Seamless Data Fusion with Lexicon

𝐞𝐱𝐭𝐞𝐫𝐧𝐚𝐥 𝐞𝐧𝐠𝐢𝐧𝐞𝐬 𝐜𝐚𝐧 𝐧𝐨𝐰 𝐰𝐫𝐢𝐭𝐞 𝐭𝐨 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 𝐭𝐚𝐛𝐥𝐞𝐬 (In public Preview) : Snowflake just announced a major leap forward in open data architecture — 𝐟𝐮𝐥𝐥 𝐛𝐢𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐢𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 𝐭𝐚𝐛𝐥𝐞𝐬 𝐯𝐢𝐚 𝐭𝐡𝐞 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐇𝐨𝐫𝐢𝐳𝐨𝐧 𝐂𝐚𝐭𝐚𝐥𝐨𝐠. This means teams can now securely write to Snowflake‑managed Iceberg tables from external engines like Spark, Trino, and others, completing the last mile in truly open, multi‑engine data ecosystems. Until now, external engines could only read from Snowflake’s Iceberg tables. With this update, organizations can maintain a single governed copy of Iceberg data in Snowflake while executing workloads wherever it makes the most sense. It eliminates the headache of multiple copies. #snowflakenewfeatures #datalake #snowflake

Snowflake Streams capture incremental changes, while Tasks automate SQL processing. Together, they enable the creation of fully incremental ELT pipelines without the need for external orchestration tools. This integration streamlines data workflows and enhances efficiency in data management. #Snowflake #Streams #Tasks #ETLPipelines

Migrating off a legacy warehouse doesn’t have to be complex or painful. Since launching last summer, #Lakebridge has been enabling fast and predictable data migrations. Our latest advancements let you: - Analyze existing source system usage with Lakebridge’s assessment tool, now with profiling support for Synapse - Convert more complex SQL using Lakebridge’s AI-powered code converter and the Databricks Assistant - Navigate migration workflows easily with a new, intuitive Lakebridge desktop app Over 1,000 customers and partners have used Lakebridge to move off legacy systems and modernize on the Databricks Platform.   👉 Read how these updates reduce risk and speed up migrations.  #Databricks #DataMigration #Lakehouse #DataWarehouseMigration https://lnkd.in/dBp2FqdX

🚀 One big shift in data engineering over the last few years: The Modern Data Stack. Instead of relying on a single platform, many teams now combine specialized tools for different layers of the pipeline. A typical stack today looks like this: • Ingestion → Fivetran / Airbyte • Storage → Snowflake / Databricks • Processing → Apache Spark • Transformation → dbt • Orchestration → Apache Airflow Instead of one heavy platform doing everything, the goal is: • modular architecture • scalable data pipelines • easier maintenance Tools will continue to change. But the fundamentals remain the same: Move data reliably. Model it well. Make it usable. #DataEngineering #ModernDataStack #BigData #DataPlatforms #DataArchitecture #CloudData #AnalyticsEngineering #C2C #DataPipelines #DataInfrastructure #Snowflake

Debezium is free like a puppy, not free like a beer. 🐶 That line from shantanu gope's new article made me laugh — and then nod, because I've heard versions of it from almost every customer running open-source CDC at scale. Shantanu lays out what happened when a healthcare company tried to replicate data from 19 SQL Server instances and 540 databases into Snowflake using Debezium + Kafka. The architecture looked something like: SQL Server → Debezium → Kafka → Kafka Connect → raw JSON → custom flatten tasks → staging → final merge Seven hops. Seven things to monitor, tune, and fix at 3am. They replaced it with Openflow — our managed connector that goes straight from SQL Server to Snowflake. One hop. 1-5 minute latency. Schema evolution and table auto-creation included. The part that stuck with me: they went from being "infrastructure-rich and data-poor" to actually using their data. That's the whole point. If you're running Debezium/Kafka for SQL Server CDC and spending more time feeding the pipeline than feeding your analytics — this is worth 10 minutes of your time. Link in comments. 👇 Huge shoutout to shantanu gope for writing this up so clearly. Real architecture, real numbers, no fluff. #Snowflake #Openflow #CDC #SQLServer #Debezium #DataEngineering #ChangeDataCapture #RealTimeData

