Datos de Forma Deliberada: Mejores Prácticas para EDW

The article discusses strategies for expanding subject areas in enterprise data warehousing, weighing proactive pre-emption against reactive back-filling. It advocates a balanced approach to governance that allows analysts some flexibility while preventing scope creep. By combining both methods deliberately, organisations can enhance data quality, governance, and responsiveness to business needs.

https://goodstrat.com/2026/03/24/datos-de-forma-deliberada-mejores-practicas-para-edw/

Confused by Data Warehouse vs. Data Lake vs. Data Mesh?

Think of it this way:
- 📦 Warehouse = organized storage room
- 🌊 Lake = throw everything in, sort later
- 🕸️ Mesh = each team owns and serves its own data - but there is still a common hub.

The key insight: Mesh isn't a storage technology. You can run a Data Mesh on top of a Warehouse or Lake. It's about ownership, not infrastructure.

👉 https://www.kdnuggets.com/data-lake-vs-data-warehouse-vs-lakehouse-vs-data-mesh-whats-the-difference

#DataMesh #DataLake #DataWarehouse #DataLiteracy
— bos | 🖼️ ai-generated

Webinair Dataviz et Logiciels Libres

https://peertube.aukfood.net/w/vEjUHGWciWp2iHiD82a2c6

Webinair Dataviz et Logiciels Libres

PeerTube

Every data professional should understand these seven core concepts.

From data warehouses and lakes to pipelines, meshes, and governance, these form the foundation of modern analytics infrastructure.
Mastering them bridges the gap between raw data and actionable business insights.

📕 https://ebokify.com/ai-data-science

#DataEngineering #DataScience #DataAnalytics #ETL #DataWarehouse #BigData #BusinessIntelligence #DataPipeline #DataGovernance

Precog giải quyết bài toán nan giải: làm cho dữ liệu doanh nghiệp (Salesforce, SAP, NetSuite…) sẵn sàng cho AI. Thay vì đổ dữ liệu thô vào LLM, Precog chỉ lấy các trường liên quan, thêm lớp ngữ nghĩa và giữ dữ liệu gốc trong kho. Dùng Snowflake Cortex để chuyển ngôn ngữ tự nhiên thành SQL – dùng AI đúng việc. Dữ liệu không rời kho, an toàn & hiệu quả. #AI #DataWarehouse #EnterpriseAI #LLM #Precog #AIAnToàn #DữLiệuDoanhNghiệp #TríTuệNhânTạo

https://www.reddit.com/r/LocalLLaMA/comments/1qlia0g/fi

Warehouse Snapshots in Microsoft Fabric (Generally Available) | Microsoft Fabric Blog | Microsoft Fabric

Managing data consistency during ETL has always been a challenge for our customers. Dashboards break, KPIs fluctuate, and compliance audits become painful when reporting hits &#8216;half-loaded&#8217; data. With Warehouse Snapshots, Microsoft Fabric solves this by giving you a stable, read-only view of your warehouse at a specific point in time and now, this capability is &hellip; <p class="link-more"><a href="https://blog.fabric.microsoft.com/en-us/blog/warehouse-snapshots-in-microsoft-fabric-freeze-data-unlock-reliable-reporting/" class="more-link">Continue reading<span class="screen-reader-text"> &#8220;Warehouse Snapshots in Microsoft Fabric (Generally Available)&#8221;</span></a>

Ingest files into your Fabric Data Warehouse using the OPENROWSET function | Microsoft Fabric Blog | Microsoft Fabric

Data ingestion is one of the most important actions in the Data Warehouse solutions. In Microsoft Fabric Data Warehouse, the OPENROWSET function provides a powerful and flexible way to read data from files stored in Fabric OneLake or external Azure Storage accounts. Whether you’re working with Parquet, CSV, TSV, or JSONL files, the OPENROWSET function &hellip; <p class="link-more"><a href="https://blog.fabric.microsoft.com/en-us/blog/ingest-files-into-your-fabric-data-warehouse-using-the-openrowset-function/" class="more-link">Continue reading<span class="screen-reader-text"> &#8220;Ingest files into your Fabric Data Warehouse using the OPENROWSET function&#8221;</span></a>

OneLake Security on the SQL Analytics Endpoint | Microsoft Fabric Blog | Microsoft Fabric

OneLake Security centralizes fine-grained data access for Microsoft Fabric data items and enforces it consistently across engines.Currently in Preview and opt-in per item, it lets you define roles over tables or folders and optionally add Row-Level Security (RLS) and Column-Level Security (CLS) policies. These definitions govern what users can see across Fabric experiences. When you &hellip; <p class="link-more"><a href="https://blog.fabric.microsoft.com/en-us/blog/onelake-security-on-the-sql-analytics-endpoint/" class="more-link">Continue reading<span class="screen-reader-text"> &#8220;OneLake Security on the SQL Analytics Endpoint&#8221;</span></a>

A TALE OF TWO ARCHITECTURES - KIMBALL VS INMON

“It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us,……...“

William’s Substack