Databricks Lakehouse: Your Data, Unified

The history of Databricks

Want to speed up ML development and simplify your data architecture? The Databricks Lakehouse, with tools like MLflow, provides a unified platform to manage the entire machine learning lifecycle—from experimentation to production. By combining the strengths of data warehouses and data lakes, it offers a solid foundation built on open-source technology and distributed computing. The creators of Apache Spark designed it with these principles in mind.

A lakehouse architecture brings together the best aspects of data warehouses and data lakes. It supports transactions, schema enforcement, BI capabilities, and a range of data types. Furthermore, it offers end-to-end streaming and high-level data governance. With its low-cost cloud storage in open formats, it enables enterprises to access and manage their data seamlessly.

Databricks is cloud-agnostic, capable of managing data stored anywhere. This flexibility supports a broad array of data and AI workloads, allowing teams to access and collaborate on data, driving continuous innovation.

The Databricks Lakehouse architecture integrates Delta Lake for performance, Unity Catalog for fine-grained governance, and supports various use cases tailored to specific roles.

An overview of the Databricks Lakehouse architecture

Databricks operates on two main planes: control and compute.

The control plane manages backend services and securely stores workspace configurations. Data processing occurs in the compute plane, using resources from AWS, Azure, or Google Cloud, depending on the user’s setup. For serverless SQL warehouses or Model Serving, Databricks uses its own serverless compute resources.

In 2020, Databricks introduced its E2 architecture, enhancing security and management. Key features include multi-workspace accounts, secure cluster connectivity, IP access lists, and token management, making Databricks more secure and manageable.

Databricks supports languages like Python, SQL, R, and Scala for tasks such as data science, engineering, and visualisation. In 2023, Databricks redesigned its user interface, streamlining navigation for data science, SQL, and machine learning users, making features more accessible.

The main benefits of the Databricks Lakehouse platform

Databricks addresses traditional data challenges, like silos and fragmented governance, by providing a unified lakehouse platform. This approach offers several key benefits:

• Simplicity: Databricks combines data warehousing and AI in one platform, using natural language for a smooth user experience.
• Openness: Built on open standards, Databricks gives users full data control and avoids proprietary formats.
• Collaboration: Delta Sharing enables secure, real-time data sharing without complex ETL processes.
• Multi-cloud support: Databricks Lakehouse operates on Azure, AWS, and Google Cloud, integrating seamlessly with native services.

Databricks Lakehouse use cases: from BI to AI?

Databricks offers a complete suite of tools that allows users to aggregate data sources, process, analyse, and monetise datasets. This platform supports diverse applications, from BI to AI.

Use cases include:

• Enterprise data lakehouse: Combines the strengths of data warehouses and lakes, providing a single source of truth.
• ETL and data engineering: Uses Apache Spark and Delta Lake to streamline data ingestion and transformation.
• Machine learning: Leverages tools like MLflow and Databricks Runtime for robust ML workflows.
• Generative AI: Supports large language models with libraries like Hugging Face Transformers.
• Data governance: Ensures secure data access with Unity Catalog.
• Real-time analytics: Enables fast insights using Apache Spark Structured Streaming.

AI and ML with Databricks

Databricks supports the full machine learning lifecycle, offering comprehensive governance across the ML pipeline. Essential ML tools include:

• Unity Catalog for data governance;
• Lakehouse Monitoring to track model quality and drift;
• Databricks AutoML for automated model training;
• MLflow to monitor model development;
• Databricks Model Serving for high-availability deployments.

The Databricks Runtime for Machine Learning includes libraries such as Hugging Face Transformers, enabling seamless integration of pre-trained models. Analysts can also use large language models like OpenAI directly in their workflows.

Databricks in Action at Devoteam

As a Databricks Consulting Partner, Devoteam assists organisations in building, deploying, or migrating to the Databricks Lakehouse Platform. With specialised expertise, our team supports complex data engineering, collaborative data science, and full ML lifecycle initiatives.

Curious to see how Databricks helped us empower Omgevingsdienst, an environmental service in the Netherlands, with better data control? Explore our success story here.

In conclusion

Over 9,000 organisations worldwide rely on Databricks for their data intelligence needs. With its scalable support for data engineering, collaborative data science, and business analytics, Databricks empowers data teams to tackle complex challenges. By democratising data and AI, Databricks helps organisations drive impactful change and innovate faster.

Want to assess Databricks’s relevance and potential for your organisation?

Connect with one of our experts today and find out if Databricks is the right solution for you.

This article and infographic are part of a larger series centred around the technologies and themes found within the TechRadar by Devoteam. To learn more about Databricks and other technologies you need to know about, please explore the TechRadar by Devoteam.