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Day: October 9, 2026

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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