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Category: Cloud

Kafka Connector for Cosmos DB

Sudhindra Sheshadrivasan announces a new connector has become generally available:

We’re excited to announce the General Availability (GA) of the Confluent fully managed V2 connector for Apache Kafka® for Azure Cosmos DB! This release marks a major milestone in our mission to simplify real-time data streaming from and to Azure Cosmos DB using Apache Kafka®.

The V2 connector is now production-ready and available directly from the Confluent Cloud connector catalog. This managed connector allows you to seamlessly integrate Azure Cosmos DB with your Kafka-powered event streaming architecture—without worrying about provisioning, scaling, or managing the connector infrastructure.

Read on to learn more about the new connector and what it takes to hook everything up.

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Checking Valid Configuration Items for Azure SQL Managed Instances

Ben Johnston looks at the art of the possible:

In my last post I described issues that might stop your migration to a SQL Server Managed Instance (SQL MI). This covers configuration items that differ or are not supported in SQL MI. These likely won’t stop your migration, but they could slow you down if you aren’t ready for these changes.

As with previous issues discussed, testing your migration is key. Validate all of your settings and be prepared to make some changes during your migration process. Most of the incompatible options make sense when you think about the purpose of SQL MI – it is controlled by Microsoft. Hardware settings, local file access, high-availability settings, and auditing are configured differently or completely disabled.

Click through to see what you can and cannot do when it comes to configuration.

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Goodbye, Azure ML SDK v1

I have a new video:

In this video, I cover some news from Microsoft around the deprecation of the Azure Machine Learning SDK v1. We’ll take a look at the upgrade guide and see what it will take to perform this upgrade.

Microsoft will still support the SDK v1 until September of 2026, so we have a year to get code sorted out. The CLI v1, however, will go away sooner, so be sure you’re keeping up on that.

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Executing a Fabric Data Pipeline from Azure Data Factory

Koen Verbeeck leaves the confines of Microsoft Fabric:

In the blog post Call a Fabric REST API from Azure Data Factory I explained how you can call a Fabric REST API endpoint from Azure Data Factory (or Synapse if you will). Let’s go a step further and execute a Fabric Data Pipeline from an ADF pipeline, which is a common request. A Fabric capacity cannot auto-resume, so you typically have an ADF pipeline that starts the Fabric capacity. After the capacity is started, you want to kick-off your ETL pipelines in Fabric and now you can do this from ADF as well.

Click through for the process. Though do check the warnings that Koen offers around either spending extra money by remaining in synchronous execution mode, or always getting a positive result in asynchronous execution mode, regardless of whether the underlying Fabric Data Pipeline worked or not.

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Running Cron Jobs in Azure Database for PostgreSQL Flexible Server

Josephine Bush schedules a task:

pg_cron is a simple cron-based job scheduler for PostgreSQL that runs inside the database as an extension. It allows you to schedule PostgreSQL commands directly from your database, similar to using cron jobs at the operating system level. pg_cron on PG Flex is pretty easy to use, making it easy to schedule regular database maintenance and processing tasks directly from within PostgreSQL.

Read on to see how to install the extension, and then how to manage cron jobs. Josephine also lays out some limitations when using pg_cron on Azure and how to track failed jobs.

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Comparing Microsoft Fabric to Snowflake

Evanjalin Joseph lays out a comparison:

Take ShopSmart, a global retail chain that operates both online and offline. The company wants to combine its sales, inventory, and customer data in order to facilitate real-time reporting and predictive analytics. Two top platforms are being assessed by the IT team for this change.

Azure, Power BI, and Microsoft 365 are already widely used by ShopSmart, which is in line with Fabric’s integrated ecosystem. The alternative, however, provides more multi-cloud flexibility and strong performance on structured data. The group has to choose between selecting a more specialized warehousing solution with more deployment options or making use of its current Microsoft investments.

Let’s examine the differences between the two platforms.

Click through for an overview of each platform and how they stack up against one another.

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Loading Excel from SQL Server via Power BI XMLA

Jared Westover doesn’t want to share:

Users want to pull data from tables in an Azure SQL database into Excel via Power Query. This situation sounds simple. However, I don’t want to provide direct access to the database for several reasons, including the potential governance and permissions nightmare. We have a Fabric workspace, and most of the data already exists in Power BI reports. How can we give users access to the data they need without providing direct access to the database for an easy SQL export to Excel?

Click through for the answer. This solution is a bit more roundabout than granting direct database access, but also comes with a host of security benefits.

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400 Bad Request when Debugging a Data Factory Pipeline

Koen Verbeeck runs into a problem:

I recently had a new pipeline fail. It was actually a copy of an old pipeline where I had made some adjustments into as part of a database migration. When triggered during an execution run, it failed saying some expression could not be parsed. When I went into the pipeline and triggered a debug, it immediately failed with the following helpful error message:

Click through for the error message and how Koen was able to fix the issue.

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Calling a Microsoft Fabric REST API via Azure Data Factory

Koen Verbeeck makes the call:

Suppose you want to call a certain Microsoft Fabric REST API endpoint from Azure Data Factory (or Synapse Pipelines). This can be done using a Web Activity, and most Fabric APIs now support service principals or managed identities. Let’s illustrate with an example. I’m going to call the REST API endpoint to create a new lakehouse. 

Click through for the instructions.

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