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xdx001
by New Contributor III
  • 921 Views
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Strong Databricks Fundamental - Gen Z

Why Databricks is the Future of Data Analytics for Gen ZIn the fast-paced world of data analytics, staying ahead of the curve is crucial. For Gen Z, who are digital natives and always on the lookout for the latest tech trends, understanding the diffe...

  • 921 Views
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ThomazRossito
by Contributor
  • 3794 Views
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Post: Lakehouse Federation - Databricks

Lakehouse Federation - Databricks In the world of data, innovation is constant. And the most recent revolution comes with Lakehouse Federation, a fusion between data lakes and data warehouses, taking data manipulation to a new level. This advancement...

Community Articles
data engineer
Lakehouse
SQL Analytics
  • 3794 Views
  • 1 replies
  • 1 kudos
Latest Reply
Freshman
New Contributor III
  • 1 kudos

Hey Quick Question, Can we use it for the production version ? We have application server as SQL server, we are planning to use lakehouse federation so we can bypass creating and maintaining 100 of workflows. as we a small dataset I am not too sure o...

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Shahram
by New Contributor II
  • 1064 Views
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Hub Star Modeling 2.0 for Medalion Architecture

Excited to share my latest publication on arXiv!“Hub Star Modeling 2.0 for Medallion Architecture” https://arxiv.org/abs/2504.08788This new version builds on the original Hub Star Modeling approach, published last year, and now tailored for the Meda...

  • 1064 Views
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genevive_mdonça
by Databricks Employee
  • 3381 Views
  • 1 replies
  • 6 kudos

Handling Complex Nested JSON in Databricks Using schemaHints

When I first got into managing schemas in Databricks, it took me a while to realize that putting in a little planning up front could save me a ton of headaches later on.I was working with these deeply nested, constantly changing JSON files. At first,...

  • 3381 Views
  • 1 replies
  • 6 kudos
Latest Reply
Advika
Community Manager
  • 6 kudos

Great tip @genevive_mdonça! schemaHints help avoid issues with evolving JSON data, making data processing more reliable and easier to maintain. Thanks for sharing.

  • 6 kudos
techgeorge
by New Contributor III
  • 2580 Views
  • 1 replies
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Understanding Coalesce, Skewed Joins, and Why AQE Doesn't Always Intervene

In Spark, data skew can be the silent killer of performance. One wide partition pulling in 90% of the data?But even with AQE (Adaptive Query Execution) turned on in Databricks, skewness isn't always automatically identified— and here’s why.What Is co...

Data Skew.png
  • 2580 Views
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Latest Reply
Louis_Frolio
Databricks Employee
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@mark_ott , this question seems right up your alley. Care to comment?

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Yuki
by Contributor
  • 2382 Views
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One of the solution of [FAILED_READ_FILE.NO_HINT] Error while reading file, when display() or SELECT

One of the solution of [FAILED_READ_FILE.NO_HINT] Error while reading file, when display() or SELECTI got stuck with the above error when using `spark.read.table().display()` or directly query the table using %sql.While the display method is just one...

  • 2382 Views
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techgeorge
by New Contributor III
  • 894 Views
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How to train a Convolutional Neural Network on Databricks with Tensorflow and Keras

Here is how to trained a lightweight Convolutional Neuronal Network (CNN) to detect pneumonia from chest X-rays pictures on Azure Databricks. I promise no LLMs, no hype, just real-world deep learning:1. Built it with TensorFlow & Keras on Databricks2...

techgeorge_0-1743756172384.png
  • 894 Views
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Brahmareddy
by Esteemed Contributor
  • 1918 Views
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Use Query Patterns to Suggest Indexes Dynamically

Hey folks,Ever notice how a query that used to run super fast suddenly starts dragging? We’ve all been there. As data grows, those little inefficiencies in your SQL start showing up — and they show up hard. That’s where something cool comes in: using...

  • 1918 Views
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DataDarvish
by Databricks Partner
  • 2608 Views
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Unit Testing for Data Engineering: How to Ensure Production-Ready Data Pipelines

In today’s data-driven world, the success of any business use case relies heavily on trust in the data. This trust is built upon key pillars such as data accuracy, consistency, freshness, and overall quality. When organizations release data into prod...

  • 2608 Views
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