Databricks vs Microsoft Fabric: Lakehouse Features, Governance, and BI Tradeoffs
Alex Rowan
2026-06-14
Practical guides, tools, and tutorials for AI development and expert prompting—craft prompts, fine-tune models, and deploy intelligent apps.
Alex Rowan
2026-06-14
A practical comparison of Databricks and Azure Synapse across architecture, pricing logic, governance, and workload fit.
2026-06-14A recurring checklist for reviewing Databricks access control, secrets, network boundaries, and audit logs on a monthly or quarterly cadence.
2026-06-14A practical Delta Lake maintenance reference covering VACUUM, OPTIMIZE, Z-ORDER, compaction, and when to revisit each one.
A reusable checklist for improving Databricks SQL performance across queries, warehouses, and Delta tables.
A practical Databricks Jobs guide for scheduling, dependencies, retries, monitoring, and recurring workflow reviews.
A practical comparison of Databricks notebooks, Jupyter, and VS Code for experimentation, collaboration, and production handoff.
A practical guide to estimating fit, limits, and cost tradeoffs for Databricks Vector Search in semantic search and RAG workloads.
A practical guide to Databricks cluster policy patterns for estimating cost, security, and self-service tradeoffs over time.
A practical comparison of Delta Live Tables, Jobs, and Structured Streaming for choosing the right Databricks pipeline pattern.
A practical Unity Catalog guide covering core features, permission design, and a migration checklist teams can review monthly or quarterly.
A practical framework for choosing Databricks or AWS Glue for ETL, streaming, governance, and long-term data engineering costs.
A practical Databricks certification guide to compare exam paths, estimate total cost, and decide when to pursue or revisit a credential.
A practical framework for comparing Databricks SQL, Snowflake, and BigQuery by workload, cost model, governance, and AI readiness.
A practical Databricks Runtime upgrade guide covering what to track, what commonly breaks, and how to decide when to upgrade.
A practical guide to choosing Databricks AutoML or custom training based on speed, control, accuracy, and production fit.
A practical framework for comparing Databricks serving endpoints, estimating scaling needs, and revisiting inference cost tradeoffs over time.
A reusable guide to MLflow on Databricks for experiment tracking, model registry decisions, and practical deployment workflow design.
A practical guide to prompt versioning for production AI apps, including testing, documentation, release workflows, and rollback planning.
A practical guide to measuring RAG with retrieval quality, groundedness, latency, and cost benchmarks that can be updated over time.