First notebook: read and profile data
Create a notebook, load a small CSV from cloud storage into a DataFrame, show schema, and compute null counts for each column.
Data / Knowledge library
Databricks
unified lakehouse platform for data and ai
Why learn Databricks?
Databricks helps teams store, process, govern, analyze, and operationalize large-scale data and AI workloads in one platform. It reduces tool sprawl by combining data engineering, SQL analytics, machine learning, and governance on a lakehouse architecture.
Where it came from
2013: Founded by the creators of Apache Spark from UC Berkeley's AMPLab. Early years: Focused on managed Spark for large-scale data processing. Delta Lake was introduced to add ACID reliability to data lakes. Databricks helped popularize the 'lakehouse' architecture (warehouse-like reliability on low-cost object storage). Unity Catalog became the central governance model for data and AI assets.
- 2013: Founded by the creators of Apache Spark from UC Berkeley's AMPLab.
- Early years: Focused on managed Spark for large-scale data processing.
- Delta Lake was introduced to add ACID reliability to data lakes.
- Databricks helped popularize the 'lakehouse' architecture (warehouse-like reliability on low-cost object storage).
- Unity Catalog became the central governance model for data and AI assets.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Create a notebook, load a small CSV from cloud storage into a DataFrame, show schema, and compute null counts for each column.
- Ingest raw orders into a bronze Delta table, clean invalid rows into a silver Delta table, and schedule it as a job.
- Create a catalog/schema/table, apply role-based permissions, and build a Databricks SQL query that only authorized users can run.
- Use MERGE to upsert daily updates into a Delta table and validate record counts before and after the merge.
- Train a simple model, register it in MLflow Model Registry, deploy an endpoint, and track prediction latency + drift signals.
More ways to practice
Build a bronze-to-silver Delta flow
Ingest raw orders into a bronze Delta table, clean invalid rows into a silver Delta table, and schedule it as a job.
Governed analytics with Unity Catalog
Create a catalog/schema/table, apply role-based permissions, and build a Databricks SQL query that only authorized users can run.
Incremental upsert pipeline
Use MERGE to upsert daily updates into a Delta table and validate record counts before and after the merge.
Deploy and monitor an ML model
Train a simple model, register it in MLflow Model Registry, deploy an endpoint, and track prediction latency + drift signals.
Documentation and sources
- Databricks Documentation (docs.databricks.com)
- Databricks Academy (www.databricks.com)
- Delta Lake Docs (docs.delta.io)
- MLflow Docs (mlflow.org)
- Unity Catalog Overview (docs.databricks.com)
Catalog research notes
- Serverless options expanded for SQL analytics, jobs, and model serving.
- Unity Catalog is now the default governance layer in many new deployments.
- Lakeflow capabilities expanded for ingestion and pipeline orchestration.
- Mosaic AI features grew for model evaluation, deployment, and agent workflows.
Connect the ideas
Explore next
Real World Example
Unified Inventory Forecasting for Global Retail
Global retail enterprises managing fragmented supply chain data across disparate regional warehouses.
The business problem
Data silos between regional inventory databases and central demand planning systems prevent accurate, real-time stock replenishment, leading to frequent stockouts and overstocking.
How this topic solves it
Utilize the Databricks Lakehouse platform to ingest raw regional inventory logs into a unified Delta Lake architecture, enabling collaborative data engineering and machine learning model development on a single source of truth.
- Configure Unity Catalog to centralize access control and define metadata schemas for regional inventory datasets.
- Develop ETL pipelines using Databricks notebooks to ingest and clean raw inventory logs into bronze, silver, and gold Delta tables.
- Train demand forecasting models using MLflow to track experiments and model versions across global regions.
- Deploy the final model as a SQL-accessible function within Databricks SQL for real-time inventory replenishment dashboards.
This diagram illustrates the transformation of raw regional data into actionable business insights.
Raw inventory transaction data
Governance and metadata management
Unified storage layer
Model training and tracking
Final replenishment insights
- Regional Logs → ETL Ingestion → Delta Lake
- Unity Catalog → Governs access → Delta Lake
- Delta Lake → Model training input → MLflow
- MLflow → Forecast output → BI Dashboard
Implementation example
This code demonstrates the ingestion process where raw inventory logs are filtered and persisted into a Delta table, serving as the foundation for the unified inventory view.
# Read raw inventory data from cloud storage
df = spark.read.format("delta").load("/mnt/inventory/raw")
# Clean and write to silver table for downstream consumption
df.filter("stock_level >= 0").write.format("delta").mode("overwrite").save("/mnt/inventory/silver")Expected business outcome
A unified view of global inventory levels that allows supply chain managers to execute data-driven replenishment strategies without moving data between isolated systems.
Limitations and checks
The platform relies on the consistency of the underlying cloud object storage; users must monitor storage latency and throughput limits to ensure that high-frequency ingestion does not degrade query performance for downstream BI dashboards.