Matthew Wooller

Data lakes have always been reliable for storing individual files. What was missing, however, was the reliability at the table/analytics layer, transactional guarantees and consistency that only a database-style abstraction can provide. In Karthic Rao's new post, drawing on 40 years of #database wisdom, we cover: - Why tables still matter - Where early #datalakes went wrong - How Apache Iceberg brings trust back - Why on-prem #objectstorage gives you an edge Check out the full piece: https://lnkd.in/gxcAqf6M

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Big news in the open-source AI world. The launch of gpt-oss by OpenAI — running natively on Databricks — feels like a pivotal moment in the GenAI space. This isn’t just about bigger models. It’s about better control, clearer governance, and real enterprise readiness. gpt-oss sets a new benchmark for what open language models can do — From advanced reasoning to seamless data integration. And when it runs on Databricks, it plays natively with your data, with full lineage and compliance baked in. Try both the 20B and 120B models today in the Mosaic AI Playground: https://lnkd.in/gaSQC3wH This is a big step toward making GenAI more transparent, secure, and customizable for every team — not just tech giants. Let me know what you build with it. Curious to see the ideas this unlocks. #OpenSourceAI #gptOSS #MosaicAI #Databricks #GenAI #EnterpriseAI #BuildersMindset #DataPattern

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Tired of complex workflows to move data from Dropbox to Snowflake? With Openflow, you can set up a secure, scalable pipeline in just a few clicks, no heavy coding required. Learn how in #DataSuperhero Maja Ferle's blog: https://lnkd.in/gic3fCTK 

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Building a reliable RAG system doesn’t stop at retrieval and generation, you need observability too. In the Retrieval Augmented Generation course, you'll explore how LLM observability platforms can help you: - Trace prompts through each step of the pipeline - Log and evaluate component behavior - Run experiments and monitor system performance over time The lesson uses Phoenix (by Arize) as an open-source example, but the techniques apply broadly, and you'll also learn where traditional tools like Grafana or Datadog fit in. 🧠 Learn how to monitor and improve your RAG systems in the full course: https://hubs.la/Q03D1ZLp0

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Uneasy about the changes Oracle is implementing with #MySQL? If you're looking for a return to open source values and robust innovation, make the switch to #MariaDB: ✅ Gain performance improvements ✅ Unlock AI workloads with MariaDB's native vector search ✅ Optimize for analytical workloads with columnar analytics ✅ Simple migration process due to compatibility with MySQL syntax and protocols Download our side-by-side comparison guide and solidify your future with MariaDB. 💪 🔗 http://bit.ly/4pqMWnh

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The one-line fix that solved PostgreSQL's 'unkillable' query mystery. Our ClickPipes team ran into a tricky upstream issue and helped track down the cause 🦸 🔸 Customer PostgreSQL read replicas had queries that ignored kill signals 🔸 Logical replication slot creation would hang for hours, forcing database restarts 🔸 Investigation revealed missing interrupt handling in PostgreSQL's wait loops 🔸 Contributed a simple fix that PostgreSQL maintainers quickly accepted

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Automating data access in PostgreSQL environments is no longer a nice-to-have–it's becoming essential as data volumes and user demands scale fast. Our latest blog breaks down how Immuta's PostgreSQL integration helps teams move away from manual grants and toward secure, scalable, policy-based access–built for both human users and AI agents. Read it here 🔗: https://ow.ly/Gvik50WmcTi

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Scale time series forecasting across any DataFrame format! Most time series libraries lock you into a single DataFrame framework, forcing painful migrations when scaling up. Switching frameworks usually means rewriting data loading, preprocessing, and model interfaces with tons of boilerplate code. TimeGPT eliminates DataFrame boundaries with universal compatibility. The method signatures of forecast are the same across all frameworks. Works seamlessly with: pandas, Polars, Dask, Spark, Ray. #TimeSeries #TimeGPT #DataScience

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