How Databricks enables Data Mesh with Delta Lake, Unity Catalog, and MLflow

Still juggling manual scripts and siloed pipelines? You’re not alone. Many enterprise data teams hit a wall when scaling analytics: delayed insights, compliance risks, and stalled AI initiatives become the norm. That’s exactly where Databricks Workflows changes the game. As a trusted Databricks Partner, Optimum helps enterprises orchestrate and automate analytics pipelines across the Databricks Lakehouse Platform to unify data operations, enforce governance, and accelerate decision-making at scale. If your team is ready to move beyond fragmented processes and build an automated, governed analytics environment, our latest blog breaks down how Databricks Workflows can help you get there. Read the full post to see how your analytics operations can scale with Databricks Workflows: https://lnkd.in/g9VQNWsz #DatabricksPartner #DataGovernance #DatabricksWorkflows #EnterpriseAnalytics

Delta Lake in Databricks – Key Capabilities Traditional data lakes provide scalability but often face challenges such as inconsistent data, lack of schema enforcement, and poor query performance. Delta Lake addresses these limitations by adding a reliability layer on top of existing data lakes, enabling a true lakehouse architecture. ACID Transactions – Ensures consistency and reliability during concurrent reads and writes. Schema Enforcement & Evolution – Prevents ingestion of bad data and adapts to changing structures. Time Travel – Enables access to historical versions of data for audits and recovery. Performance Optimization – Features like auto-compaction and Z-ordering improve query efficiency. Delta Lake provides a unified approach to storing, managing, and processing data for analytics, AI, and real-time insights. #Databricks #DeltaLake #Lakehouse #DataEngineering

Databricks Partner Tech Summit FY26 📅 Sep 9–11, 2025 Today was packed with insights, innovation, and inspiring conversations — 14 sessions in total! Here are some highlights from the sessions I attended on Day Day 1 🔹 Databricks Vision & Strategy – future roadmap and impact on partners & customers. 🔹 Lakebase / OLTP - Nikita Shamgunov – exciting developments in transactional workloads on the Lakehouse. 🔹 Agent Bricks - Kasey Uhlenhuth – unleashing the power of intelligent agents in Databricks. 🔹 Lakeflow: A Unified Data Engineering Solution Shrikanth Shankar – simplifying pipelines with a unified approach. 🔹 Lakehouse EDW Features & Migrations Shant Hovsepian – strategies for smooth migrations & enterprise features. 🔹 Agent Frameworks & Compound AI Systems: Building and Scaling Intelligent Applications Robert Mosley – shaping next-gen intelligent applications with agent-based frameworks. 🔹 Data Catalogs & Governance: UC Business Semantics Can Efeoglu – evolving Unity Catalog for smarter governance and semantics. 🔹 Palantir Partnership – Technical Aspects Justin Burks & Benjamin Abood – unlocking enterprise use cases together. 🔹 Advanced Cost Control & Budget Management – strategies for optimization across the Databricks platform. A truly inspiring start — can’t wait for Days 2 & 3! #Databricks #PartnerTechSummit #AI #Lakehouse #DataGovernance #Innovation #DatabricksMVP

80% of companies collect massive amounts of data, but less than 20% manage to turn it into real business value. We see this gap every day as businesses spend heavily on storage, pipelines, and dashboards, yet without scalable and reliable data infrastructure, critical decisions remain guesswork. At Infinytics.ai, we help bridge this gap by building modern data stacks with tools like Snowflake, BigQuery, and Databricks, automating workflows using Airflow and dbt, and optimizing pipelines for both cost and speed. The real question is, where does your biggest challenge lie: collecting the right data, cleaning it, or converting it into revenue-driving insights?

🔐 Delta Sharing – The Secure Data Collaboration One of the most powerful features in the Databricks ecosystem is Delta Sharing. But what makes it special?   💡 What is Delta Sharing? It’s an open-source protocol that allows secure data sharing — not just inside Databricks, but across organizations, platforms, and even without Databricks. With Delta Sharing you can share: 📊 Tables 📂 Files & datasets 📒 Notebooks All in a governed, secure, and real-time way.   🔹 Why it matters Works with Databricks users & non-Databricks users alike Removes the pain of copying/moving large datasets Enables true cross-org data collaboration without vendor lock-in Backed by open standards → no “closed garden”   ✨ In short: Delta Sharing breaks silos. It lets teams, partners, and systems work off the same single source of truth — securely and at scale. That’s a big step towards the future of open, governed data ecosystems. #AzureDataEngineering #DeltaLake #Databricks #DeltaSharing #BigData #DataCollaboration #DataGovernance

