Maximize the value of your data lakehouse with Databricks and Liquibase

Databricks is a data and artificial intelligence (AI) company founded by the creators of innovative open-source projects including Delta Lake, MLflow, Apache Spark, Mosaic, and PT. Databricks converges these open-source technologies as the foundation for a broad unified data analytics platform so teams can use data and AI together seamlessly.

Integrating DevOps practices into every level of modern application development and data pipelines is no longer a luxury or a competitive advantage – it's a requirement. Fast, reliable automation across your data stores is a necessity to keep every element of your data-driven businesses at the forefront of growth and innovation. 

The rise of nontraditional and unstructured data stores — such as data lakehouses — introduces flexibility at a scale that makes automating processes increasingly tricky. Yet automation is crucial to supporting rapid application development cycles and data pipelines that run right through the heart of the business. 

The Databricks for Liquibase connector brings DevOps automation to data lakehouses to support the agility, scalability, and innovation your teams demand. 

Enabling a truly unified data pipeline

Before Databricks, most organizations had a difficult time realizing a unified platform because the existing technology required stitching together many disparate parts. Data ingestion, transformation, business intelligence (BI), advanced machine learning (ML), and now generative AI all need to be integrated but have separate governance and data models. 

The complicated interconnections resulted in too many brittle failure points, making it difficult to manage, scale, control, and govern a unified data stack because it wasn't really unified.

Databricks solves this problem with its data lakehouse. Its connective governance layer called Unity Catalog enables a truly unified environment that interoperates seamlessly. The data lakehouse combines a data lake's scalable storage capabilities with a data warehouse's management and performance features, enabling both high-volume data storage and efficient data analytics.

With Databricks SQL Warehouse and Databricks Unity catalog working together, you can seamlessly govern access and collaborate on all of your data and AI assets and get automated end-to-end lineage.

Databricks offers classic table constraints, like primary and foreign key constraints, identity columns, and data quality check constraints. Databricks SQL now supports system tables that allow you to get all the standard information you're used to seeing – the information required to run database change management and governance on your data warehouse.

Automating change management for Databricks with Liquibase

With the Databricks for Liquibase connector, you can now manage your Database SQL Warehouse as you would other data warehouses. This enables Database DevOps automation with the governance, auditability, and observability your teams need to accelerate application delivery and continuously optimize processes throughout your data practice. 

Liquibase streamlines, centralizes, and improves database change workflows so they run automatically when triggered by corresponding application and data updates. It does this by:

• Automating and tracking database change management
• Unifying control and enabling customizable governance
• Enabling database observability for continuous optimization

Databricks and Liquibase help database, application, and DevOps professionals get maximum value from their data lakehouses. In this on-demand webinar, experts from both companies explain how to automate change management across a data lake to improve collaboration, observability, governance, and productivity.

Elevating DevOps maturity

Registrants of this webinar were surveyed about which phase of DevOps maturity they felt most aligned with their organization and team, including:

• Initial: Traditional environment with Dev & Ops separate
• Managed: Beginning stages focused on agility with initial automation
• Defined: Org-wide transformation begins with defined process
• Measured: Process & automation, with continuous improvement
• Optimized: Achievements are visible & team gaps disappear

Results showed 20% of the audience fell into the first three stages of DevOps maturity, while the remaining 40% chose “measured” but notably no one selected “optimized.” This shows that while DevOps is, of course, a strong culture at most organizations, there’s still abundant space for automation, efficiency, and innovation to take hold. 

That’s why so many database, application, and data professionals flock to database DevOps – as one of the last remaining holdouts from the CI/CD pipeline, embracing it can make the difference between one maturity stage and the next. More importantly, these stages of maturity represent more advanced capabilities for pipeline speed, innovation, and reliability.

Extending CI/CD automation to data stores

Database change management’s absence from most modern CI/CD pipelines is exactly the reason for Liquibase to exist. While application development whizzes along at warp speed with automation and continuous optimization, database workflows remain manual and stale. 

Database development running asynchronously to application development – typically handled manually by a separate team – means a higher propensity for errors and a slow process that bottlenecks overall delivery frequency. By bringing a balance of velocity, security, and governance to database workflows throughout the organization, Liquibase enables the CI/CD automation that’s been missing from high-velocity pipelines for so long. 

Liquibase brings the power of database automation to modern data stores like Databricks Data Lakehouse as well as 60 additional supported database types. By automating change management across data lakes, Liquibase and Databricks turbocharge collaboration, observability, governance, and productivity, turning the ideal of a data-driven business into a tangible reality.

Databricks provides the infrastructure and analytical tools necessary for an efficient data lakehouse, while Liquibase handles automating database change management. Combined, this solution supports a broad set of databases and caters to the needs of BI and AI pipelines without suffering the traditional database change bottlenecks. 

Moving from data lakes to lakehouses and leveraging Databricks' infrastructure innovation alongside Liquibase's CI/CD automation empowers a more integrated, efficient, and flexible data ecosystem that promises to unlock new frontiers of growth and innovation for organizations hungry to maximize the value of their data. 

For a complete walkthrough of Databricks SQL using Liquibase, in which Databricks Sr. Solutions Architect Cody Davis presents a live demo, head to the webinar replay.