Snowflake Announces GA of Advanced Data Engineering Capabilities

Our advanced data engineering capabilities for open table formats are now GA: ✅ Zero-ETL connection to ANY Iceberg catalog ✅ Write to any Iceberg table - no vendor lock-in ✅ Automatic optimization - no manual tuning required ✅ Cross-cloud sharing with persistent governance Why manage complex infrastructure when you can focus on insights?

Super thrilled about the latest wave of interoperability features for #ApacheIceberg that have now gone GA. It's a proud moment for our team as we played a vital role in enabling the automatic file sizing and partition support features within this release. These improvements significantly enhance the performance, efficiency, and reliability of large-scale data ingestion and query processing. #Snowflake #Interoperability #Performance https://lnkd.in/g89C4RiE

Snowflake continues to raise the bar — advanced open table format capabilities now GA, enabling a fully connected, governed, and high-performance lakehouse for the AI era. #Snowflake #DataEngineering #Lakehouse #AI #OpenData

Last week at the Databricks World Tour in Boston, one message came through loud and clear: 👉 It’s time to start using agents. I heard Arthur Dooner speak about the new world of no-code and low-code agent implementation, showing how accessible this technology has become. Michael Ban emphasized that the key is to just start — don’t overanalyze cost or value. And Gail Miller highlighted the semantic layer as the bridge that allows agents to truly understand and act on enterprise data. Those ideas stuck with me. Despite being at a Databricks event, I actually spend a lot of my time in Snowflake — and that’s where I found my “low-effort, low-value” opportunity. Every day, a member of our Data Operations team validates the success of long-running batch processes — ingesting terabytes of weather reanalysis data, national and international wholesale electricity market data, and more. These processes fail often: Market operators publish data late or incorrectly. Weather services undergo massive refactors that back up queues for weeks. Even governments go offline, making data availability uncertain. So, I built an Agent in Snowflake to analyze our dimension and fact tables and interpret the status of these batch operations. Along the way, I hit bugs, undocumented feature limits, and some odd edge cases — but it turned out to be an awesome project. The result? A medium-effort, low-value agent… for now. But with more experience, I can see it becoming medium value, and with a dedicated effort, high value. Here’s what I’ve learned so far: 1️⃣ Engineers need to be masters of semantic layers. 2️⃣ We need to understand systems deeply enough to explain them clearly to Agents through those layers. The biggest payoff will come in day-to-day maintenance. Our batch data pipelines require constant context switching, on-call rotations, and detective work. If Agents can handle that repetitive validation, engineers can focus their critical thinking on solving new problems instead of rehashing old ones.

🚀 Excited to share my recent learnings in Azure Data Engineering! Over the past few days, I’ve been exploring architectures and optimization techniques that are transforming the way we handle large-scale data: 🔹 Modern Data Warehouse (2-Tier Architecture) – A streamlined approach where data is ingested into a central storage layer and then served directly to a compute/analytics layer, enabling scalability and faster insights. 🔹 Lakehouse Architecture – Combining the flexibility of data lakes with the reliability and performance of data warehouses for unified analytics. 🔹 Delta Engine – Leveraging Delta Lake for ACID transactions, schema enforcement, and performance optimizations. 🔹 Optimization Techniques – Learning advanced methods to enhance query performance and efficiency, including: Data skipping using statistics Delta cache Compaction / Bin-packing Optimize with Z-order indexing Partitioning and bucketing Vacuum for data cleanup Photon Query Engine for accelerated execution These concepts are strengthening my foundation in designing scalable, high-performance data pipelines and preparing me to apply them in real-world projects. #Azure #DataEngineering #Lakehouse #DeltaLake #BigData #Learning

Just wrapped up The Big Book of Data Engineering from Databricks, and I think it’s worth a share. The book isn’t really a hands on deep dive but it does a great job framing today’s data challenges: - Why reliable pipelines are the foundation for AI success (no good data → no good GenAI) . - How does the medallion architecture (Bronze → Silver → Gold) organize transformations - The role of Delta Lake + Unity Catalog for unifying data quality and lineage and governance across clouds . - tools like Lakeflow and Databricks Assistant that simplify orchestration and bring AI directly into the engineering workflow . For me, coming from a Google Cloud data engineering background it really was interesting to compare these ideas to BigQuery + Dataflow + Dataplex. Databricks clearly position the lakehouse as the answer to a multi cloud complexity and governance If you’re in data engineering I really suggest giving this a read. It’s free and provides a solid big picture lens on where data engineering field is heading.