Feeling energized after attending the Databricks Data + AI Summit 2025! 🚀 📎 This year's conference truly showcased the future of enterprise data engineering and governance. 🏢 📌 One standout for me was the ‘Introduction to Lakeflow’ session. Having worked extensively with Airflow, I was impressed by how Lakeflow brings declarative, end-to-end orchestration directly to Databricks. Lakeflow Connect and Lakeflow Jobs felt like a natural evolution, simplifying data pipeline design and making scheduling far more intuitive than traditional workflow tools. I especially appreciated the real-world examples of how Lakeflow is powering innovation across industries—from real-time decision-making to robust analytics. 🔗 📌 Equally exciting were the latest Unity Catalog features. Unity Catalog now supports both Delta Lake and Apache Iceberg, truly unifying governance and interoperability across clouds and engines. The new attribute-based access control (ABAC) and automated data classification make secure, scalable data management feel seamless. These enhancements are set to transform data stewardship and compliance, putting robust auditing, lineage, and discovery into every workspace. The idea of certified KPIs being natively managed in Unity Catalog is something I see fundamentally changing how business metrics are tracked and shared within teams. 📊 🤝 Connecting with fellow data professionals and discussing the rapid pace of innovation—especially around tools that bridge the gap between data engineering and AI—has been inspiring. Grateful to Databricks and the fantastic presenters for raising the bar yet again! 🙌 🙏 Also want to thank Tata Technologies for these opportunities. Sincere gratitude to Nidhish Shah for their ongoing encouragement and support—your mentorship made this possible! 👏 #DataAISummit #Databricks #Lakeflow #UnityCatalog #DataEngineering #AIInnovation #DeltaLake #tatatechnologies #tatamotors

🚀 Unlocking the True Power of Data with Databricks Metastore + Unity Catalog In the modern data landscape, data is only as valuable as it is discoverable, governed, and shareable. Yet, too many organizations are still trapped in data silos, struggling with inconsistent governance and duplicated effort. That’s where Databricks Metastore — supercharged by Unity Catalog — changes the game. 💡 Why it matters: - Centralized Metadata Management → One source of truth for all your tables, views, and volumes across workspaces. - Cross-Workspace Data Sharing → Break down silos and enable secure, governed access at scale. - Three-Level Namespace → Organize data with Catalog → Schema → Table for clarity and scalability. - Governance at Scale → Fine-grained permissions, audit trails, and compliance baked in. - Delta Lake Integration → ACID transactions + performance optimizations for reliable analytics. 📊 The Business Impact: - Faster onboarding for data teams — no more “Where’s the data?” - Reduced compliance risk with consistent governance policies. - Accelerated analytics and AI adoption through trusted, discoverable datasets. 🔍 Pro Tip: If you’re still relying on workspace-level Hive metastores, you’re leaving collaboration, governance, and scalability on the table. Migrating to Unity Catalog’s regional Metastore is not just a tech upgrade — it’s a strategic business move. 💬 Question for you: How is your organization managing metadata today — and is it helping or hindering your data strategy? #Databricks #UnityCatalog #DataGovernance #DeltaLake #DataEngineering #MetadataManagement #BigData #DataStrategy

𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗨𝗻𝗶𝘁𝘆 𝗖𝗮𝘁𝗮𝗹𝗼𝗴 In modern data platforms, data governance is just as important as data processing. That’s where Databricks Unity Catalog (UC) comes in. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝘀𝗶𝗻𝗴𝗹𝗲 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗹𝗮𝘆𝗲𝗿 𝘁𝗵𝗮𝘁:  • Centralizes permissions across workspaces & clouds  • Provides fine-grained access control (catalog, schema, table, column, row)  • Tracks data lineage for compliance & trust  • Governs not just tables but also files, ML models & dashboards  • Enables secure data sharing with external partners (Delta Sharing) 𝗨𝗻𝗶𝘁𝘆 𝗖𝗮𝘁𝗮𝗹𝗼𝗴 𝗮𝘀 𝗮 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗼𝗯𝗷𝗲𝗰𝘁𝘀: Catalog → Schema → Table → Column  • Catalog = Top-level container (e.g., “Sales_Data”)  • Schema = Groups related objects (e.g., “Marketing”, “Finance”)  • Table = Actual dataset (e.g., “Customer_Transactions”)  • Column = Fine-grained control (e.g., hide PII, mask SSN) This structure gives clarity + security, ensuring the right people access the right data at the right level. With Unity Catalog, teams spend less time managing permissions and more time unlocking insights — securely. #Databricks #UnityCatalog #DataGovernance #BigData #DataEngineering #ETL #DeltaLake

Pleased to have earned the Databricks Academy Accreditation – Databricks Fundamentals. This accreditation validates my understanding of the Databricks Data Intelligence Platform, including: - Databricks architecture and the Data Lakehouse concept - Security, governance, and key platform features - The role of Databricks in unifying data and AI A solid foundation as I continue to explore the Databricks ecosystem. #DatabricksLearning #Databricks

Auto-Optimisation in Databricks: A Smarter Alternative to OPTIMIZE Manually running OPTIMIZE on Delta tables has been a standard practice for improving query performance. But as data grows, this approach can become costly and hard to maintain. That’s where Databricks Auto-Optimisation comes in. Instead of worrying about when and how to run OPTIMIZE, Auto-Optimisation handles it behind the scenes—intelligently and continuously. In my latest article, I break down: ✅The challenges with manual OPTIMIZE ✅ How Auto-Optimisation works under the hood ✅ Why it’s a game-changer for performance & cost management ✅ When to use Auto-Optimisation vs. manual tuning ✅You can enable it at the Spark session level, table level or cluster level. Article Link: https://lnkd.in/gYPKrSam #Databricks #DeltaLake #DataEngineering #AutoOptimisation #BigData Databricks Azure Developer Community Microsoft DATAENGINEER.space #DataEngineering #optimise #autooptimise #autocompact