How Databricks Workflows can unify your analytics operations

Data Mesh is at the forefront of revolutionizing data platforms. In my latest Medium article, I delve into how Databricks, with its offerings like Delta Lake, Unity Catalog, and MLflow, plays a pivotal role in bringing Data Mesh to life with scalability and governance. Explore the details here: #DataMesh #Databricks

Databricks closes $1B round and reports a $4B revenue run rate Trend: Databricks closed a $1 billion Series K and reported a roughly $4 billion annualized revenue run rate as demand for data and AI platforms grows. Why it matters: Large private rounds and rising revenue show enterprise budgets shifting to unified data and ML platforms, and signal competition for integrated AI operational stacks. Question: If your team had one unified data and AI platform, which single workflow would you prioritize migrating first and why? 🔁 Repost if unified data platforms accelerate AI delivery 🔔 Follow me for signals about operationalizing data and models 🌟 Takeaway: Platform consolidation can cut ops overhead but requires migration plans

For many organisations, the premium cost of data and analytics engineering is a significant barrier to scaling their initiatives on Databricks. meshynix offers a direct solution! We are a purpose-built Databricks partner that helps organisations fundamentally reduce their operational spend without compromising on quality. Our combination of deep Databricks expertise and a cost-effective global delivery model is designed to deliver immediate financial impact for your Data and AI initiatives. This isn't about cutting corners, it's about being smart with your data engineering budget. Data & AI budget should fund engineering excellence, not a complex consulting hierarchy. Too often, organisations pay a premium for the structure of their consulting partners, not just for the expertise they provide. This leads to budget leakage and a lower return on investment. At meshynix, we've eliminated this inefficiency with our ladderless and lean structure. Ready to see how our expert Databricks engineers can help you achieve your data and ai outcomes more affordably? Reach out to us today. #Databricks #CostSavings #DataAnalytics #DataEngineering #Meshynix

🔍 Enhanced Data Governance with Databricks Unity Catalog 🔐 In the modern data landscape, secure, unified, and auditable data governance is essential for driving trusted insights and ensuring compliance. Databricks Unity Catalog is setting new expectations by centralizing governance for all data and AI assets—across clouds and platforms. ✨ What sets Unity Catalog apart? Unified Data View: Gain a single, consolidated view across data lakes, warehouses, and AI models, streamlining discovery and reducing data sprawl. Fine-Grained Access Control: Leverage role-based permissions and column/row-level masking to safeguard sensitive information. Automated Data Lineage: Visualize end-to-end lineage for all workloads—track data flow for transparency, troubleshooting, and compliance. Auditing & Compliance: Built-in, detailed audit logs and policy enforcement help meet regulatory requirements with ease. Open Standards & Integration: As an open-source-first solution, Unity Catalog integrates natively with Databricks, Azure Data Factory, and BI tools like Power BI and Tableau. Unity Catalog empowers data teams to govern with confidence, collaborate securely, and accelerate innovation—making the Lakehouse truly enterprise-ready! 💡 Are you leveraging next-gen data governance in your organization? Let’s discuss best practices! #Databricks #UnityCatalog #DataGovernance #Azure #Lakehouse

This Microsoft-backed analytics platform is rapidly transforming organizations and is poised to become a primary asset for analytics teams in the near future.  Its intuitive graphical user interface, advanced features, serverless architecture, and powerful 'GENIE' (an AI-driven chat assistant for organizational data) are fully optimized and production-ready. With the most practical approach to Clients problems, this Business oriented analytical capability should be a go-to tool for independently delivering actionable insights without going into technical expertise. #databricks #microsoft #insights #analytics #dataanalyst

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?

Databricks 𝗠𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲 + 𝗗𝗲𝗹𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 = 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻𝗹𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝗲𝘀𝘀 The Databricks Marketplace isn’t just a catalog of datasets and AI models; it’s powered by Delta Sharing, an open protocol that enables seamless data exchange. ⚡ 𝗪𝗵𝗮𝘁 𝗗𝗲𝗹𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗱𝗼𝗲𝘀:  • Let's you share live data securely across clouds & platforms.  • Removes the need for copying or moving files.  • Works with any tool that supports open standards. 📈 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲:  • Providers can deliver up-to-date, ready-to-use data products.  • Consumers get instant access to their existing tools.  • Teams collaborate without worrying about format lock-in. In practice, this means faster time to insights, lower integration overhead, and a smoother path to building data-driven products. If you’re exploring the Databricks Marketplace, think of Delta Sharing as the engine making the ecosystem actually work. 𝙍𝙚𝙖𝙙 𝙢𝙤𝙧𝙚 𝙝𝙚𝙧𝙚: https://lnkd.in/d5Tsz8Rw #Databricks #DeltaSharing #Lakehouse #Marketplace #DataEngineering

Across all the latest roadmaps — from Databricks to Snowflake — 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗹𝘂𝗺𝗻 𝗱𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻𝘀 have become a default capability. It’s no longer innovation. 𝘐𝘵’𝘴 𝘫𝘶𝘴𝘵 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦. And yes, it’s an accelerator. It reduces friction, improves catalog readability, and speeds up onboarding. But it’s also a 𝗿𝗲𝗱 𝗳𝗹𝗮𝗴. Because it risks becoming yet another excuse — not just for missing documentation, but for a deeper issue: a lack of vision. If you're profiling data after ingestion, and relying on AI to "guess" what the columns mean, that’s not smart. That’s reactive. 𝗧𝗵𝗮𝘁’𝘀 𝗮𝗱𝗺𝗶𝘁𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗴𝗼𝘁 𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝘆𝗼𝗻𝗲 𝗸𝗻𝗲𝘄 𝘄𝗵𝘆. It’s like closing the barn after the cattle have run off — or worse, reopening the gate just to let garbage back in. Metadata automation is helpful. But it’s not a substitute for ownership, purpose, and design. And it’s definitely not a strategy. #DataGovernance #MetadataMatters #DataProductThinking #CloudNative #AIandData #DataQuality #DataStrategy #StopFeedingTheGarbage

Do the work! As Francesco De Cassai points out, putting the investment of time and energy into your metadata pays off. It shouldn’t be delegated.

In my ongoing efforts to strengthen the data infrastructure at QISTA, I came across Databricks, a powerful platform that brings together everything data in one place: ✅ Data ingestion & storage ✅ ETL & pipeline orchestration ✅ Advanced analytics & BI ✅ Machine learning & AI use cases ✅ Real-time collaboration and much more The immediately caught my attention, so I decided to roll up my sleeves and dive in. First milestone achieved: I have completed the Databricks Foundational Accreditation. Next steps? Exploring how to: - Build robust data pipelines, - Experiment with AI agents (important for CAC AI Solutions), - Unlock BI & AI-driven insights for real impact. I am so motivated and excited about this journey of mastering and using Databricks.