I used to think having more data tools meant you were doing data right. Snowflake for your warehouse. Fivetran for ingestion. Atlan for catalog. Monte Carlo for observability. Something else for governance. In the industry, it's normalized as the "modern data stack". Then I watched teams spend several months just integrating everything… …and more months debugging why things kept breaking between tools. 5 vendors. 5 integration points. 5 expensive bills. Every integration point is a failure point. When we started building Autolake, we asked one simple question: What if ingestion, governance, lineage, catalog, and monitoring were part of the same system from day one AND could be built/ran in your own cloud environment in 15 minutes? So that's exactly what we built. Along the way I mapped several Modern Data Stack architectures and the most common integration failures I’ve seen across teams. Where ingestion breaks. Where lineage gets lost. Where observability usually fails. comment “Stack” and I’ll DM you the diagram. #DataEngineering #DataArchitecture #DataPlatform

The staging layer of my cloud data engineering project is now complete. After building and validating the RAW ingestion pipeline in Snowflake, the next step was to introduce structured transformations using dbt. What this phase included: • Creating dedicated staging models for each raw table • Standardizing column names and enforcing consistent naming conventions • Handling null values and basic data cleaning • Casting data types appropriately for downstream use • Preserving source-level grain while preparing data for analytics Each staging model now acts as a controlled interface between raw ingestion and business logic. This separation keeps transformations modular and maintainable, which is essential in production-grade data platforms. The architecture now flows: Source files → Snowflake RAW → dbt STAGING → (next: MARTS) The next milestone will be designing fact and dimension tables in the marts layer, where business-level transformations and analytical modeling will be implemented. Project repository: https://lnkd.in/giJ_6UcZ #DataEngineering #dbt #Snowflake #AnalyticsEngineering

❄️ Snowflakers ❄️! 📢 Feature Update – dbt Core versions update (Mar 2, 2026)! Read more 👇👇👇 🚨🧐 Snowflake now supports newer versions of dbt Core, giving teams more flexibility to standardize and upgrade their transformation workflows. Yes, you read that right! You can now align your Snowflake projects with the latest supported dbt Core releases and keep your data stack up to date. 🛠️⚙️ In this feature: 1️⃣ Support for additional dbt Core versions in Snowflake 2️⃣ Greater flexibility to upgrade projects without breaking compatibility 3️⃣ Better alignment with modern analytics engineering practices 4️⃣ Continued integration between Snowflake and dbt for transformation workloads 📋✨ Perfect for: • Analytics engineers managing dbt upgrades • Teams standardizing dbt versions across environments • Organizations modernizing their transformation layer • Data platforms looking for tighter Snowflake + dbt alignment 💡 Keeping your dbt Core version current means access to performance improvements, new features, and long-term maintainability — all while running on Snowflake’s powerful data platform. 🫵 Ready to upgrade your dbt projects on Snowflake? ♾️ Follow me here and Medium https://lnkd.in/dZjfenTm for more tips! 📃 Doc reference: https://lnkd.in/ecJCYHEN Amilee Alesna #Snowflake #DataSuperhero #Snowflake #Postgres #SnowflakePostgres #DataEngineering #CloudData #ModernData #OLTP #Analytics #snowflakedevelopers #superdataheroes #dbt #dbtcore #workloads

Unlock the true power of your data with a modern stack that just works. Transform, model, and analyze seamlessly with dbt driving your pipelines. Scale effortlessly on Snowflake or Redshift, no infrastructure headaches. Turn raw data into actionable insights faster than ever before.

Everyone wants a lakehouse. Not everyone has lakehouse-ready data. If your pipelines are fragile, definitions differ by team, and governance is in the docs, a “new platform” often just moves the chaos to a newer place. We put together a clear comparison and a simple decision path, including what usually goes wrong in migrations and what to check before you touch the architecture. 1/ warehouse vs lake vs lakehouse (what each is good at) 2/ how to choose based on data maturity 3/ where Databricks, Snowflake, and BigQuery fit 4/ migration strategies that don’t blow up quarter-end reporting Full breakdown here: https://lnkd.in/eBz9e5CD