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Day: September 30, 2026

Power BI Projects Now GA

Published 2026-09-30 by Kevin Feasel

John Kerski takes note:

Do you know where you were on June 15, 2023? Do not fret, neither do I. But I do remember a blog post written by Rui Romano announcing the preview of the Power BI Project (PBIP) format back then.

Many in the Power BI world rejoiced at this news. Microsoft was finally taking steps toward better practices seen in software development and applying them to the analytics space.

As it happens, June 15, 2023 was just the first step. Since then, PBIP has evolved into the format we have today and I am happy to see that, as of September 2026, PBIP is generally available as a fully-supported feature for Power BI by Microsoft.

Before I explain the mechanics of PBIP, let me outline the real problems it was designed to solve.

Not included in John’s article: “Power BI Report Server.” Because Power BI Report Server users learn early on not to attempt to rise above their station.

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SQL Server on Azure Local Now GA

Published 2026-09-30 by Kevin Feasel

Raj Pochiraju announces a product:

Some of the world’s most critical databases run in places where cloud connectivity cannot be assumed. A remote industrial site, for example, may need production systems to continue operating when external connectivity is unavailable. A regulated organization may need sensitive data and processing to remain within sovereign boundaries.

Today, SQL Server on Azure Local is generally available for connected and disconnected operations. With this release, customers can modernize where their data resides while maintaining control over infrastructure, connectivity, and data placement. They can bring AI closer to their data with Foundry Local on Azure Local, currently in preview, and use eligible existing SQL Server licensing investments.

Azure Local is rather limiting in terms of the available hardware, though there are specific environments that I think could be well-suited for this.

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DiskANN Vector Index and Search Now GA

Published 2026-09-30 by Kevin Feasel

Pooja Kamath makes an announcement:

Today, we are announcing the general availability of DiskANN Vector Index & Search across Azure SQL Database, Azure SQL Managed Instance with the always-up-to-date update policy, and SQL database in Microsoft Fabric.

DiskANN brings scalable approximate nearest-neighbor search directly to the SQL engine. Developers can store vectors alongside relational data and combine vector similarity with the filters, joins, security policies, and transactional data their applications already rely on.

It does support INSERT, UPDATE, and DELETE, and they say there’s asynchronous index maintenance. If that works the way it should, it gets rid of one of the biggest pain points of using DiskANN-based vector indexes in the SQL Server universe today.

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Flat File Ingestion Options with Microsoft Fabric

Published 2026-09-30 by Kevin Feasel

Andy Brownsword loads some data:

For this example I’ll use NHS monthly prescribing data which provides us with a large CSV, and we’ll copy it into a Lakehouse. For the Bronze layer we want to retain data as close to source as possible, so simply retrieving the file and retaining the CSV is the goal.

I’ll compare the options for this specific use case, with multiple runs for each. So, let’s see how the options stack up.

Click through to see how long each of the four options takes and what Andy recommends.

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Ten Decisions to Make Prior to Creating a Fabric Workspace

Published 2026-09-30 by Kevin Feasel

Meagan Longoria has a list:

Creating a Fabric workspace takes about 30 seconds. Restructuring workspaces after people have built content in them takes a lot longer. Moving content to another workspace usually means redeploying or recreating it, and anything bound to the old item IDs has to be rebound: reports connected to a semantic model, a notebook’s default lakehouse, pipeline activities, and shortcuts. Item-level shares, app content, and links people saved don’t carry over either.

Most of that rework is avoidable if you make a handful of design decisions before anyone starts building. None of these decisions has a single right answer. The right choice depends on your organization’s size, skills, security requirements, and how much self-service you want to support. But I’d much rather see them made deliberately, before the first workspace exists, than left at the defaults and discovered later. For each decision, I’ll cover the options, what should drive the choice, and the platform constraints that narrow it down.

Read on for that list.

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Queues in Postgres

Published 2026-09-30 by Kevin Feasel

Jobin Augustine builds a queue:

In many real production databases, we often see some tables receiving high-frequency updates (millions per day) and autovacuum running repeatedly (hundreds of times per day). Additionally, those tables can sometimes become heavily bloated. The end effect is poor performance, poor concurrency, and an unmanageable database.

In case we are using methods pg_gather for diagnosis, such anomalies are easy to spot by clicking on the table header to sort.

Read on to learn more about the PgQue extension and how it works.

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