Databricks

The leading data lakehouse platform — combines data warehousing and data lake capabilities on Apache Spark, and is the central tool of modern enterprise data stacks.

Databricks is a cloud-based data and AI platform built on Apache Spark, created by the original Spark developers. It introduced the 'lakehouse' architecture — combining the low-cost storage and flexibility of a data lake with the structure and performance of a data warehouse. Databricks is used by over 10,000 organizations for large-scale data engineering, ML training, and SQL analytics, and integrates deeply with AWS, Azure, and GCP. It has become a central component of modern enterprise data stacks, often running alongside dbt, Snowflake, and Airflow. Databricks experience is increasingly required in senior data engineering and ML engineering roles.

Typical time to job-readiness: ~4 weeks.

Learning Databricks

Beginner

Understand the lakehouse concept and how Databricks differs from a traditional data warehouse. Get hands-on with Databricks Community Edition (free): create a cluster, load data into a Delta table, and run PySpark and SQL queries in a notebook.

Intermediate

Delta Lake fundamentals (ACID transactions, time travel, schema enforcement), structured streaming for real-time pipelines, Unity Catalog for data governance, and integrating Databricks with dbt for transformation workflows.

Advanced

Databricks Asset Bundles for CI/CD, MLflow for experiment tracking and model registry, optimizing Spark jobs (partitioning, caching, broadcast joins), and Databricks SQL for analyst-facing workloads. Databricks certifications (Data Engineer Associate, Machine Learning Professional) are well-regarded and employer-recognized.

Key concepts

  • Lakehouse architecture: combines data lake (cheap storage, flexible schema) with warehouse (SQL queries, ACID transactions)
  • Delta Lake: open-source storage layer providing ACID transactions, schema enforcement, and time travel on top of Parquet
  • Unity Catalog: centralized governance for data access, lineage, and permissions across the Databricks workspace
  • MLflow: open-source experiment tracking and model registry — tracks parameters, metrics, and artifacts per run
  • Databricks notebooks: interactive Python/SQL/Scala/R notebooks that run on Spark clusters
  • Jobs: scheduled or triggered runs of notebooks or code; the production workflow execution layer

Common interview topics

  • What is the lakehouse architecture and how does Databricks implement it
  • What is Delta Lake and what does it add over plain Parquet files
  • How does MLflow work and why do you need experiment tracking
  • How would you optimize a slow Spark job running in Databricks
  • Walk me through how you'd design a production data pipeline in Databricks

Browse Databricks jobs