AI cannot rescue a broken data foundation. End-to-end data engineering — source ingestion, Bronze/Silver/Gold, certified BI, and governed AI agents — is the discipline that turns raw enterprise data into intelligence executives actually trust. The future state isn’t data plumbing. It’s intelligence engineering.
A reference Azure Databricks architecture sized for mid-market teams, lane by lane, with the Azure services and Unity Catalog decisions that are expensive to change later.
A Databricks implementation has two price tags: the services to build the lakehouse and the platform consumption to run it. Here is what moves each one, scope by scope.
Databricks system tables turn the lakehouse bill into data you can query. This guide covers the billing tables, tag design, budgets and a chargeback model finance will accept.
Detection engineering is usually taught as a SOC skill. On a lakehouse it becomes a data engineering discipline, with versioned rules, backtests on real history and measured alert quality.
A NetSuite data warehouse on Databricks succeeds or fails on the gold model and the trial balance tie-out, not the connector. Here is how to build one that finance will sign off.
Three ways to get SAP data into the Databricks lakehouse, compared on copies, semantics, latency and lock-in, with the process red flags to clear before you connect anything.
Most Splunk alternative lists compare log search tools. Security teams under ingest-cost pressure are really choosing where their data lives. Here are seven real options and how to judge them.
dbt and Lakeflow Declarative Pipelines both transform data on a Databricks lakehouse. Here is how to decide which one owns which layer, and how to run dbt well if you keep it.