06.04 · Walkthrough
Databricks and Unified Analytics
Use Databricks notebooks and Delta tables to build an offline feature transformation pipeline.
Databricks notebooks can be used as the development surface for Spark feature pipelines, with Delta tables providing the reliable storage contract. Raw or refined lakehouse data is transformed into reusable offline feature tables, then scheduled as jobs so training, batch scoring, validation and backfills use consistent, reproducible inputs.
What this lesson answers
- how to build offline features in Databricks
- when should feature pipelines write Delta tables
- how do notebooks become production Databricks jobs
Notes
Databricks provides a unified analytics environment where data engineering, data science, and machine learning workflows can run on the same lakehouse foundation. Instead of moving data between separate storage systems, ETL tools, notebooks, and ML platforms, teams can use notebooks to develop transformations interactively, schedule them as production jobs, and store the results in Delta tables.
Common questions
- Are Databricks notebooks only for exploration?
- No. They are often used interactively at first, but the same logic can be parameterised, tested and run as scheduled jobs. For feature engineering, the notebook is the authoring interface, while the production workflow is the job that executes the transformation against governed inputs and writes curated outputs.
- Why use Delta tables for offline feature engineering?
- Delta tables add reliability to object storage: schema checks, transactional writes, table history and efficient changes. That matters for feature pipelines because training data must be reproducible, joins must be repeatable, and downstream consumers need a stable table contract rather than an ad hoc file dump.
- What makes an offline feature pipeline reproducible?
- The transformation should be deterministic, tied to a defined input snapshot or processing window, and written to versioned tables. If a model needs to be investigated later, engineers should be able to identify the source data, transformation code and resulting feature table used for that training or scoring run.
Short definition: what is Databricks and Unified Analytics?