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Core learning
Learn relationships, roles, configuration effects, and tradeoffs through Databricks examples.
Databricks as a system: data, AI, Lakebase and Lakewatch
Follow a support request across governed analytical data, agent tools, transactional state and security telemetry. Learn which boundary each service owns and which settings change the design.
Auto Loader: from arriving files to trustworthy BI
Connect file discovery, checkpoint state, schema decisions, Delta layers and BI grain; reason about settings, recovery, permissions and cost.
Lakebase: separate serving data from transactional truth
Design a returns service that reads analytical features and writes operational decisions. Trace synced tables, Postgres transactions, change feeds and LTAP Direct Writes, then reason about keys, scaling, availability and cost.
Databricks AI roles: Genie, governed agents, memory and evaluation
Assign business questions, agent workflows and coding assistance to the right roles. Follow tool identities, state isolation, budget limits and failure evidence in a support case.
Lakewatch: follow security evidence from collection to response
Map security telemetry, normalization, governed evidence, detections and agent-assisted investigation. Use a synthetic AWS-oriented case to distinguish an alert, an incident decision and an executed response.
Data-connected AI with Databricks: Agent Bricks and Unity Gateway in practice
Design authorization, meaning, audit, and cost before retrieval quality when connecting AI to enterprise data.