Databricks Lakehouse Playbook: From Ingestion to BI
A practical guide to designing a Databricks lakehouse that scales from raw ingestion to analytics-ready data products without losing governance or performance.
TL;DR
Databricks works best when you standardize on Delta Lake tables, apply Medallion layers, and manage access through Unity Catalog. Combine workflows, quality checks, and cost controls to keep pipelines reliable and affordable.
LogNroll Team
Data Engineering
What Makes Databricks Different
Databricks combines a data lake, data warehouse, and AI platform in one environment. The lakehouse model keeps data on object storage while providing ACID transactions, governance, and fast analytics without duplicate copies.
Core Pillars
Delta Lake storage
ACID tables on object storage with schema enforcement and time travel.
Medallion layers
Bronze, Silver, Gold pipelines that separate raw, clean, and curated data.
Unity Catalog
Central governance for data access, lineage, and auditability.
Workflows & jobs
Orchestrate ETL, ML, and reporting with reliable scheduling.
Medallion Architecture in Practice
The Medallion model keeps your data pipeline simple and auditable. Bronze stores raw ingestion, Silver applies cleaning and quality checks, and Gold delivers business-ready aggregates for dashboards and ML features.
Bronze: raw ingestion
Store immutable source data with ingestion metadata and minimal parsing.
Silver: clean + conformed
Apply validation, dedupe, and schema rules so downstream data is trusted.
Gold: analytics + products
Model data for BI, metrics, and ML features with consistent semantics.
Pipelines That Don’t Break
Use Delta Live Tables or structured streaming when data is continuously arriving. Pair it with quality expectations to stop bad data before it reaches Gold.
Quick rule of thumb
Keep Bronze ingestion append-only. Do all enrichment in Silver. Only build business logic once in Gold.
Governance with Unity Catalog
Unity Catalog provides centralized permissions, lineage, and audit trails. Use it to define who can access what data, then enforce policies consistently across SQL, notebooks, and dashboards.
Access control
Apply role-based policies at the catalog, schema, and table levels.
Lineage & audit
Track where data came from and who used it for compliance reporting.
Cost Controls That Matter
Databricks cost typically comes from compute. Use autoscaling, right-size clusters, and turn on Photon for analytics-heavy workloads. Track cost per pipeline to avoid runaway jobs.
- Prefer job clusters for ETL and all-purpose clusters for exploration.
- Schedule heavy jobs during off-peak hours with lower node costs.
- Cache Gold tables for BI tools that scan frequently.
Migration Path from Legacy Warehouses
Start by ingesting raw data into Bronze, then incrementally recreate curated tables in Gold. Run reports in parallel until metrics match. Only then retire legacy systems.
Launch Checklist
- Define your source systems and data SLAs before you build pipelines.
- Pick a Medallion standard (Bronze/Silver/Gold) and document it.
- Centralize governance with Unity Catalog from day one.
- Instrument quality checks in Silver and Gold layers.
- Cache or optimize for BI workloads with materialized views or aggregates.
Wrap Up
Databricks shines when you commit to the lakehouse model: Delta tables, Medallion layers, and consistent governance. Build reliable pipelines, track costs, and keep the Gold layer focused on clear business outcomes.