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Author: Kevin Feasel

Tuning Kafka Connect Source Connectors

Catalin Pop makes things faster:

Kafka Connect is an open source data integration tool that simplifies the process of streaming data between Apache Kafka® and other systems. Kafka Connect has two types of connectors: source connectors and sink connectors. Source connectors allow you to read data from various sources and write it to Kafka topics. Sink connectors send data from the topics to another endpoint. This blog post discusses how to tune your source connectors to help you get the best throughput out of your compute resources. 

This includes which elements are tunable, metrics you’ll want to pay attention to along the way, and a detailed example.

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String Aggregation with STRING_AGG

Jeff Mlakar uses the STRING_AGG() function:

Often when generating dynamic sql we must add some separator values in the string(s) we build. A function delivered way back in 2017 called STRING_AGG is a simple way to do this. Let’s look at some examples.

There are a lot of people who have never heard of the function and don’t know that there’s a better alternative to the combination of STUFF() and FOR XML PATH to concatenate together strings.

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PostgreSQL 16 and Infinity

Ryan Lambert goes to infinity and beyond:

This month, Ryan Booz chose the topic: What Excites You About PostgreSQL 16? With the release of Postgres 16 expected in the near(ish) future, it’s starting to get real. It won’t be long until casual users are upgrading their Postgres instances. To decide what to write about I headed to the Postgres 16 release notes to scan through the documents. Through all of the items, I picked this item attributed to Vik Fearing.

  • Accept the spelling “+infinity” in datetime input

The rest of this post looks at what this means, and why I think this matters.

Read on to see what’s new about this and what it all means.

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Building a Report Model from Agile User Stories

Kelly Broekstra digs into a story:

Are you a model designer or BI developer tasked with building a data model and/or report from a series of user requirements or Agile User Stories? Do you know where to start, or what to try first? Here are some practical tips and techniques that I have used to design a great report model from Agile user stories.

Read on for questions to ask and how to translate that into a star schema model.

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STRING_SPLIT Not a Recognized Built-In Function Name

Kendra Little resolves an error:

Yesterday, I was writing some Transact SQL to dust off the cobwebs. I got confused when I was playing around with the STRING_SPLIT function, and kept getting the error:

Msg 195, Level 15, State 10, Line 2 ‘STRING_SPLIT’ is not a recognized built-in function name.

When I saw this, I immediately thought compatibility level, as that’s something I’ve run into several times before. In this case, it turns out that the correct answer was something else.

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Interesting R Functions for Package Dependencies and File Analysis

Maelle Salmon shows off a few interesting functions:

How does this package depend on this other package? pak::pkg_deps_explain()

The pak package by Gábor Csárdi makes installing packages easier. If I need to start working on a package, I clone it, then run pak::pak() to install and update its dependencies. It’s a “convenience function” that is convenient for sure! Bye bye remotes::install_deps().

Read on for an example of this, as well as details on two other functions in different packages. H/T R-Bloggers.

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Building Correlation Heatmaps in R

Steven Sanderson shows two packages for building heatmaps in R:

Data visualization is a powerful tool for understanding the relationships between variables in a dataset. One of the most common and insightful ways to visualize correlations is through heatmaps. In this blog post, we’ll dive into the world of correlation heatmaps using R, using the mtcars and iris datasets as examples. By the end of this post, you’ll be equipped to create informative correlation heatmaps on your own.

Read on to see how to build heatmaps with the corrplot and ggcorrplot packages.

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Restoring a Database with Standby in SQL Server

Steve Jones stands by for station identification:

Sometimes you want to restore part of your data, but you still want the option to continue restores. A classic example of this is when you are restoring a number of transaction logs and want to check the data to find a place where certain values haven’t been changed.

Suppose someone deletes a bunch of data between 10am and 11am from the supplier table. You know that they added “Acme” to this table before the delete. You might restore up to 10am and check the supplier table for the old data and look for Acme. If it’s not there, maybe you restore the 10:05am log backup and check again. If it’s not there, then the 10:10am log, etc.

Click through to see how you can do that.

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Executing Transactions in PostgreSQL

Salman Ahmed rolls it back:

Transactions, like any other database, are a key component of PostgreSQL. A transaction is a sequence of one or more database operations that are executed as a single unit of work. These operations can be queries (e.g. SELECT, INSERT, UPDATE and DELETE) that modify the database’s state.

A transaction’s main purpose is to combine multiple statements into an atomic, all-or-nothing process. It ensures that either all operations within a transaction are fully completed, or none of them are executed at all. Concurrent transactions cannot see each other’s unfinished changes. Updates from ongoing transactions remain hidden until completion, at which point all changes become visible simultaneously.

This is very similar to SQL Server, except their savepoints actually work they way they’re supposed to.

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Multiple Workspaces and Microsoft Fabric Git Integration

Kevin Chant can’t stop at one:

In this post I want to cover working with Microsoft Fabric Git integration and multiple workspaces. By highlighting one method that you can use in the real-world.

I must admit that I have been very keen to test this particular way of working with Microsoft Fabric Git integration and multiple workspaces.

By the end of this post, you will know one way that you can work with Microsoft Fabric Git integration and multiple workspaces. Based on real-world working practices. Including multiple branches and pull requests.

Click through to see what Kevin has in mind usingg Azure DevOps.

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