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Day: August 13, 2021

Moving Data from Confluent Cloud to Cosmos DB

Nathan Ham announces the Azure Cosmos DB sink connector in Confluent Cloud:

Today, Confluent is announcing the general availability (GA) of the fully managed Azure Cosmos DB Sink Connector within Confluent Cloud. Now, with just a few simple clicks, you can link the power of Apache Kafka® together with Azure Cosmos DB to set your data in motion.

Click through for a marketing-heavy look at how this works.

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Change Data Capture with Kafka Connect and Cassandra

Paul Brebner picks up where a series left off:

We introduced the Debezium architecture and its use of Kafka Connect and explored how the Debezium Cassandra Connector (on the source side of the CDC pipeline) emits change events to Kafka for different database operations. 

In the second part of this blog series, we examine how Kafka sink connectors can use the change data, discover that Debezium also propagates database schema changes (in different ways), and summarize our experiences with the Debezium Cassandra Connector used for customer deployment. 

Read on for information on some of the concepts, as well as experiences working with the Debezium Cassandra connector.

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Tips for Azure Site Recovery

Joey D’Antoni shares a few experiences when using Azure Site Recovery:

I need to blog more. Stupid being busy. Anyway, last week, we were doing a small scale test for a customer, and it didn’t work the way we were expecting, and for one of the dumbest reasons I’ve ever seen. If you aren’t familiar with Azure Site Recovery it provides disk level replication for VMs, and allows you to bring on-premises VMs online in Azure, or in another Azure region, if you VMs are in Azure already. It’s not an ideal solution for busy SQL Server VMs with extremely low recovery point objectives, however, if you need a simple DR solution for a group of VMs, and can sustain around 30 minutes of data loss, it is cheap and easy. The other benefit that ASR provides, similar to VMware’s Site Recovery Manager, is the ability to do a test recovery in a bubble environment.

Read on for notes from Joey.

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The Benefits of Dot Plots

Alex Velez shows off two powerful forms of dot plots:

Unfortunately, many graphing tools don’t include dot plots in their default charting options—including Excel, my preferred graphing tool. To build a dot plot in Excel, you need to get creative and format an existing chart to present as a dot plot. 

It sounds like some sort of wizardry, yet hopefully, this article will take the magic out of the process, enabling you to build dot plots and other custom creations.

Click through for a step-by-step walkthrough of how to create dot plots in Excel. Unfortunately, it’s not that much better in Power BI.

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Dealing with Insufficient Detail in Work Requests

Kenneth Fisher airs some grievances:

Many years ago I was given a work request that literally just said:

It’s broke. Fix it.

I’m sure you can see how that is supremely unhelpful. And this isn’t the only time I’ve gotten such great requests.

Kenneth has some great advice here when framing requests and I have just one more thing to add: err on the side of using nouns over pronouns, even when it feels repetitive. “I was in the XYZ service and when I clicked the button, it stopped working” is a mess of an error report because it could refer to the service, to the button, or to some third thing which you have in mind but never wrote down.

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Generating Artificial Data with Databricks Generator

Ust Oldfield shows off a new tool:

Databricks Labs is a relatively new offering from Databricks which showcases what their teams have been creating in the field to help their customers. As a Consultant, this makes my life a lot easier as I don’t have to re-invent the wheel and I can use it to demonstrate value in partnering with Databricks. There’s plenty of use cases that I’ll be using, and extending, with my client but the one I want to focus on in this post is the Data Generator.

Read on for an example of how this works. Something not in Ust’s post but worth mentioning is that you can control the distribution of random numeric features. That’s a piece of functionality you often don’t see in data generators.

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