Practical Databricks notes covering notebooks, jobs, Python services, assistants, environments, and lakehouse delivery patterns.
Build Production Databricks Workflows With Quality Gates: A practical design pattern for Databricks workflows that separates ingestion, transformation, validation, publishing, and recovery.
Build Reusable Databricks Notebook Utilities in Python: A practical pattern for packaging reusable Python helpers that Databricks notebooks and jobs can share instead of duplicating logic cell by cell.
Call Databricks Jobs from a Python Service: A practical example of triggering Databricks jobs from a Python service so notebook workflows can plug into larger engineering systems.
Databricks Platform Architecture: A reference for how Databricks is organized: the split between control plane and data plane, Unity Catalog’s 3-level namespace, the available compute types, and what Delta Lake provides at the storage layer.
Deploy Databricks Projects with Declarative Automation Bundles: A practical reference for Declarative Automation Bundles (DABs): project structure, environment targets, bundle commands, Git Folders, and wiring up a CI/CD pipeline with GitHub Actions.
Design Reliable Databricks LLM Workflows for Data Teams: A practical framework for making Databricks-based LLM workflows more dependable by separating instructions, context, and operational controls.
Govern Data in Unity Catalog: A practical reference for Unity Catalog data governance: the difference between managed and external tables, GRANT/REVOKE/DENY privilege rules, column-level masking, row-level security with row filters, and attribute-based access control.
Ingest Data into Databricks with COPY INTO, Auto Loader, and Lakeflow Connect: A practical reference for the four main ways to bring data into Databricks: COPY INTO for batch loads, Auto Loader for incremental file ingestion, Lakeflow Connect for managed connectors, and JDBC for database sources.
Manage Databricks Deployments With Assets, Environments, and Promotion: A best-practice guide for promoting Databricks work across environments using explicit assets, configuration, jobs, tests, and release discipline.
Monitor Databricks Jobs and Troubleshoot Spark Performance: A practical guide to monitoring Databricks job runs, reading the Spark UI, diagnosing data skew and spill, tuning shuffle partitions, and diagnosing common cluster startup failures.
Operate Delta Lake Tables with Time Travel, VACUUM, and Liquid Clustering: A practical reference for Delta Lake table operations: querying history, reverting changes, purging deleted data, compacting files, and clustering for query performance.
Orchestrate Work with Lakeflow Jobs: A practical reference for Lakeflow Jobs: task types, dependency wiring, retry configuration, conditional branching, trigger types, and how SKIPPED and FAILED statuses work.
Organize Databricks Projects as Assets, Not Ad Hoc Notebooks: A practical case for organizing Databricks projects as explicit assets, jobs, configs, and reusable modules instead of letting logic sprawl across disconnected notebooks.
Serve ML Inference from Databricks and Python Clients: A practical architecture for serving Databricks-hosted models behind a stable API that Python clients can call from downstream applications.
Transform Data Across Lakehouse Layers: A practical guide to Databricks transformation patterns: cleaning and typing data in the Silver layer, join operations, window functions, building Gold layer objects, and enforcing data quality with DLT expectations.
Use Databricks Assistant to Speed Up SQL and Notebook Work: A practical guide to using Databricks Assistant for SQL generation, notebook cleanup, debugging, and faster lakehouse iteration.