Insights
Modern data platforms, lakehouses, pipelines, integration, migration and lineage.
36 articles
From 12 January 2027, EU cloud providers can no longer charge switching fees. Rights are not readiness: this checklist helps you make data portable and an exit executable.
Most analytical queries touch far less data than the clusters built to run them. This guide explains when single-node engines like DuckDB are the better tool, and how to adopt them safely.
Managed Postgres is moving inside the lakehouse platform. Here is what Lakebase, Snowflake Postgres and pg_lake change for apps, AI agents and pipelines.
Synapse is still supported, but new investment is going into Fabric. Here is what moves easily, what does not, and how to plan a Synapse-to-Fabric data migration in 2026.
The Digital Omnibus moved high-risk AI deadlines, not the data governance work. This playbook shows how lineage turns Article 10 requirements into evidence you can produce on demand.
AI agents are becoming the busiest consumers of enterprise data. This playbook shows how to expose data products to agents through MCP without losing ownership, governance or cost control.
Streams and tables are converging. This guide compares the main ways to land Kafka data in Iceberg, explains the tradeoffs, and gives a playbook for a reliable streaming lakehouse.
Data contracts and data products finally have shared, open standards. Here is what ODCS and ODPS define, why adoption accelerated in 2026, and how to roll them out without bureaucracy.
Iceberg v3 is now generally available on the major platforms. Here is what it changes for big data teams, and a practical plan for adopting it without breaking pipelines.
AI agents can now translate stored procedures and legacy SQL at scale. This playbook shows where they help, where they do not, and how to verify their output before cutover.
Nightly batch loads no longer satisfy dashboards, operational apps or AI agents. Here is why change data capture became the backbone of modern data integration, and how to run it well.
Zero-ETL, managed connectors and open source CDC all promise fresh data with less effort. This guide shows when each one fits, what it hides, and how to choose per data flow.
Independent integration vendors are being absorbed. Here is what the 2025–2026 deals mean for pricing, open source and lock-in, and how to make your integration stack resilient.
With warehouses writing open Iceberg tables and lakehouses serving dashboards, the old either-or question no longer fits. Here are the four patterns that matter in 2026 and how to choose between them.
Training, RAG and agents need fast access to images, video, documents and embeddings. Here is how the multimodal lakehouse works and how to design one alongside Iceberg.
With open table formats settled, the catalog decides governance, interoperability and lock-in. This guide compares the leading options and gives a practical selection method.
Virtualization, AI-assisted conversion and new targets have changed the economics of leaving Teradata and similar appliances. This guide helps you choose the path, target and timing.
PowerCenter 10.5 standard support has ended and SSIS is heading into Fabric. This decision guide compares four ETL modernization paths and shows how to choose per workload.
Text-to-SQL agents fail when business meaning lives in scattered dashboards. Here is how to modernize the warehouse with a governed semantic layer that both people and AI agents can trust.
Data mesh was declared both the future and dead. In 2026 the truth is more useful: four principles survived in modified form, and AI is now the main business case.
Hadoop clusters still run critical workloads. This playbook covers assessment, in-place versus rewrite migration to Iceberg, dual running, validation and safe decommissioning.
Moving a live database without stopping the business is possible, but only with disciplined CDC, continuous validation and a rehearsed cutover. Here is the playbook.
Data mesh stalls without a platform that makes publishing a data product easy. This playbook applies platform engineering to data: golden paths, self-service and metrics.
Data mesh, data fabric and the central lakehouse answer different questions. This guide compares them, shows how they combine, and offers a readiness checklist.
Where does your lakehouse really stand? A five-stage maturity model, a self-assessment checklist and a 2027–2030 roadmap for an open, catalog-centric, AI-ready platform.
Open table formats let many engines read the same data. This guide compares Spark, Trino, StarRocks and other engines by workload and offers criteria for choosing a mix.
Embeddings have become a big data problem. This guide compares four ways to store and search vectors at scale and gives criteria for matching each to your workloads.
Getting modeled data back into business applications is now an integration priority. Compare reverse ETL, zero-copy federation and composable CDPs, with criteria and a checklist.
Table-level lineage is no longer enough. This playbook shows how to scope, capture, stitch and operate column-level lineage across a multi-engine lakehouse.
Lineage used to be a catalog feature that broke at every tool boundary. OpenLineage turned it into an open event standard. Here is how it works and how to adopt it pragmatically.
Code conversion is getting faster, but trust still depends on proving the data matches. This playbook shows how to validate and reconcile a warehouse migration before cutover.
Open table formats need housekeeping. This playbook covers compaction, snapshot expiry, orphan cleanup and when to trust managed maintenance on S3 Tables or Databricks.
With ECC mainstream maintenance ending at the close of 2027, data readiness is now the critical path for S/4HANA programs. This playbook covers approach, cleansing, mock loads and reconciliation.
Supervisors now expect end-to-end, attribute-level lineage from data capture to risk report. This checklist helps banks assess gaps and plan remediation.
Event streams are now core integration infrastructure. This playbook shows how to design, document and govern events with AsyncAPI 3.0, CloudEvents and schema registries.
Classic lineage stops at the dashboard. Generative AI needs lineage that follows data into chunks, embeddings, vector indexes, model endpoints and agent actions.
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