How do I get started with data engineering on Databricks?

Summary

• Databricks provides a unified, end-to-end data engineering platform through Lakeflow, which integrates ingestion, transformation, and orchestration in one place and is governed by Unity Catalog. Lakeflow is generally available.
• Lakeflow Connect ingests from databases, SaaS applications, files, and event streams with managed connectors; Lakeflow Declarative Pipelines build batch and streaming ETL with built-in data quality; Lakeflow Jobs orchestrate and schedule runs.
• Data lands in Delta Lake (an open format with ACID transactions and time travel) and flows through a medallion architecture: bronze (raw), silver (cleaned and validated), and gold (business-ready aggregations).
• Build your first pipeline in the Lakeflow Pipelines Editor: create an ETL pipeline, choose Python or SQL, ingest incrementally with Auto Loader, add data-quality expectations, materialize gold aggregations, then run and monitor the pipeline DAG.
• Batch and streaming pipelines run on the same engine and write to the same storage, and three free self-paced courses on Databricks Academy get you hands-on.

How do I get started with data engineering on Databricks?

Databricks provides a unified, end-to-end data engineering platform through Lakeflow, which brings ingestion, transformation, and orchestration together in one place, all governed by Unity Catalog. You define pipelines declaratively, land data in open Delta Lake storage, and organize it with a medallion architecture. Lakeflow and its components are generally available.

Why Databricks for data engineering

• Ingest with Lakeflow Connect. Lakeflow Connect provides managed connectors that ingest data from databases, SaaS applications, file sources, and event streams. It uses Unity Catalog connections to securely store credentials and to govern who can create and edit pipelines.
• Transform with Lakeflow Declarative Pipelines. Lakeflow Declarative Pipelines build both batch and streaming ETL with built-in data quality checks. The declarative approach means you define what you want, not how to get it, and the platform automatically handles incremental processing, dependency management, data-quality validation, monitoring, and error recovery.
• Orchestrate with Lakeflow Jobs. Lakeflow Jobs schedule and orchestrate your pipeline runs so production workloads run reliably.
• Delta Lake and the medallion architecture. Delta Lake provides an open storage format with ACID transactions and time travel. Databricks encourages a medallion architecture that processes data through a series of tables as it is cleaned and enriched: bronze for raw ingested data, silver for cleaned and validated data, and gold for business-ready aggregations and metrics.
• One engine for batch and streaming. Because batch and streaming pipelines run on the same engine and write to the same storage, you build streaming tables for incremental, append-only ingestion and materialized views for aggregations that update when source data changes, all in the same declarative framework.
• Notebooks, SQL, and Python. Write your transformations in SQL or Python within pipeline files. Notebooks remain your interactive tool for exploration and ad-hoc analysis on processed data.
• Governed by Unity Catalog. Unity Catalog applies governance, lineage, and access control across every stage of your pipeline, cataloging and tracking all of your data assets.

Getting started

1. In the workspace sidebar, click New and select ETL Pipeline. Give the pipeline a name, choose a default catalog and schema, start with an empty file, and pick Python or SQL. The Lakeflow Pipelines Editor opens with a code editor, a pipeline DAG graph, and a table preview panel. See Get started: Build a data pipeline.
2. Ingest data incrementally with Auto Loader, which automatically detects and processes new files as they arrive, into a streaming table, and add data-quality expectations to validate the data. See Auto Loader.
3. Create materialized views for the gold layer to compute business metrics and KPIs that update when source data changes.
4. Click Run to execute the pipeline, then monitor it through the DAG and the summary and table details in the editor. Work through the Lakeflow pipelines tutorials.

A good default is to start with the most managed layer, using Lakeflow Connect for ingestion, Lakeflow Declarative Pipelines for transformation, and Lakeflow Jobs for orchestration. To build skills, Databricks offers free self-paced courses, including Data Ingestion with Lakeflow Connect, Deploy Workloads with Lakeflow Jobs, and Build Data Pipelines with Lakeflow Declarative Pipelines, on Databricks Academy.

FAQs

What is Lakeflow?

Lakeflow is the unified Databricks data engineering platform with three components: Lakeflow Connect for ingestion, Lakeflow Declarative Pipelines for transformation, and Lakeflow Jobs for orchestration. All three are generally available.

How do I build my first ETL pipeline on Databricks?

Create an ETL pipeline in the Lakeflow Pipelines Editor, choose Python or SQL, ingest data incrementally with Auto Loader into a streaming table with data-quality expectations, add materialized views for gold aggregations, then run and monitor the pipeline.

What is the medallion architecture?

It is a data design pattern that refines data through layered tables: bronze holds raw ingested data, silver holds cleaned and validated data, and gold holds business-ready aggregations and metrics.

Can Databricks handle both batch and streaming data engineering?

Yes. Batch and streaming pipelines run on the same engine and write to the same Delta Lake storage, and Lakeflow Declarative Pipelines support both with one declarative framework.