusted engines
Portable table access needs a policy contract between the catalog and trusted query engines.
AI for Data Teams
Redshift in ChatGPT Work: governed conversational analytics
The analytical interface is beginning to move outside the BI application while keeping governed enterprise data behind it.
Cloud Platforms
Redshift and Databricks: querying across platforms
Interoperability is becoming a practical alternative to consolidating every analytical workload onto one platform.
Data Engineering
Snowflake Zero-Copy Interactive: fewer serving copies
Low-latency analytical workloads increasingly do not require teams to create and maintain another specialised copy of their data.
Modern Data Stack
The data stack is becoming agent-ready
September’s platform changes point to a common architectural challenge: giving agents reliable context, explicit permissions and controlled execution.
Modern Data Stack
AWS’s agentic lakehouse: data access and governance
AWS’s multi-cloud lakehouse pattern puts catalogue access and permissions at the centre of agent design.
Cloud Platforms
AWS and Salesforce: governing zero-copy data access
AWS describes direct access from Salesforce Data 360 to Iceberg data in S3. Shared access still needs explicit ownership.
BI & Analytics
BigQuery AI.AGG: summarisation, quality and cost
AI.AGG brings grouped text and image summarisation into BigQuery. Test the summaries as carefully as the query.
Data Governance
Gemini Enterprise logs: what data teams should monitor
Google’s BigQuery logging pattern supports deeper analysis of Gemini Enterprise use. The telemetry also needs its own controls.
Data Engineering
Snowflake Cortex AI Function Studio: quality and cost
Snowflake’s AI Function Studio evaluates quality and inference cost together. A vendor benchmark is a starting point for your own test.
Data Engineering
Snowflake SCD-1 pipelines: partial updates and nulls
Partial change events can erase valid values if a current-state pipeline treats missing fields as replacements.
AI for Data Teams
Maia Foundation on BigQuery: what to evaluate
Matillion announced Maia Foundation for BigQuery. Teams should evaluate generated pipelines against their own delivery requirements.