#dataengineers — Bluesky Posts

@pitoach.bsky.social

6 days ago

CDD | 2026-02-12 | Low Code Medallion Using Lakehouse Materialized views | Dennes Torres YouTube video by DataDrivenCommunity

🎥 Meeting Recording! Missed Data Driven Community session on Feb 12? Watch Dennes Torres explain how Materialized Views enable #LowCode #Medallion #architecture in #lakehouse environments.

Great insights for #DataEngineers building modern #data pipelines.

youtu.be/VSvHwZPxV0w

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Ronen Ariely

@pitoach.bsky.social

1 month ago

Low Code Medallion - Using Lakehouse Materialized Views, Thu, Feb 12, 2026, 12:00 PM | Meetup 💡 A **practical deep dive** into one of the most impactful new capabilities for modern lakehouse architectures. **Materialized lake views** are a game-changing feature fo

📅 Join us on Feb 12! Transform your #Data #Lakehouse #workflows with #LowCode #Medallion using Materialized #Views! 🚀 with Dennes Torres.

Simplify medallion architecture & boost productivity.

Register: www.meetup.com/cloud-data-d...

#Data #DataEngineers #Lakehouse #LowCode #MaterializedViews

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@mahi204301.bsky.social

2 months ago

LinkedIn Data Scientist PySpark (Hard — Level) Interview Problem — Solution in Detail Steps Problem Based on User Continuous Streak on Visiting a Platform

Hello data scientists,
Here is an article published, LinkedIn Data Scientist PySpark (Hard — Level) Interview Problem — Solution in Detail Steps.

#DataScientists #PySpark #DataEngineers #Data #Medium #Articles #DataEngineering

medium.com/meanlifestud...

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@mahi204301.bsky.social

2 months ago

Amazon Data Engineer PySpark (Medium — Level) Interview Question — Solution Reinforce Your PySpark Scripting Skills by Solving Different Kinds of Problems

Hello Data Engineers,
Here is an article published on Amazon Data Engineer PySpark (Medium — Level) Interview Question — Solution.

#dataengineers #pyspark #dataengineering #dataanalytics #bigdata #medium #articles

medium.com/meanlifestud...

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O'Reilly

@oreilly.bsky.social

4 months ago

Data Engineering in the Age of AI Much like the introduction of the personal computer, the Internet, and the iPhone into the public sphere, recent developments in the AI space, from Generative

"In order to adapt to this new era of AI, the most important thing data engineers can do involves a fairly self-evident mindshift. Simply put, data engineers need to understand AI and how data is used in AI systems." #Radar aricle by our Andy Kwan to learn more: bit.ly/484XHF7 #AI #DataEngineers

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@lezproductions.bsky.social

4 months ago

"Data engineers: the backbone of AI initiatives. 72% of tech leaders agree they're integral to business success. Download the report to learn more!" #AI #DataEngineers

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Jason Miles

@edudatasci.net

4 months ago

Bronze Is Live Now: what Mirroring + Shortcuts really change about cost, archives, and getting to Silver For years, “Bronze” quietly became a parking lot for periodic snapshots: copy a slice from the source every hour/day, write new files, repeat. It worked, but it was noisy and expensive—lots of hot storage, lots of ingest compute, and a tendency to let “temporary” landing data turn into de‑facto history. Fabric upends that with two primitives that encourage Zero Unmanaged…

In #MSFabric, we've been doing things the way we always have because it was supported, but with the latest developments in mirroring and shortcuts, #DataArchitects have new levers to use to reduce the costs of a data architecture. #DataEngineers can now use Bronze as a live layer, while archiving…

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Jason Miles

@edudatasci.net

5 months ago

Materialized Lake Views (MLVs) in Microsoft Fabric A Materialized Lake View (MLV) is a table in your Fabric lakehouse that’s defined by a SQL query and kept up‑to‑date by the service. You write one CREATE MATERIALIZED LAKE VIEW … AS SELECT … statement; Fabric figures out dependencies, materializes the result into your lakehouse, and refreshes it on a schedule. Today, MLVs are in preview, SQL‑first (Spark SQL), and designed to make Medallion layers (Bronze → Silver → Gold) declarative instead of hand‑assembled pipelines.

In #MSFabric, one of my favorite new technologies are #MaterializedLakeViews. MLVs are currently in preview, but you can already see the potential to completely change the way #DataEngineers and #DataArchitects interact with and build multi-layer data architectures. The idea of #ZeroUnmanagedCopies…

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Jason Miles

@edudatasci.net

5 months ago

The Microsoft Fabric Delta Change Data Feed (CDF) In Microsoft Fabric you’re sitting on top of Delta Lake tables in OneLake. If you flip on Delta Change Data Feed (CDF) for those tables, Delta will record row‑level inserts, deletes, and updates (including pre‑/post‑images for updates) and let you read just the changes between versions. That makes incremental processing for SCDs (Type 1/2) and Data Vault satellites dramatically simpler and cheaper because you aren’t rescanning entire tables—just consuming the “diff.” Fabric’s Lakehouse fully supports this because it’s natively Delta; Mirrored databases land in OneLake as Delta too, but (as of September 2025) Microsoft hasn’t documented a supported way to

Today, for #FabricFriday, I'm taking a deep dive into one of the most promising features in the #MSFabric Lakehouse. That's the Delta Change Data Feed. Right now, #DataEngineers can only pull data from traditional delta files written in traditional ways, but one can see a future where mirrored…

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