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Category: Data Lake

Iceberg Data Support in OneLake

Published 2025-03-18 by Kevin Feasel

Matthew Hicks isn’t replicating data anymore:

Microsoft OneLake is the single, unified, logical data lake that allows your entire organization to store, manage, and analyze data in one place. It provides seamless integration with various data sources and engines, making it easier to derive insights and drive innovation.

At the most recent Microsoft Build conference, we announced the integration effort between Snowflake and OneLake, which aims to allow users of both Snowflake and Microsoft Fabric to work on the same Iceberg data in OneLake, with no data duplication/movement needed. More recently, we released the preview of OneLake’s Iceberg table format support, which included the ability for Snowflake to write Iceberg tables directly to OneLake.

Click through for more information about the current status of this feature, as well as what’s coming soon.

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Writing Data into a Microsoft Fabric Lakehouse via Notebook

Published 2025-03-12 by Kevin Feasel

Stepan Resl writes some code:

Since Lakehouse is one of the key items within Microsoft Fabric, it is important to know how to write data into it in various formats and using different tools. One of the most common tools is notebooks, as they provide great flexibility and speed for development and testing with graphical outputs. In this article, I want to focus primarily on the following types of notebooks:

  • PySpark
  • Python

Click through to see how it works in both notebook types.

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Table Compaction in Apache Spark

Published 2025-02-27 by Kevin Feasel

Miles Cole groups things together:

If there anything that data engineers agree about, it’s that table compaction is important. Often one of the first big lessons that folks will learn early on is that not compacting tables can present serious performance issues: you’ve gotten your lakehouse pilot approved and it’s been running for a couple months in production and you find that both reads and writes are increasingly getting slower and slower while your data volumes have not increased drastically. Guess what, you almost surely have a “small file problem”.

What engineers won’t always sing the same tune on is how and when to perform table compaction.

Read on for a dive into the power of compaction (converting a large number of small files into a small number of large files) and plenty of tips along the way.

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Data Retention for Data in the Microsoft Fabric Lakehouse

Published 2025-01-29 by Kevin Feasel

Kenneth Omorodion clears out some data:

More than before, organizations now aim for a well-defined approach to manage their data storage effectively. Some reasons for this include operational efficiency, cost management, regulatory compliance, and strategic decision-making. In this article, I will describe an approach on data retention management​ for Lakehouse files to manage data storage when the data exists as files in the Fabric Lakehouse.

There’s nothing built in but Kenneth makes it easy.

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Data Lakes, Warehouses, and Lakehouses

Published 2025-01-06 by Kevin Feasel

Noa Shavit disambiguates three terms:

A data warehouse is a repository and platform for storing, querying, and manipulating data. Warehouses are particularly suited for structured data used for decision support and business intelligence. Modern data warehouses have become more efficient, flexible, and scalable (particularly in the context of massively parallel processing and distributed computation), but they still bear the mark of their early development in the previous century.

The data warehouse concept dates back to data marts in the 1970s. After a long incubation period, the idea began to bear fruit commercially at IBM in the late 1980s and early 1990s. Data warehousing improved on the inefficiency of data marts, siloed data stores maintained by individual departments. 

Click through to learn more about each of the three concepts and how they relate.

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Implementing a Star Schema in a Microsoft Fabric Lakehouse

Published 2024-09-30 by Kevin Feasel

Nikola Ilic builds a lakehouse:

But, what is a star schema in the first place? I have good and bad news for you:)…The bad news is that I’m not covering it in this article because this one focuses on explaining how to implement a star schema in Fabric Lakehouse (assuming that you already know what star schema is). The good news is: I’ve already written about it, so go and read this article first, if you’re not sure what star schema represents in the world of data modeling…

In one of the previous articles, I also shown how to implement a star schema in Power BI, by leveraging Power Query Editor.

Now, let’s get our hands dirty and build a star schema by using PySpark in the Fabric notebook!

Click through to see how.

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Microsoft Fabric Direct Lake and Reframing Operations

Published 2024-09-10 by Kevin Feasel

Reza Rad changes the frame:

Power BI offers a new type of connection to Microsoft Fabric Lakehouse or Warehouse, called Direct Lake. The Direct Lake connection acts like DirectQuery and won’t need the data to be refreshed. However, the Power BI semantic model has refresh settings that can be turned on or off. In this article and video, you will learn about the Refresh settings for the Power BI semantic model that is connected using a Direct Lake connection, what that is, and why it is called Reframe.

Read on to learn more, or to check out the video.

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Finding Columns in Memory in Power BI Direct Lake Mode

Published 2024-08-20 by Kevin Feasel

Chris Webb goes searching:

As you probably know, in Power BI Direct Lake mode column data is only loaded into memory when it is needed by a query. I gave a few examples of this – and how to monitor it using DMVs – in this blog post from last year. But which columns are loaded into memory in which circumstances? I was thinking about this recently and realised I didn’t know for sure, so I decided to do some tests. Some of the results were obvious, some were a surprise.

Read on for the answer.

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Building Real-Time Dashboards from Lakehouse Data in Microsoft Fabric

Published 2024-08-09 by Kevin Feasel

Dennes Torres gets around a limitation:

Real-Time dashboards are a great feature in Real Time Intelligence experience to monitor our data. However, by default it’s made to work only with Kusto Databases. The options to create a real time dashboard or to define its data source only accept Kusto Databases.

What if we would like to see in real time the information we have in a lakehouse as well? Let’s discover a solution for this.

Read on for the solution.

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