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Side by Side Upgrade to SQL Server 2022

As a SQL Server DBA, the migration of SQL Server from an existing version to the latest version is a usual activity. In today’s cloud-oriented world, many organizations still prefer an on-prem environment; my organization is one of them. There are multiple reasons to keep your data on-prem, like having more privacy and control of the environment. Currently, our major project is to migrate our existing Microsoft SQL Server 2019 to SQL Server 2022. Recently, we completed the POC. Today, let’s discuss the steps of the SQL Server 2022 installation and migration of our databases.

2025-09-24

External Article

Getting Started with Bayesian Modeling

Multivariate analysis in data science is a type of analysis that tackles multiple input/predictor and output/predicted variables. This tip explores the problem of predicting air pollution measured in particulate matter (PM) concentration based on ambient temperature, humidity, and pressure using a Bayesian Model.

2025-09-19

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How to Measure Replication Latency in SQL Server AlwaysOn Synchronous Availability Groups

Synchronous replicas in SQL Server Availability Groups promise no data loss, but they don’t promise zero delay; under heavy load they can still fall behind. This article shows how to measure and track that hidden replication delay using SQL Server performance counters, so you can see how well your system keeps up during IO‑intensive operations and plan maintenance more safely.

2025-09-17

External Article

A Rolling Filtered Index in SQL Server

I recently resolved an issue where a query pulling data from the last 30 days would time out due to the table’s size and the lack of a supporting index. Creating a supporting index is possible, but not ideal; it will be very large and may not be useful for most queries and use cases. I wonder how I could implement a filtered index that follows time and is always limited to the last n days.

2025-09-10

External Article

How to Script Dimensions with data build tool (dbt) Macros

In this article, we’ll revisit the dimension models we created. We wrote the entire SQL statement for the dimension by hand, and the dimensions themselves were very rudimentary; they lacked a surrogate key and there were no audit columns (such as insert date and update date). We’ll show you how we can expand the dimensions using Jinja, but also how we can minimize development effort by baking reusable patterns into the Jinja code.

2025-09-03

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Question of the Day

String Similarity I

On SQL Server 2025, when I run this, what is returned? 

SELECT EDIT_DISTANCE_SIMILARITY('SQL Server', 'MySQL')

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