Data Engineering
Tens of millions of production workloads run daily on Databricks
Easily ingest and transform batch and streaming data on the Databricks Lakehouse Platform. Orchestrate reliable production workflows while Databricks automatically manages your infrastructure at scale. Increase the productivity of your teams with built-in data quality testing and support for software development best practices.
Unify batch and streaming
Eliminate silos on one platform with a single and unified API to ingest, transform and incrementally process batch and streaming data at scale.
Focus on getting value from data
Databricks automatically manages your infrastructure and the operational components of your production workflows so you can focus on value, not on tooling.
Connect your tools of choice
An open Lakehouse Platform to connect and use your preferred data engineering tools for data ingestion, ETL/ELT and orchestration.
Build on the Lakehouse Platform
The Lakehouse Platform provides the best foundation to build and share trusted data assets that are centrally governed, reliable and lightning-fast.
“To us, Databricks is becoming the one-stop shop for all of our ETL work. The more we work with the Lakehouse Platform, the easier it is for both users and platform administrators.”
How does it work?
Simplified data ingestion
Ingest data into your Lakehouse Platform and power your analytics, AI and streaming applications from one place. Auto Loader incrementally and automatically processes files landing in cloud storage — without the need to manage state information — in scheduled or continuous jobs. It efficiently tracks new files (scaling to billions) without having to list them in a directory, and can also automatically infer the schema from the source data and evolve it as it changes over time. The COPY INTO command makes it easy for analysts to perform batch file ingestion into Delta Lake via SQL.
“We’ve seen a 40% productivity uplift for data engineering — reducing the time it takes to develop new ideas from days to minutes and increasing the availability and accuracy of our data.”
— Shaun Pearce, Chief Technology Officer, Gousto
Automated ETL processing
Once ingested, raw data needs transforming so that it’s ready for analytics and AI. Databricks provides powerful ETL capabilities for data engineers, data scientists and analysts with Delta Live Tables (DLT). DLT is the first framework that uses a simple declarative approach to build ETL and ML pipelines on batch or streaming data, while automating operational complexities such as infrastructure management, task orchestration, error handling and recovery, and performance optimization. With DLT, engineers can also treat their data as code and apply software engineering best practices like testing, monitoring and documentation to deploy reliable pipelines at scale.
Reliable workflow orchestration
Databricks Workflows is the fully managed orchestration service for all your data, analytics and AI that is native to your Lakehouse Platform. Orchestrate diverse workloads for the full lifecycle including Delta Live Tables and Jobs for SQL, Spark, notebooks, dbt, ML models and more. Deep integration with the underlying Lakehouse Platform ensures you will create and run reliable production workloads on any cloud while providing deep and centralized monitoring with simplicity for end users.
“Our mission is to transform the way we power the planet. Our clients in the energy sector need data, consulting services and research to achieve that transformation. Databricks Workflows gives us the speed and flexibility to deliver the insights our clients need.”
— Yanyan Wu, Vice President of Data, Wood Mackenzie
End-to-end observability and monitoring
The Lakehouse Platform gives you visibility across the entire data and AI lifecycle so data engineers and operations teams can see the health of their production workflows in real time, manage data quality and understand historical trends. In Databricks Workflows you can access dataflow graphs and dashboards tracking the health and performance of your production jobs and Delta Live Tables pipelines. Event logs are also exposed as Delta Lake tables so you can monitor and visualize performance, data quality and reliability metrics from any angle.
Next-generation data processing engine
Databricks data engineering is powered by Photon, the next-generation engine compatible with Apache Spark APIs delivering record-breaking price/performance while automatically scaling to thousands of nodes. Spark Structured Streaming provides a single and unified API for batch and stream processing, making it easy to adopt streaming on the lakehouse without changing code or learning new skills.
State-of-the art data governance, reliability and performance
Data engineering on Databricks means you benefit from the foundational components of the Lakehouse Platform — Unity Catalog and Delta Lake. Your raw data is optimized with Delta Lake, an open source storage format providing reliability through ACID transactions, and scalable metadata handling with lightning-fast performance. This combines with Unity Catalog to give you fine-grained governance for all your data and AI assets, simplifying how you govern, with one consistent model to discover, access and share data across clouds. Unity Catalog also provides native support for Delta Sharing, the industry’s first open protocol for simple and secure data sharing with other organizations.
Migrate to Databricks
Tired of the data silos, slow performance and high costs associated with legacy systems like Hadoop and enterprise data warehouses? Migrate to the Databricks Lakehouse: the modern platform for all your data, analytics and AI use cases.
Integrations
Provide maximum flexibility to your data teams — leverage Partner Connect and an ecosystem of technology partners to seamlessly integrate with popular data engineering tools. For example, you can ingest business-critical data with Fivetran, transform it in place with dbt, and orchestrate your pipelines with Apache Airflow.
Data Ingestion and ETL
+ Any other Apache Spark™️ compatible client
