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Month: October 2024

Building an Impact Analysis Process

Published 2024-10-18 by Kevin Feasel

Marc Lelijveld needs more than the minimum impact analysis:

Imagine you have a semantic model in the Power BI Service (or Fabric if you will), and you’re about to make a breaking change to this semantic model. How do you inform your end users? How do you tell them about this change? In this blog I will zoom in to options you have in the interface that will help you to reach out to your users, looking at different aspects from other reports in Power BI, but also more complex the users that connect via Analyze in Excel.

Click through for the use case, why the built-in impact analysis option for Power BI isn’t sufficient, and what you can do to flesh it out.

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Tips for Adopting Microsoft Fabric

Published 2024-10-18 by Kevin Feasel

Paul Turley shares some thoughts:

Hello, friends. I’ve spent the past few months working with several new Fabric customers who were seeking guidance and recommendations for Fabric architecture decisions. What have we learned about using Fabric in enterprise data settings in the past 11 months? This post covers some of the important decisions points and Fabric solution design patterns.

Much of the industry’s experience with Microsoft Fabric over the past several months has been at a high-level as organizations were dipping their toe in the pool to test the water. So far, our Data & AI team have assisted around 50 clients with Fabric projects of various sizes. We have also implemented a handful of production scale projects with enterprise workloads, comparing notes with community leaders and the product teams who develop the product. What lessons have we learned?

Click through for several bits of high-level architectural guidance intended to make that adoption easier.

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Working with Parquet Files in Postgres

Published 2024-10-18 by Kevin Feasel

Craig Kerstiens announces an extension:

Today, we’re excited to release pg_parquet – an open source Postgres extension for working with Parquet files. The extension reads and writes parquet files to local disk or to S3 natively from Postgres. With pg_parquet you’re able to:

  • Export tables or queries from Postgres to Parquet files
  • Ingest data from Parquet files to Postgres
  • Inspect the schema and metadata of existing Parquet files

Code is available at: https://github.com/CrunchyData/pg_parquet/.

Read on for more background on why we built pg_parquet or jump below to get a walkthrough of working with it.

Hey, that’s my job to tell people to read on to learn more!

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Avoid Storing Files in Databases

Published 2024-10-18 by Kevin Feasel

Joey D’Antoni explains why you almost never want to use FILESTREAM:

Yesterday, I accidentally walked into a discussion on LinkedIn about the merits of Filestream in SQL Server. If you aren’t familar with Filestream, consider yourself lucky, it was a feature that was added to SQL Server in the short timeframe that people thought it was a good idea to use databases for file storage, and before the enlightened times when object storage became a thing. I first remember blobs being a thing in Oracle 8i, where at least you had the ability to store them in a tablespace with a larger block size than 8k, and had a dedicated area of the buffer pool with that larger block size that you could dedicate to that blob tablespace.

Joey has the right of things. There are rare exceptions where it could make sense to store files in databases. My best example involves storing ML models that we use in SQL Server ML Services, simply because of how difficult it is to read anything off of disk via ML Services. But that’s a real edge case.

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Migrating to Azure PostgreSQL Flexible Server from Single Server

Published 2024-10-18 by Kevin Feasel

Josephine Bush performs a migration:

Why Migrate to Flexible Server?

  • High availability and disaster recovery: Flexible Server provides higher availability with zone-redundant architecture.
  • Customizable maintenance windows: More control over when updates and maintenance tasks occur.
  • Performance improvements: Fine-tuned scaling and performance adjustments without downtime.
  • Enhanced security: With VNet integration and more advanced networking options.

Read on to learn more about by when you have to migrate and how you can perform the migration.

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Data Masking in Snowflake: Tagging Policies

Published 2024-10-17 by Kevin Feasel

Kevin Wilkie creates some tags:

In our last post, we discussed data masking and setting it up for specific columns. Now, I don’t know about y’all, but I deal with hundreds of tables with several columns in each on a daily basis that truly need data masking. If I have to go through and set up a masking policy one by one, I might go crazier than I normally am.

Let’s say that I have an existing table, Employee, in my database that I want to apply a policy. As I’m sure many of you know, any table with Employee data should have at least some of its data masked. The first thing that we need to do is to set up the tag that we’ll be using for, in our case, a column named Salary.

Read on to learn more about how to create tags and why you might want to.

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Troubleshooting Chains of Common Table Expressions

Published 2024-10-17 by Kevin Feasel

Jared Westover calls me out:

I started using Common Table Expressions (CTEs) in Oracle before Microsoft SQL Server and found them easier to read than a derived table. Something about reading from the top down just clicked in my brain. As the years have passed, I’m less of a fan for one reason: troubleshooting a chained CTE with several links is hard—especially when it’s someone else’s code. Even the ones I wrote years ago fill me with unease when something breaks. Is there an easier way to break down each link in the chain when things go wrong?

This is, I would argue, the biggest inconvenience around using common table expressions. The fact that SQL Server cannot materialize the contents of CTEs is likely a more prevalent problem, but challenges in how you can troubleshoot the individual parts of common table expressions are real.

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Thoughts on Blobs in PostgreSQL

Published 2024-10-17 by Kevin Feasel

Stefanie Janine Stölting shares some information on using blobs in PostgreSQL:

PostgreSQL does not have a BLOB data type as specified in the SQL standard. The nearest implementation is the data type BYTEA. Since PostgreSQL 9.0 it does handle data by standard as hexadecimal data.

Click through to learn more about how that data type works, some of the limitations around it, and why it’s not a smart idea to store blob-like data in PostgreSQL The arguments are very similar to not doing this in SQL Server, and the counter-arguments are similarly there and exceptional in nature.

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Configuring the Fabric Service Principal to Support Storage APIs

Published 2024-10-17 by Kevin Feasel

Gilbert Quevauvilliers grants some permissions:

This blog post explains how to configure access for my Service Principal to interact with the Azure Storage API to use the API to get details for Microsoft Fabric Storage.

This is part of a blog post series where I am going to show you how to “View Total Storage consumed in Microsoft Fabric”

When I started this blog post I realized that I first need to explain how to configure the Service Principal authentication to interact with the Azure Storage API permissions. This is because in my notebook these steps are required for the notebook to run successfully.

Read on to find out how.

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Regarding pg_dump as a Backup Tool

Published 2024-10-17 by Kevin Feasel

Robert Haas shares some thoughts:

Recently, I’ve been hearing a lot of experienced PostgreSQL users reiterate this line: “pg_dump is not a backup tool.” In fact, the documentation has recently been updated to avoid saying that it is a backup tool, to widespread relief. Experienced PostgreSQL users and developers have been publicly called out for having the temerity to assert that pg_dump is, in fact, a backup tool. I find this narrative deeply frustrating, for two reasons.

Click through for those reasons.

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