Download lakehouse reference architectures

This article covers architectural guidance for the lakehouse in terms of data source, ingestion, transformation, querying and processing, serving, analysis/output, and storage.

Each reference architecture has a downloadable PDF in 11 x 17 (A3) format.

Generic reference architecture

Download: Generic lakehouse reference architecture for Databricks (PDF)

Organization of the reference architectures

The reference architecture is structured along the swim lanes Source, Ingest, Transform, Query and Process, Serve, Analysis, and Storage:

• Source

  The architecture distinguishes between semi-structured and unstructured data (sensors and IoT, media, files/logs), and structured data (RDBMS, business applications). SQL sources (RDBMS) can also be integrated into the lakehouse and Unity Catalog without ETL through lakehouse federation. In addition, data might be loaded from other cloud providers.

• Ingest

  Data can be ingested into the lakehouse via batch or streaming:

  • Files delivered to cloud storage can be loaded directly using the Databricks Auto Loader.

  • For batch ingestion of data from enterprise applications into Delta Lake, the Databricks lakehouse relies on partner ingest tools with specific adapters for these systems of record.

  • Streaming events can be ingested directly from event streaming systems such as Kafka using Databricks Structured Streaming. Streaming sources can be sensors, IoT, or change data capture processes.

• Storage

  Data is typically stored in the cloud storage system where the ETL pipelines use the medallion architecture to store data in a curated way as Delta files/tables.

• Transform and Query and process

  The Databricks lakehouse uses its engines Apache Spark and Photon for all transformations and queries.

  Due to its simplicity, the declarative framework DLT (Delta Live Tables) is a good choice for building reliable, maintainable, and testable data processing pipelines.

  Powered by Apache Spark and Photon, the Databricks Data Intelligence Platform supports both types of workloads: SQL queries via SQL warehouses, and SQL, Python and Scala workloads via workspace clusters.

  For data science (ML Modeling and Gen AI), the Databricks AI and Machine Learning platform provides specialized ML runtimes for AutoML and for coding ML jobs. All data science and MLOps workflows are best supported by MLflow.

• Serve

  For DWH and BI use cases, the Databricks lakehouse provides Databricks SQL, the data warehouse powered by SQL warehouses.

  For machine learning, model serving is a scalable, real-time, enterprise-grade model serving capability hosted in the Databricks control plane.

  Operational databases: External systems, such as operational databases, can be used to store and deliver final data products to user applications.

  Collaboration: Business partners get secure access to the data they need through Delta Sharing.

Capabilities for your workloads

In addition, the Databricks lakehouse comes with management capabilities that support all workloads:

• Data and AI governance

  The central data and AI governance system in the Databricks Data Intelligence Platform is Unity Catalog. Unity Catalog provides a single place to manage data access policies that apply across all workspaces and supports all assets created or used in the lakehouse, such as tables, volumes, features (feature store), and models. Unity Catalog can also be used to capture runtime data lineage across queries run on Databricks.

  For Observability, system tables is a Databricks-hosted analytical store of your account's operational data. System tables can be used for historical observability across your account.

• Orchestration

  Databricks workflows orchestrates data processing, machine learning, and analytics pipelines on the Databricks Data Intelligence Platform. Workflows has fully managed orchestration services integrated into the Databricks platform, including Databricks jobs to run non-interactive code in your Databricks workspace and Delta Live Tables to build reliable and maintainable ETL pipelines.

The Data Intelligence Platform reference architecture on Azure

The Azure Databricks Reference Architecture is derived from the generic reference architecture by adding Azure-specific services for the Source, Ingest, Serve, Analysis/Output, and Storages elements.

Download: Reference architecture for the Databricks lakehouse on Azure

The Azure reference architecture shows the following Azure-specific services for Ingest, Storage, Serve and Analysis/Output:

• Azure Synapse and SQL Server as source systems for Lakehouse Federation
• Azure IoT Hub and Azure Event Hub for streaming ingest
• Azure Data Factory for batch ingest
• Azure Data Lake Storage Gen 2 (ADLS) as the object storage
• Azure SQL DB and Azure Cosmos DB as operational databases
• Azure Purview as the enterprise catalog to which UC will export schema and lineage information
• Power BI as the BI tool

Use case: Batch ETL

Download: Batch ETL reference architecture for Azure Databricks

Ingest tools use source-specific adapters to read data from the source and then either store it in the cloud storage from where Auto Loader can read it, or call Databricks directly (for example, with partner ingest tools integrated into the Databricks lakehouse). To load the data, the Databricks ETL and processing engine - via DLT - runs the queries. Single or multitask jobs can be orchestrated by Databricks workflows and governed by Unity Catalog (access control, audit, lineage, and so on). If low-latency operational systems require access to specific golden tables, they can be exported to an operational database such as an RDBMS or key-value store at the end of the ETL pipeline.

Use case: Streaming and change data capture (CDC)

Download: Spark structured streaming architecture for Azure Databricks

The Databricks ETL engine uses Spark Structured Streaming to read from event queues such as Apache Kafka or Azure Event Hub. The downstream steps follow the approach of the Batch use case above.

Real-time change data capture (CDC) typically uses an event queue to store the extracted events. From there, the use case follows the streaming use case.

If CDC is done in batch where the extracted records are stored in cloud storage first, then Databricks Autoloader can read them and the use case follows Batch ETL.

Use case: Machine learning and AI

Download: Machine learning and AI reference architecture for Azure Databricks

For machine learning, the Databricks Data Intelligence Platform provides Mosaic AI, which comes with state-of-the-art machine and deep learning libraries. It provides capabilities such as Feature Store and model registry (both integrated into Unity Catalog), low-code features with AutoML, and MLflow integration into the data science lifecycle.

All data science-related assets (tables, features, and models) are governed by Unity Catalog and data scientists can use Databricks Workflows to orchestrate their jobs.

For deploying models in a scalable and enterprise-grade way, use the MLOps capabilities to publish the models in model serving.

Use case: Retrieval Augmented Generation (Gen AI)

Download: Gen AI RAG reference architecture for Azure Databricks

For generative AI use cases, Mosaic AI comes with state-of-the-art libraries and specific Gen AI capabilities from prompt engineering to fine-tuning of existing models and pre-training from scratch. The above architecture shows an example of how vector search can be integrated to create a RAG (retrieval augmented generation) AI application.

For deploying models in a scalable and enterprise-grade way, use the MLOps capabilities to publish the models in model serving.

Use case: BI and SQL analytics

Download: BI and SQL analytics reference architecture for Azure Databricks

For BI use cases, business analysts can use Databricks Lakeview, the Databricks SQL editor or specific BI tools such as Tableau or Power BI. In all cases, the engine is Databricks SQL (serverless or non-serverless) and data discovery, exploration, and access control are provided by Unity Catalog.

Use case: Lakehouse federation

Download: Lakehouse federation reference architecture for Azure Databricks

Lakehouse federation allows external data SQL databases (such as MySQL, Postgres, SQL Server, or Azure Synapse) to be integrated with Databricks.

All workloads (AI, DWH, and BI) can benefit from this without the need to ETL the data into object storage first. The external source catalog is mapped into the Unity catalog and fine-grained access control can be applied to access via the Databricks platform.

Use case: Enterprise data sharing

Download: Enterprise data sharing reference architecture for Azure Databricks

Enterprise-grade data sharing is provided by Delta Sharing. It provides direct access to data in the object store secured by Unity Catalog, and Databricks Marketplace is an open forum for exchanging data products.