Haider Rizvi

great post on the intersection of AI workloads and Open Container Initiative "The State of OCI Artifacts for AI/ML" https://lnkd.in/gbXZykMq

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Just read “Why Metadata Is the New Interface Between IT and AI” by Krishna Subramanian on BigDataWire. Great insights from the author about how metadata is becoming the bridge between raw systems and intelligent models. Rather than forcing huge document ingestion or monolithic pipelines that need to stay in sync and strain your infra, metadata gives AI a map to work with, letting models pick what matters rather than going over all the data blindly. At Nuklai, we built Nexus with exactly that philosophy: - Leverage metadata-driven retrieval so models use context, not bulk data from slow and imperfect RAG systems. - Connect the data instead of ETL or duplication - Provide traceability and governance so outcome can be verified - Allow natural language queries that are efficient and trustworthy When metadata is the interface, AI finally gets context and trust. (Link in comment) #NuklaiNexus

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With AI adoption and deeper integration into enterprise products, the world moves toward being collectively intelligent. First movers will have a distinct advantage. Fully backward-integrated AI companies are best positioned to win on economics.

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We ran Rama against the TPC-C benchmark with great results: - 40% less AWS cost than CockroachDB for the same throughput - Much tighter latency distribution than CockroachDB for each transaction (3x variance compared to 400x for writes) https://lnkd.in/gujinSnk

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Excited to share a new blog that I co-authored with Sachin Kulkarni and Sayan Pal, now published on Medium! We explored how organizations can build a governed Data Product Marketplace using automated metadata enrichment powered by IBM watsonx.data. The blog covers key architecture concepts, governance practices, automation workflows, and how watsonx.data simplifies scaling enterprise data platforms. If you’re working with data engineering, governance, or AI adoption, this article may help you gain clarity on modern data product strategies. #IBM #WatsonxData #DataGovernance #DataMarketplace #MetadataManagement #DataProducts #AI #DataEngineering

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ICYMI Episode 5 of AI Now is available on-demand. Tune in to learn how your business can take advantage of the latest AI innovations to fuel productivity and navigate disruption. https://lnkd.in/gewaCdDm

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𝗜𝘁'𝘀 𝗵𝗮𝗿𝗱 𝘁𝗼 𝘀𝗲𝗲 𝗖𝗹𝗼𝘂𝗱𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗗𝗮𝗿𝗸 - 𝗪𝗵𝘆 Redis 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝗼𝘂𝗱 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗪𝗵𝗲𝗻 𝘁𝗵𝗲 𝗖𝗹𝗼𝘂𝗱 𝗚𝗼𝗲𝘀 𝗗𝗮𝗿𝗸 When a major cloud goes down, so do the apps, sessions & experiences that depend on it. Outages cost revenue, trust & time. Cloud-native services like ElastiCache or Memorystore work — until their cloud doesn’t. They’re locked to a single provider, with no easy failover across regions or clouds. One outage can take everything with it. That’s why multi-cloud resilience is no longer optional. With Redis’s Active-Active architecture, data stays synchronized across clouds and regions — automatically. - Zero downtime - Seamless failover - Consistent, low-latency performance everywhere When uptime is mission-critical, Redis Multi-Cloud gives you the control & confidence to stay online — no matter who’s offline. Because “always on” should mean always ready for the unexpected. #Redis #MultiCloud #HighAvailability #Resilience #CloudComputing #DevOps https://lnkd.in/emwFT4Xb

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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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Although it's been a while since we blogged about SOTA on the BIRD SQL benchmark, here's a recent chat with my fellows at Emergence AI, Ayan Kumar Bhowmick and Jeremy H., hosted by Vivek Haldar

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EDB Day Session — Part 1: Calm Operations Are the New Performance On Monday, during an EDB Day session, something familiar happened. Different roles. Different priorities. Same underlying truth. It all converged on one line about enterprise PostgreSQL 18 and EPAS 18: Features matter.  But operational confidence decides velocity. PostgreSQL wasn’t framed as “open source” or “cost savings.” It showed up as a platform promise: reliability, resilience, and change without fear. What stood out: almost nobody asked about raw performance anymore.  That chapter is mostly written, especially with PostgreSQL 18’s focus on async I/O, better monitoring, and safer-by-default operations like data checksums. Instead, the questions were sharper, heavier, more operational: • What’s new in PostgreSQL 18 / EPAS 18—and what does it actually unlock at enterprise scale? • How do PGD 6.x capabilities change the conversation on resilience and distributed reality across regions and clouds? • How do we think about migration paths from Aurora, while staying clear-eyed about AWS-managed constraints and operational tradeoffs? • How do we make patching and major upgrades uneventful—by design, not by weekend heroics? • How do we improve monitoring so we cut false alerts and only page humans when action is truly needed? • How do we time-block changes intelligently—based on workload patterns—so risk drops without slowing the business? Because the goal isn’t “more tooling.” The goal is fewer surprises. Here’s the belief that crystallized in that room: A modern data platform is no longer defined by the features it ships.  It’s defined by the changes it can absorb—calmly, repeatedly, safely. That’s why the conversation on the EDB PG AI Platform mattered—not as “AI hype,” but as a path to guided operations: smarter patching, cleaner signal-to-noise in monitoring, explainable optimization, and recommendations that respect workload windows across hybrid AWS + self-managed estates. This series will unpack the themes we explored: • PostgreSQL 18 / EPAS 18 – what’s new and what it unlocks for enterprises • PGD 6.x – resilience, scale, and distributed realities in the real world[enterprisedb +2] • Aurora migration thinking – what changes when you move off AWS-managed Postgres • Operational excellence – patching, monitoring, optimization, alert quality • Workload-aware time blocks – change windows aligned to real usage • EDB PG AI Platform – simplifying operations and making upgrades boring (on purpose) Part 1 begins here: modernization now moves at the speed of operational trust. The next posts will get practical—where most talks stay polite. Stay tuned. #EDB #PostgreSQL #EPAS #PGD #Aurora #AWS #CloudMigration #DataPlatform #PlatformEngineering #DatabaseReliability #SRE #Observability #AIOps #Modernization #EnterpriseIT

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