Darius Kemeklis

This foundational checklist from Augusto Rosa’s Snowflake Architecture Series is a must-read. Whether you're new to Snowflake or scaling your cloud data platform, this checklist breaks down the essential building blocks—like identity and access, cost governance, and data modeling—that every data team needs to get right. Set yourself up for long-term success with Snowflake—this is the perfect place to begin. https://lnkd.in/eYP8jZ2s

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Every data story has a journey, but too often, we lose track of it in the noise of pipelines, dashboards, and ETL logs. In this post, Sanyam Shah breaks down how to map end-to-end Data Lineage in Snowflake, turning metadata into visibility, compliance, and trust. If your goal is to make your data traceable, auditable, and AI-ready, this is a must-read. 👉 https://lnkd.in/gspE7Qs3 #BOTConsulting #DataLineage #Snowflake #DataGovernance #DataEngineering

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Data silos are one of the biggest roadblocks to efficient analytics and AI workflows. MinIO’s new Aistor Table Sharing is designed to tear down those barriers by enabling direct, on-premises data access from Databricks without copying or duplicating data. Key takeaways: • Aistor Table Sharing lets Databricks query on-prem MinIO data directly, eliminating data movement. • This approach reduces complexity, storage sprawl, and latency for hybrid and on-prem use cases. • By keeping data in place while supporting modern analytics and AI consumption, organizations can accelerate time to insight. Full story: https://lnkd.in/e4mh22md #DataManagement #HybridCloud #AIAnalytics MinIO

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The importance of metadata and data semantics is back in the spotlight with the recent OSI – Open Semantic Interchange. https://lnkd.in/dXCEFucy Just like the old “plug and pray” boards revealed configuration struggles, today’s AI agents will only succeed if they rely on consistent semantic models — something often as hard as getting developers to value the importance of semantic models, and managers to appreciate the relevance of modern data persistence. These models strengthen data governance, requiring organizations to describe their data right at the source. Their true power emerges when connected to normalization and interoperability layers such as MCP (Model Context Protocol), or when linked to rules expressed as code in critical domains like tax compliance. The adoption of any standard largely depends on its market penetration — just as we’ve seen with USB-C (no more PnP boards), which became universal thanks to its interoperability power. Industry and regulators must also play their role by promoting adoption, setting clear requirements, and ensuring interoperability frameworks become the backbone of trust and compliance. At SOVOS, we are designing our products with these trends in mind, staying close to our clients, and ensuring their Business/IT alignment.

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Terraform provisions Snowflake. That does not solve the harder problem of governing schema and control plane change over time. As Snowflake takes on a bigger role in analytics, data products, and AI, teams need more than provisioning. Our latest post breaks down where Terraform fits, where Liquibase Secure fits, and why provisioning is not enough. Read it here: https://hubs.li/Q047Ckpr0 #Snowflake #DataEngineering #DataGovernance #DevOps #Terraform

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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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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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Quartr API datasets are now available via Snowflake. Access structured earnings data – live audio, real-time and historical transcripts, filings, slide decks, and event summaries. Pull Quartr data directly into your existing Snowflake workflows and augment your datasets. No new pipelines, no separate API integration. Build with data that drives decisions.

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