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Month: October 2026

Securing Temporary Objects, Metadata Visibility, and Audit Assurance in SQL Server

Published 2026-10-12 by Kevin Feasel

Fabiano Amorim hits three topics:

Over the course of this series, I’ve challenged the common assumption that a SQL Server environment is safe simply because it’s patched, firewalled, and configured according to familiar defaults.

I’ve proven that this is not quite true, because real security is not a checkbox. It requires a verification mindset, especially in areas where SQL Server intentionally provides powerful operational features for DBAs, developers, monitoring tools, and application frameworks. 

In this guide, I’ll focus on three features that are easy to underestimate because they look like normal SQL Server behavior. They are global temporary objects, Dynamic Management Views (DMVs), and audit coverage – each a useful feature in their own right.

The other thing they all have in common? They can each become part of an attack chain if granted too broadly, used inside privileged workflows, or trusted without validation. 

Click through for Fabiano’s guidance.

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The Value of Apache Ossie

Published 2026-10-12 by Kevin Feasel

Victoria Holt shares some information:

Apache Ossie (incubating) is an industry-wide specification effort to standardize how we exchange semantic metadata across analytics, AI and BI platforms, providing a vendor neutral, single source of truth for semantic data. Ossie was previously known as Open Semantic Interchange (OSI). A semantic model is simply a shared definition of business concepts, metrics and relationships that helps people, reports and AI systems interpret data consistently.

Click through to learn more about Ossie’s intent. There’s still a long way to go, but with all(?) of the major players on board, hopefully we’ll get some good outcomes.

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The TABLESAMPLE Process in Postgres

Published 2026-10-12 by Kevin Feasel

Radim Marek provides an explanation:

At least Postgres avoids flushing its own cache by reading large tables through a small ring buffer. Nevertheless the disk still perform full read. On a primary server handling production traffic is shared with everything else. When all you need is exploratory confirmation “roughly how many” or “are there any representative samples” reading the whole terabyte is too expensive.

SQL Standard comes with TABLESAMPLE to help with that. It goes in the FROM clause right after the table name, and the rest of the query runs against the subset of pages it picks.

Read on to learn more.

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Defining a Data Science Pit of Success

Published 2026-10-09 by Kevin Feasel

Bruno Rodrigues thinks about robustness in data science:

Rico Mariani, a long-time performance engineer at Microsoft, coined the idea of the pit of success. A system has a pit of success when the natural, lazy way of using it leads to good outcomes. You don’t need heroics or perfect memory. You fall into the right result, and climbing out to do something wrong takes deliberate effort.

I came across this framing in a talk that I was recently recommended and I further recommend it to anyone interested in the topic: Functional architecture – The pits of success – Mark Seemann. It put a name to something I had been circling around for years.

This is worth a careful read if you’re tied in with a data science team. H/T R-Bloggers.

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Consolidating Postgres Databases using Logical Replication

Published 2026-10-09 by Kevin Feasel

Dimitri Fontane explains a process:

This architecture is the one the documentation lists as “consolidating multiple databases into a single one, for example for analytical purposes”. The application developer’s version: three different applications (a shop, a CRM, a billing system), each with its own schema and its own server, all feeding a warehouse; and then the warehouse’s changes exported to something that is not Postgres.

Click through for examples, as well as what limitations you’re liable to run into along the way.

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Choosing a Fabric Rollout Strategy

Published 2026-10-09 by Kevin Feasel

Paul Turley makes a recommendation:

Pilot versus enterprise-wide is the wrong debate. The real question is which one your organization is actually ready for.

Start narrow, then earn scale.

Microsoft’s capacity planning guidance recommends beginning with a proof-of-concept phase on a free 60-day trial capacity, a single use case, a small user group, and an isolated workspace so nothing touches production. You’re testing the concept, not committing the whole company. That isolation matters as much as the scope — keeping the proof-of-concept out of production workspaces means a bad assumption costs you a workspace, not a week of executive reporting.

Read on for a few more tips along these lines.

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Connection Pooling with Microsoft.Data.SqlClient

Published 2026-10-09 by Kevin Feasel

Malcolm Daigle has an update:

You told us that applications using Microsoft.Data.SqlClient can take too long to establish the database connections they need. When many requests need connections at the same time, waiting for the connection pool to grow can delay application readiness and increase latency. This can happen even in common situations like querying several pieces of metadata during startup or receiving a burst of traffic after a quiet period. Microsoft.Data.SqlClient’s new connection pool (Pool V2) is designed to address this problem by establishing new connections concurrently, allowing the pool to respond more quickly when multiple requests need new connections at the same time.

Click through to see how this differs from prior behavior, and to see how well it performs.

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Time Zones and Offsets in SQL Server

Published 2026-10-09 by Kevin Feasel

Erik Darling doesn’t know what time it is:

Or maybe you just started a new job where they cared about time zones and then your other jobs didn’t. So any, all kinds of crazy things can happen. But when you’re working with time zones, especially if you have dates that are not time zone oriented in any way, like for example, this one, this is just a date time too.

And getting used to the functions and how they work and behave can be a little tricky at first. It can be a little mind numbing. So if we run this query, what I want to show you is just we start off with a date time too that has no time zone affiliated with it.

In November, Erik will be right about Eastern time being UTC-5, as that’s Eastern Standard Time. Or maybe Erik is secretly in Indiana, in one of the counties that stays on Eastern Standard Time year-round. Hopefully I didn’t secretly dox him here.

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Power Query Linting for Code Coverage

Published 2026-10-08 by Kevin Feasel

John Kerski has an update to pql-test:

Several years ago, I wrote a blog article introducing the concept of code coverage for semantic models: Part 8: Bringing DataOps to Power BI. With the state of Power BI technology at the time, the bridge was a little far and implementation was quite arduous.

That has changed. With Power BI Project files and User-Defined Functions becoming generally available in 2026, we now have detailed inspection possibilities with what tests exist, what specifically is being tested, and where the gaps in testing live.

I’m happy to announce that version 0.1.18 of pql-test introduces our first attempt at code coverage for semantic models: pql-test code-coverage.

Click through to see what’s new, as well as links to pql-test and more.

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