Introducing Lakehouse Analytics with Snowflake! Achieve the performance and security of a data warehouse directly on your data lake, all without moving or copying data. Snowflake’s single engine allows you to connect data in-place, reliably powering BI, AI, and more, all on a platform that just works to supercharge decision-making with AI you can trust. Watch the full demo from Vino Duraisamy to see how Snowflake’s single engine and built-in governance help you build a simpler, more powerful data architecture. Here’s what you'll learn: ❄️ Iceberg Discovery: How to automatically discover and query fresh Iceberg tables directly from your AWS Glue Data Catalog via a catalog-linked database. ❄️ Enterprise Governance: How to apply and manage consistent, enterprise-grade governance and fine-grained access policies using Snowflake Horizon. ❄️ AI-Powered Access: How to talk to your lakehouse data with Snowflake Intelligence or securely access it via Data Share. Learn more about Lakehouse Analytics with Snowflake: https://lnkd.in/gbhjXAGb

𝗦𝘁𝗶𝗹𝗹 𝘂𝘀𝗶𝗻𝗴 𝗛𝗶𝘃𝗲 𝘁𝗮𝗯𝗹𝗲𝘀 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲? 𝗬𝗼𝘂'𝗿𝗲 𝗱𝗼𝗶𝗻𝗴 𝗶𝘁 𝘄𝗿𝗼𝗻𝗴. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗜𝗰𝗲𝗯𝗲𝗿𝗴 is the modern table format built for today's data challenges: 1)ACID transactions on object storage 2)Schema & partition evolution (without rewriting data) 3)Snapshot-based time travel 4)Engine-agnostic: works with Spark, Flink, Trino & more 4)Built-in performance via metadata & file pruning If you're working with S3, ADLS, or GCS — Iceberg is how you bring reliability, performance, and flexibility to your data lake. ✅ Future-proof, lakehouse-ready, and open-source. Follow Abhishek Agrawal for more AWS Data Engineering insights, tips, and best practices! 🔗 𝑱𝒐𝒊𝒏 𝒐𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝑨𝒑𝒑 𝒄𝒉𝒂𝒏𝒏𝒆𝒍 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒖𝒑𝒅𝒂𝒕𝒆𝒅 𝒐𝒏 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈! https://lnkd.in/dUuscrch 📲

Snowflake + Fivetran + dbt = the open modern data stack. With the announcement of Fivetran and dbt merging, will Snowflake make the next move? Fivetran has long led in automated data movement and governance, while dbt defined the open transformation layer that thousands of data teams rely on through dbt Core. If both companies maintain their open and ecosystem-first approach, this merger could reshape how we build and scale data platforms. Snowflake fits naturally into this picture. With full support for Apache Iceberg, Snowflake is enabling an open, engine-agnostic storage layer that can power analytics and AI anywhere. At the same time, Snowflake is leading the Open Semantic Interchange (OSI) initiative, which brings together ecosystem partners, including dbt Labs, to create shared standards for business semantics like metrics, hierarchies, and relationships. While Fivetran is not a founding OSI member, its deep partnership with Snowflake makes it well positioned to align with this open standards movement. Together, Fivetran for ingestion, dbt for transformation, and Snowflake for open storage and semantics form an interoperable foundation for the next era of data and AI. This collaboration signals something bigger: a shift from closed stacks toward open infrastructure that connects rather than confines. In the AI era, openness isn’t just a philosophy. It’s a strategic advantage and one Snowflake could fully realize by creating a truly open, end-to-end platform. https://lnkd.in/gxkkkbT7 #DataEngineering #OpenSource #DataInfrastructure #dbtLabs #Fivetran #Snowflake #ApacheIceberg #OpenSemanticInterchange #OSI