Azure Databricks moves to Unity Catalog-only model on Sept 30 2026

A significant advancement in data platform architecture has emerged from Databricks. The introduction of Unity Catalog Open APIs enables organizations to allow external compute engines, such as Apache Spark, Flink, DuckDB, and Trino, to directly create, read, and write to Unity Catalog Managed Delta Tables. This is achieved while maintaining full centralized governance, auditing, and fine-grained access control. This development eliminates the long-standing trade-off between governance and flexibility. Now, there is a single copy of data that is governed centrally and accessible from any engine. This is a game-changer for enterprises operating in multi-engine environments. This advancement represents a big win for reducing data duplication, lowering costs, and simplifying compliance. What are your thoughts on this direction for open data platforms? #Databricks #UnityCatalog #DataGovernance #DeltaLake #DataEngineering #AIPlatform #Database #Lakehouse #MicrosoftFabric #Microsoft #Deltalake #OpenAPI #DuckDB

If you want your data assets in Databricks to be accessed only by the people you choose, then Unity Catalog is exactly what you need. If you have worked with Databricks, you probably already know how powerful Unity Catalog is for data governance and access control. I recently built a notebook that covers the major components and interactions with Unity Catalog, including: Tables, Volumes, Views, Functions, External locations, Credentials and more It is a practical walkthrough to help understand how everything connects in real use cases. Check out the doc, and if you want to download the notebook from the repo, the link is in the comments. #Databricks #UnityCatalog #DataEngineering #DataGovernance #BigData #DataPlatform #Lakehouse #DataArchitecture #CloudComputing #AzureDatabricks #AWS #GCP #MachineLearning #ArtificialIntelligence #DataScience #Analytics #DataSecurity #AccessControl #DataAccess #DataManagement #MetadataManagement #DataOps #MLOps #ETL #ELT #DataPipelines #ModernDataStack #SQL #ApacheSpark #DeltaLake #DataCatalog #DataLake #CloudData #EnterpriseData #TechCommunity #AIEngineering #BackendEngineering #DataSolutions #SoftwareEngineering #OpenSource #TechLearning

Before Unity Catalog, Databricks governance was a mess nobody liked to admit. Every workspace had its own Hive metastore. Access controls were inconsistent across workspaces. Lineage tracking was either manual or nonexistent. Data sharing between teams meant copying datasets and hoping nothing drifted. Unity Catalog changed all of that with one idea: one metastore to govern everything. Here is what actually changed. Before, a table in workspace A was invisible to workspace B. Now catalogs, schemas, and tables are defined once and visible across every workspace in your account. Your data engineers, data scientists, and analysts all operate from the same hierarchy. Access control went from coarse and inconsistent to column-level and row-level, enforced uniformly across Spark, SQL, and even the REST API. You define it once in Unity Catalog. It applies everywhere. Lineage became automatic. Every read, every write, every transformation is tracked end to end without any manual tagging. You can trace a dashboard metric back to the raw source table in a few clicks. Delta Sharing lets you share live data across clouds and organizations without copying anything. The recipient queries directly from your catalog. Nothing moves. And all of it sits under one metastore, one audit log, one place to manage everything. If you are still running workspace-level Hive metastores in 2026, the migration is worth it. Unity Catalog is not just a governance upgrade. It is a platform maturity upgrade. Are you already on Unity Catalog? What did the migration look like for your team? #Databricks #UnityCatalog #DataEngineering #DataGovernance #DeltaLake #BigData #DataArchitecture

Title: “Data Governance” has Evolved From “Afterthought” Into “Engine”. 🚀 Anyone who’s dealt with managing Databricks environments with multiple workspaces, using old-fashioned Hive Metastore, knows what I’m talking about: metadata silos, scattered permissions, and absolutely zero lineage tracking. For anyone who was fortunate enough to develop a scalable data platform architecture, switching to Unity Catalog (UC) represents much more than an improvement—it is an evolution of treating data as a first-class citizen. Why Unity Catalog is a Breakthrough for Your Lakehouse: Unified Governance: One-stop-shop for granting access to all your data files, databases, and models in your various workspaces and cloud regions. Automatic Lineage: Column-level lineage tracking comes out of the box with Unity Catalog. This feature a godsend a for auditing data lineage from Hub tables to Satellite tables in Data Vault 2.0. Granular Permissions: Finally, row-level/column-level permissioning is natively supported. You want to mask personal information for a particular analyst team within your organization? A simple SQL statement will do. Effortless Data Distribution: The integration of Unity Catalog with Delta Sharing allows you to share datasets with any partner who doesn’t even use Databricks as their cloud provider, while avoiding duplicate copies of data. #Databricks #UnityCatalog #DataGovernance #DataEngineering #Lakehouse #ModernDataStack #SeniorDataEngineer

