Michelle Winters

The Databricks Startups team answers founders’ most common AI questions—where to start without hype, how to stay compliant with sensitive data, what to build versus buy, how to cut hallucinations, and how to keep costs in check. 💡 Get step‑by‑step starting points, governance patterns for sensitive data, a pragmatic build‑versus‑buy lens, tactics to curb hallucinations, and cost controls that scale. 🔗 Full post: https://lnkd.in/ehRa3Yp5 #Databricks #DatabricksForStartups #AI #GenAI #MLOps

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"All-in-One" Suite 🆚 "Best-of-Breed" Approach Most organizations aren’t 100% on any single platform. Heterogeneity is the norm. That’s why metadata freedom is becoming a critical architectural discussion. 👉 Full read: https://lnkd.in/gHnrPvxd In this article, we break down: • why vendor lock-in matters more at the metadata layer • how multi-engine environments change governance • the tradeoffs between open catalogs and vertically integrated platforms This topic affects every engineering team operating across Spark, Trino, Flink, warehouses, vector stores, and AI systems.

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Databricks isn’t the problem. The missing orchestration layer is. Even with millions invested, too many teams end up with: 😱 Endless engineering backlogs 😱 Fragile, inconsistent pipelines 😱 Stakeholders shut out of the process The result? Zero trust. Zero impact. In our free whitepaper, we break down the 5-step action plan to turn Databricks into real business value. Link in the comments ⬇️

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There's one key challenge the new Open Semantic Interchange (OSI) from Snowflake & others doesn't solve.  ❎ The integration issue. Don't get me wrong, I'm thrilled about the OSI. But no matter how much this initiative improves the translation of data between systems, the integration piece is still required to take action based on the knowledge (i.e. what the data means).  That's true for all industries.  In my industry specifically (retail), we have some big integration issues. Disconnected systems are everywhere. I wrote more about the OSI and its potential impact on AI readiness over on Substack (link in comments). How do you think the OSI and data standardization in general will affect industries like retail in the coming years? Let me know ⬇️

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Databricks just shipped Zerobus Ingest to GA. The pitch: stream data directly into Delta tables, skip the Kafka cluster entirely. Sub-5-second latency. 10+ GB/sec throughput. For teams doing high-volume sensor and telemetry ingest, this is interesting. Not because Kafka is bad (it's not), but because most IoT data pipelines don't actually need pub/sub fanout. They need a fast, reliable pipe from source to storage. And operating Kafka for that is like renting a warehouse to store a bicycle. https://lnkd.in/guvXmJTJ We see this pattern constantly at Tiger Data. Teams running industrial IoT workloads with millions of sensor readings per second, and half their operational headaches aren't the database. They're the middleware. The message bus. The glue code between systems that shouldn't need glue code. This is why we built Tiger Data Cloud around direct ingest into TimescaleDB. The less stuff between your sensors and your database, the fewer things break at 2am. Streaming architecture is getting simpler. That's a good thing. #IoT #TimeSeries #DataEngineering #Streaming #TigerData

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New research from Databricks. It's about training enterprise search agents via RL. KARL introduces a multi-task RL approach where agents are trained across heterogeneous search behaviors, constraint-driven entity search, cross-document synthesis, and tabular reasoning. It generalizes substantially better than those optimized for any single benchmark. KARL is Pareto-optimal on both cost-quality and latency-quality trade-offs compared to Claude 4.6 and GPT 5.2. With sufficient test-time compute, it surpasses the strongest closed models while being more cost efficient.

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Cross Posting #Databricks CEO Just Dropped The Most Honest Advice About The Future Of AI The next wave of AI success is going to be completely dependent on structured data. Maybe that’s a no-brainer to you. Maybe I’m telling you that the next time you take a drink of water, that water will be wet. They don’t say things like all software developers will be replaced by AI (the deadline for which has since passed) or all tech employees should become baristas and bartenders (the dream is over, nerds) or don’t worry, we’ve hammered out all the unintended consequences for letting users create their own AI At the same time, AI is, theoretically anyway, offering executives and leaders actionable insights, options, and answers. AI Is Motion, Data Is Experience AI is fire, while data is fuel. https://lnkd.in/gqcRq6vY

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How to $30k Monthly In 2026: Compete with Palantir, Google Cloud and Snowflake Using AI This **eye-opening video reveals detailed, practical strategies you can use right now to transform your existing data assets into predictable, recurring income streams, leveraging AI and automation to operate at an enterprise level—even if you’re a solo entrepreneur or small team. Get 100 Free Leads Month at www.datatoleads.com Gone are the days when you needed a massive IT budget and teams of analysts to monetize data. With the right approach and tools, you can now compete with giants like Palantir, Snowflake, and Google Cloud, building your own data commerce empire while keeping overhead low and profits high. ✅ Why traditional big data solutions (Snowflake, Palantir, Google Cloud) were designed for large enterprises—and how 2026’s advancements let small businesses and individual entrepreneurs play at the same level. ✅ How platforms like AvocaData allow you to access, manage, enrich, and resell data without needing complex infrastructure, technical architecture, or advanced coding skills. ✅ How AI-powered automation can replace expensive teams while boosting your capacity to enrich, clean, and package high-value datasets. ✅ Actionable methods to build your own data marketplace or create a white-labeled data resale system for agencies, lead gen companies, and SaaS clients. ✅ Why 2026 is the perfect year to ride the data monetization wave, positioning yourself ahead of the competition and building a long-term, scalable income stream. ✅ Step-by-step frameworks to enrich datasets, resell aged leads, bundle high-demand niches, and leverage AI models for predictive targeting. ✅ How to launch your data monetization business without writing a single line of code, managing complicated ETL pipelines, or requiring enterprise-level funding. With AI automation and advanced platforms like AvocaData, you can: ✔️ Monetize aged leads, phone lists, skip-traced databases, and niche appointment data effortlessly. ✔️ Build predictable recurring revenue while helping businesses lower their customer acquisition costs. ✔️ Enrich and cleanse data to increase its resale value in the mar

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