Get Started With Databricks - Try Databricks for Free
Current official signup comparison: Free Edition vs. a two-week commercial Trial.
Read the docs →RESOURCE LIBRARY
Official Databricks docs, organized
Thirty-two links from the official documentation — adaptive query execution, Delta file management, streaming, Unity Catalog, Lakeflow and more. Every link opened and verified in September 2026.
Getting started — Free Databricks signup
Databricks Free Edition limitations | Databricks on AWS
Free Edition scope: one serverless workspace, no payment card, no expiration, personal/non-commercial use only, with usage and feature limits.
Read the docs →1. AQE and query performance
Adaptive query execution - Azure Databricks
How AQE dynamically re-optimizes queries at runtime (join strategies, skew handling, partition coalescing).
Read the docs →Adaptive query execution | Databricks on Google Cloud
Databricks Runtime specifics of AQE: enabling, skew thresholds, and advisory partition sizing.
Read the docs →Query profile | Databricks on AWS
Visualize query execution to find bottlenecks: operator DAG, top operators, performance insights, and sharing profiles.
Read the docs →2. Delta file management
Control data file size | Databricks on AWS
Optimized writes, auto compaction, and target file sizes for fighting the small-files problem.
Read the docs →OPTIMIZE - Azure Databricks - Databricks SQL | Microsoft Learn
SQL reference for `OPTIMIZE`: bin packing, `FULL`, partition predicates, and `ZORDER BY`.
Read the docs →Remove unused data files with vacuum | Databricks on AWS
`VACUUM` retention, `DRY RUN`, `FULL` vs. `LITE` preview, and time-travel safety caveats.
Read the docs →3. Liquid clustering and partitioning
Use liquid clustering for tables | Databricks on AWS
Liquid clustering guide: replaces static partitioning and `ZORDER`; recommended for new tables.
Read the docs →When to partition tables on Databricks | Databricks on Google Cloud
When manual partitioning still makes sense (most tables under ~100 TB don't need it).
Read the docs →CLUSTER BY clause (TABLE) | Databricks on AWS
SQL reference for `CLUSTER BY`, including `AUTO` and `NONE`, with create/alter examples.
Read the docs →4. Spark configuration and troubleshooting
Compute configuration reference | Databricks on AWS
Complete reference for compute (cluster) settings and configuration options.
Read the docs →Handling large queries in interactive workflows | Databricks on Google Cloud
Query Watchdog thresholds, disruptive joins, output ratios, and shared interactive compute guidance.
Read the docs →Classic compute termination error codes | Databricks on AWS
Look up any compute termination error code in the event log: causes, troubleshooting steps, and recommended fixes.
Read the docs →Error conditions in Databricks | Databricks on AWS
Named error conditions (human-readable, programmatic-friendly) returned by Databricks Runtime and Databricks SQL.
Read the docs →5. Structured Streaming
Use foreachBatch to write to arbitrary data sinks | Databricks on Google Cloud
Micro-batch custom sinks: at-least-once semantics, idempotency, retries, and error handling.
Read the docs →Delta Lake table streaming reads and writes | Databricks on AWS
Delta as source and sink: exactly-once writes, checkpoints, backlog metrics, and change data feed.
Read the docs →Production considerations for Structured Streaming | Databricks on AWS
Run streams as scheduled Lakeflow Jobs: watermarks, RocksDB state store, checkpointing, latency tuning, and failure design.
Read the docs →6. SQL warehouses
Create a SQL warehouse - Azure Databricks
Step-by-step creation of a SQL warehouse, including sizing and settings.
Read the docs →SQL warehouse types | Databricks on AWS
Classic, pro, and serverless warehouse types compared.
Read the docs →SQL warehouse sizing, scaling, and queuing behavior | Databricks on AWS
Sizing, scaling, and queueing behavior plus how to monitor and right-size.
Read the docs →Example queries for monitoring SQL warehouse activity | Databricks on Google Cloud
System-table queries for monitoring warehouse performance, usage, and costs.
Read the docs →7. Unity Catalog
What is Unity Catalog? | Databricks on AWS
Unity Catalog overview: unified governance for data and AI assets across clouds.
Read the docs →Data governance with Unity Catalog | Databricks on AWS
Governance guide hub: access control, auditing, lineage, and data discovery.
Read the docs →Unity Catalog privileges reference | Databricks on AWS
Full reference of securables and privileges for access control.
Read the docs →8. Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables)
What are Lakeflow pipelines? | Databricks on AWS
Core concepts of Lakeflow Spark Declarative Pipelines: datasets, flows, and pipeline development.
Read the docs →Tutorial: Build an ETL pipeline with Lakeflow Spark Declarative Pipelines - Azure Databricks
Hands-on tutorial building your first declarative ETL pipeline.
Read the docs →Best practices for Lakeflow pipelines | Databricks on AWS
Production best practices for pipeline design, maintenance, and cost.
Read the docs →Use ForEachBatch to write to arbitrary data sinks in pipelines | Databricks on Google Cloud
Custom `foreachBatch` sinks inside declarative pipelines.
Read the docs →9. Lakeflow Jobs and workflow debugging
Lakeflow Jobs | Databricks on AWS
Overview of Lakeflow Jobs: jobs, tasks, triggers, monitoring, limits, and programmatic management.
Read the docs →Monitor Lakeflow Jobs | Databricks on Google Cloud
Monitor runs, task metrics, notifications, and system tables for job observability.
Read the docs →Troubleshoot and repair job failures | Databricks on AWS
Diagnose failed runs and use repair-and-rerun to recover without reprocessing everything.
Read the docs →