15 Must-Read Databricks Interview Questions 2026 1. What is the difference between Auto Loader and Event Hubs? In which scenarios would you choose one over the other? 2. You receive files daily in Azure Data Lake Storage (ADLS). How would you automatically detect file arrival and identify corrupt files without stopping ingestion? 3. How would you design a Lakehouse architecture for streaming data that supports both real-time processing and historical backfills? 4. For streaming ingestion, is it better to use a single Delta table or multiple Delta tables? How do you make this decision? 5. How do Delta Lake and checkpointing together ensure exactly-once processing? 6. How would you handle schema evolution in streaming pipelines without breaking downstream consumers? 7. How do you design a pipeline to safely handle late-arriving data? 8. How would you isolate, audit, and reprocess corrupt or bad records in a streaming pipeline? 9. How does Unity Catalog enable enterprise-level data governance in Databricks? 10. Why are organizations migrating from Hive Metastore to Unity Catalog, and what challenges need to be addressed during migration? 11. What happens when a streaming job fails or restarts, and how is data consistency maintained? 12. How would you optimize Delta tables written by high-throughput streaming jobs? 13. How do you design a streaming architecture that supports multiple consumers with different SLA requirements? 14. Why are Microsoft Preview features considered an advantage for enterprise teams using Azure Databricks? 15. How would you ensure data quality so that only trusted data reaches Gold tables in near real time? #Databricks #DataEngineering #ApacheSpark #DeltaLake #UnityCatalog #Lakehouse #StreamingData #AutoLoader #EventHubs #ADLSGen2 #DataArchitecture #DataPipeline #ETL #DataOps #RealTimeAnalytics #DataGovernance #SchemaEvolution #Checkpointing #ExactlyOnceProcessing #DataQuality #CloudComputing #AnalyticsEngineering #ModernDataStack #BigData #DataLakehouse #StreamingArchitecture #DataReprocessing #PerformanceTuning #DataEngineer #InterviewPreparation

Just shipped my data engineering project on Azure + Databricks. ShopStream — a retail data platform that ingests real-time order events and batch data, processes them through a medallion architecture (bronze → silver → gold), and serves aggregated metrics for reporting. Stack: Azure Event Hubs (real-time streaming) + ADLS Gen2 (storage) Databricks Structured Streaming + Auto Loader (ingestion) Delta Lake + Unity Catalog (governance) PySpark (transformations — SCD2, fact tables, aggregations) Databricks Asset Bundles (infrastructure as code) All pipelines are version-controlled and deployed via CLI — no manual UI. Code: https://lnkd.in/e8ets-9Y #DataEngineering #Databricks #Azure #DeltaLake #PySpark #Portfolio

Everyone talks about Spark, clusters, and performance tuning in Databricks… But some of the real power lies in the features that barely get attention . Here are a few underrated things that can seriously level up your workflows: • Cluster Policies → enforce standards without slowing teams • SQL Warehouses → super fast analytics without cluster headaches • Delta Live Tables → built-in data quality + pipeline automation • Auto Optimize & Z-Order → performance boost without manual tuning • Job Task Values → cleaner workflows, less glue code • Data Skipping → faster queries on large datasets (quietly powerful) • Secret Scopes → secure configs the right way Most teams ignore these until scaling becomes painful. The truth? You don’t always need more compute — you need better usage of what already exists. What’s one Databricks feature you discovered late but now can’t live without? #Databricks #DataEngineering #BigData #CloudComputing #Azure #DataPlatform

What I’m Realizing About Data Engineering Over the last few weeks, one thing has become very clear to me: Modern Data Engineering is no longer just about writing ETL pipelines. It’s evolving into a combination of: ✔ Distributed Computing ✔ Governance & Metadata Management ✔ Real-time Processing ✔ Lakehouse Architecture ✔ Scalability & Cost Optimization As I continue learning tools like Microsoft Fabric, PySpark, Kafka, Delta Lake, and Azure services, I’m starting to understand how important it is to build systems that are not only functional — but also scalable, reliable, and maintainable. A few concepts I’m currently exploring deeper: 🔹 Medallion Architecture 🔹 Streaming Pipelines with Kafka 🔹 Spark Optimization Techniques 🔹 Data Modeling & Partitioning 🔹 Unity Catalog & Governance 🔹 Real-world ETL Design Patterns One thing I genuinely enjoy about this field is that there’s always something new to learn. Every session, project, and discussion adds another layer of understanding. Looking forward to building more hands-on projects and learning from the community. 🚀 #DataEngineering #PySpark #MicrosoftFabric #Azure #Databricks #BigData #Kafka #DeltaLake #snowflake

Every data engineer has that one pipeline. The one that "just works" but nobody really understands anymore. The one that breaks at 2am. A lot of this week's Databricks updates are quietly aimed at killing exactly that kind of setup 🔥 . No more duct-taping your OLTP and OLAP pipelines together. No more "which runtime version was stable again?" New episode of the Release Notes Podcast is out! 🎙️ Here's what landed: 💠 Lakehouse Sync bridges your operational Lakebase database directly to Delta Lake via CDC — finally, one platform for both transactional and analytical workloads, no custom ingestion layer needed. 💠 Runtime versioning gets a makeover from v19 onwards: one version, auto-promoted to GA, 3-year LTS. Simpler, more predictable, easier to govern. 💠 Automatic Identity Management with Entra ID and Okta means onboarding a business user to a Genie Space is now a one-click operation instead of a SCIM configuration nightmare. 💠 Genie gets smarter — you can now define workspace-level instructions so your AI assistant actually speaks your company's language across every chat. 👉 On top of that: new managed connectors (HubSpot GA, Outlook/GitHub/Smartsheet in beta), uv pip support in serverless notebooks for faster cold starts, Qwen 3.5 122B available as a hosted model, and Catalog Managed Commits hitting GA for proper multi-table transaction governance. A lot moved from preview to production this cycle. ➡️ Worth a listen 🎙️: https://lnkd.in/dXd8FPRE #Databricks #DataEngineering #DataPlatform #ReleaseNotes