Darshan Pandya

Great news for Spanner and BigQuery users. Datastream now provides a seamless, serverless bridge from Spanner’s transactional power to the high-performance analytical capabilities of BigQuery and BigLake. Get the most scalable and available database that runs Google with BigQuery and BigLake’s serverless AI-Native Lakehouse!

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Operational and analytics capabilities—without the burden of managing servers or pipelines, and with flexibility to choose which operational data becomes analytical.

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Snowflake signaling its intent to acquire Observe, Inc. shows how important observability has become to core data infrastructure. Logs, metrics, and traces are now: 👉 Some of the largest datasets teams generate 👉 Increasingly reused across analytics and AI 👉 Too expensive and too valuable to treat as disposable This move suggests the next phase of the data stack will continue to bring high-volume data and metadata closer to the analytics layer. As that happens, storage and data architecture choices become increasingly important. https://lnkd.in/ez73Fhbr

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This is a clear example of AI infused into the data platform. Partitioning, Z order, V order... they all require knowing the shape of your data and how it will be used in advance. But you don't know. The world is being flipped on its head. Liquid Clustering adapts to use. Your data platform needs to evolve with you.

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Snowflake is releasing so many AI features lately, it's really hard to keep track of them all. For AI Agents, evaluation is very important to improve their performance. But setting up an evaluation system yourself takes a lot of work. Fortunately, the Evaluation feature for Snowflake's Cortex Agent is now in GA! If users just need to prepare evaluation data they can utilize this built-in feature to run continuous evaluations. In my blog post, I explain how this feature basically works, and also share my favorite part: how to run it and get the evaluation results using just SQL. [medium]: https://lnkd.in/gyj-Qu9n [zenn]: https://lnkd.in/gzMV2k_P

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Most PostgreSQL conversations still start in the wrong place. They start with features. Benchmarks. Migration mechanics. Architecture diagrams. Important, yes. But in real enterprise environments, the harder question is usually this: Can your PostgreSQL platform change without making your team nervous? That’s the thread I pulled on in this blog post. It comes from a strategy discussion I was part of late last year, where the most valuable part of the conversation was not “what PostgreSQL can do.” It was: • how to keep upgrades from becoming drama • how to think about HA/DR without creating operational ambiguity • how to evaluate Aurora migration through the lens of control • how to reduce monitoring noise and false alerts • how to make patching, tuning, and change feel more routine My belief is simple: A mature PostgreSQL platform is not defined only by capability. It is defined by how calmly it can evolve. In the post, I break that down into practical areas: • PostgreSQL 18 and reachable upgrades • HA/DR and distributed clarity • Aurora migration checkpoints • observability that improves decisions • and a simple framework for calmer PostgreSQL operations If you work with PostgreSQL in production, I think this will resonate. #PostgreSQL #DatabaseReliability #PlatformEngineering #SRE #Observability #Modernization

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Databricks and Perplexity co-founder Andy Konwinski is forming the Laude Institute, a new AI research institute, and backing it with $100 million of his own money. The Laude Institute will function less as a traditional research lab and more like a fund making investments structured as grants. It will focus on "Slingshots" (early-stage research) and "Moonshots" (long-horizon labs tackling species-level challenges). The institute's board includes AI luminaries like UC Berkeley's Dave Patterson, Google's Jeff Dean, and Meta's Joelle Pineau. The institute announced its first flagship grant of $3 million annually for five years to anchor the new AI Systems Lab at UC Berkeley. The new fund aims to catalyze work that doesn't just push the field of AI forward, but guides it toward more beneficial outcomes. Congratulations to Andy Konwinski on this incredible commitment to AI research. TechCrunch: https://lnkd.in/dy7x9zAT #AI #Research #AIforGood #Philanthropy #VenturePhilanthropy #Databricks #Perplexity #UCBerkeley

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