Practical articles from our engineers on the latest platform releases, on migrating to Databricks, and on what it all means for banking, insurance, fintech, telecom and healthcare.
What Canadian banks, insurers and enterprises need to plan for when they build on Databricks: Canadian regions, OSFI guidelines, PIPEDA and Québec Law 25.
Read the article: Databricks in Canada: data residency, OSFI and Québec Law 25 →Genie One is now generally available for business users, and Genie Code brings an AI coding agent to data engineering and ML.
Read the article: Genie One and Genie Code: what they mean for data teams →Lakebase brings managed Postgres into the lakehouse, and LTAP aims to serve transactional and analytical work from a single copy of data.
Read the article: Lakebase and LTAP: one copy of data for apps and analytics →Agent Bricks is generally available with managed memory and sandboxes, and Unity AI Gateway adds runtime controls for AI spend, routing and MCP servers.
Read the article: Governing enterprise agents with Agent Bricks and Unity AI Gateway →Spark Declarative Pipelines gained a real-time mode, and Lakehouse//RT entered beta as a real-time SQL warehouse.
Read the article: Real-time on the lakehouse: Real-Time Mode and Lakehouse//RT →LakeBridge, Databricks' free AI-assisted migration tool, received improved SQL conversion this year.
Read the article: Moving to Databricks with LakeBridge: a practical playbook →Unity Catalog Metrics and Domains are in public preview, giving teams governed KPI definitions and business groupings for data and AI assets.
Read the article: Unity Catalog Metrics and Domains: KPIs your agents can trust →Banks want AI assistants and fraud models, but most still cannot see one customer across products. Fix that first.
Read the article: Banking: build a governed customer 360 before your next AI tool →Insurers hold their most valuable facts in documents. Agent Bricks and the lakehouse can turn them into decisions.
Read the article: Insurance: from claim PDF to decision with document AI →Fast-growing fintechs outgrow their app database. A lakehouse with real-time pipelines keeps analytics and risk on one platform.
Read the article: Fintech: real-time payments and fraud without a second stack →Telcos generate some of the largest data volumes of any industry. The lakehouse turns them into retention, quality and revenue gains.
Read the article: Telecom: churn, network and revenue assurance on one lakehouse →Healthcare AI only works when clinicians trust the data and patients' privacy is protected. Start with the foundation.
Read the article: Healthcare: governance first for clinical AI →Hadoop clusters are expensive to keep alive and hard to staff. Here is how to move without breaking the reports that depend on them.
Read the article: Leaving Hadoop: a step-by-step path to Databricks →You do not have to leave AWS to simplify your data platform. Consolidate several services into one governed lakehouse.
Read the article: From Redshift, EMR and Glue to Databricks on AWS →Run Databricks on Google Cloud next to your data, and bring analytics, engineering and ML onto one platform.
Read the article: BigQuery and Dataproc to Databricks on Google Cloud →End-of-life appliances and heavy ETL estates are a strong case for moving. Our team knows both sides.
Read the article: IBM Netezza, Db2 and DataStage: modernizing to Databricks →Capacity-bound warehouses cost the most at peak. Elastic compute on the lakehouse changes the economics.
Read the article: Teradata, Oracle and SQL Server: replacing the legacy warehouse →A migration is approved on value, not technology. Here is how to build a case your CFO will sign.
Read the article: Building the business case for a Databricks migration →Product names and availability reflect Databricks announcements and may change. Subscribe via RSS.
