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Category: ETL

Real-Time Streaming ETL with Kafka and Debezium

Dursun Koc doesn’t have time for batched ETL:

Debezium is not extracting data using SQL. It uses database log files to track the changes in the database, so it has minimum effect on the source system. For more information about Debezium, please visit their website

After the data is extracted, we need Kafka Connect to stream it into Apache Kafka in order to play with it and reshape it as we required. And we will be using ksqlDB in order to reshape the raw data in a way we are required in the target system. Let’s consider a simple ordering system database in which we have a customer table, a product table, and an orders table, as shown below.

Read on for an overview as well as a link to the GitHub repo where you can try this all out.

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Using the ShortCircuitOperator in Airflow

Lior Gavish shows off a useful operator in Apache Airflow:

But what happens when Airflow testing doesn’t catch all of your bad data? What if “unknown unknown” data quality issues fall through the cracks and affect your Airflow jobs? 

One helpful but underutilized solution is to leverage the Airflow ShortCircuitOperator to create data circuit breakers to prevent bad data from flowing across your data pipelines.

Data circuit breakers are powerful, but as with most data quality tactics, the nuances of how they are implemented are critical. Otherwise, you can make a bad problem worse.

Read on to learn more about the operator and how you can use it. The code block images are a bit fuzzy but still readable enough. It might be a little clearer on the original post.

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Data Modification with Synapse Link for SQL Server 2022

Kevin Chant changes some data:

In this post I want to cover some things that happen internally when you do updates and deletes with Azure Synapse Link for SQL Server 2022 whilst it is running.

Because recently somebody asked if Azure Synapse Link for SQL Server 2022 captures updates and deletes after they had read a previous post. Where I covered my initial tests for Azure Synapse Link for SQL Server 2022.

Anyway, short answer is that Azure Synapse Link for SQL Server 2022 captures updates and deletes. In this post I will go into more detail about some of the things that appear to happen along the way.

Click through for Kevin’s tests and what the results look like.

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Power Automate and Dataset-Driven Power BI Subscriptions

Dan English follows up on a prior topic:

In the last post I went over using Power Automate to perform a data driven report subscription using a Paginated report referencing an AAS database. The flow referenced an Excel file with the information to make the process data driven and generate 2000 PDF files that could then be emailed to users. In the flow the PDF files were simply placed in a OneDrive folder for testing purposes to validate the flow would run as expected and to review the metrics after the fact to evaluate the impact of running the process.

For the follow up there were two items that I wanted to compare against the original flow

1. Moving the AAS database being referenced to a Power BI dataset hosted in the same capacity as the Paginated report

2. Using a Power BI report instead of a Paginated report

In this post I will cover the first comparison. 

Check out what changes and what stays the same between using Azure Analysis Services and Power BI-hosted datasets.

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Azure Synapse Link for SQL Server 2022 and File Analysis

Kevin Chant digs into Azure Synapse Link for SQL Server 2022:

In this post I want to cover some file tests for Azure Synapse Link for SQL Server 2022 that I performed.

Because a while back I spotted something interesting whilst I was doing some initial tests for Azure Synapse Link for SQL Server 2022.

Which is when you add new data after the initial load that a new folder called ‘ChangeData’ appears in the storage account container. I noticed that the new file containing the insert was a comma separated value (csv) file. Whereas the table used for the initial load was a parquet file.

Is there a method to this madness? Click through to see Kevin’s tell-all story.

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Adding an Existing Data Factory to GitHub

Andy Leonard has a three-parter for us. Part 1 shows you how to create a GitHub account and repo:

The unabridged topic of source control with github is beyond the scope of this post. There are a number of ways to accomplish the tasks described in this post and series. I welcome your suggestions in the comments.

This post is written to help Azure Data Factory developers get started using github.

Part 2 connects a Data Factory to the repository:

For the purposes of this demo, accept the defaults for “Publish branch” and “Root folder.” Check the “Import existing resources to repository” checkbox under the “Import existing resource” property, select the main branch in the “Import resource into this branch” property, and then click the “Apply” button:

Part 3 handles changes:

Applying what we’ve configured and learned thus far, let’s put this to work in a code-management workflow.

When it’s time to make a change, first create a new branch. I can hear some of you thinking, “Why, Andy? Why create a new branch?” That’s an excellent question. I am so glad you asked! Think of the new branch as a temporary copy of the current state of my Azure Data Factory. 

This series works from the assumption that you don’t have any real experience with Git (or GitHub) for source control, and maybe not much source control experience at all.

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Database-Driven Parameterization for Synapse Pipelines

Paul Hernandez does some configuring:

Particularly in Synapse, there are even no global parameters like in Azure Data Factory. 

When you want to move your development to another environment, typically CI/CDs pipelines are used. These pipelines consume an ARM template together with its parameter file to create a workspace in a target environment. The parameters can be overriding in the CD pipeline as explain here: https://techcommunity.microsoft.com/t5/data-architecture-blog/ci-cd-in-azure-synapse-analytics-part-4-the-release-pipeline/ba-p/2034434

Even so, I have not found a proper way to change the values of a pipeline parameter (the same for data flows and datasets parameters). I saw some custom parameters manipulation to set the default value of a parameter and then deploy it without any value, or even JSON manipulation with PowerShell (the dark side for me).

Read on for an alternative solution which does the job well.

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Feeding Synapse Spark Info to On-Prem Kafka Clusters

Bhadreshkumar Shiyal finds a solution:

Microsoft’s official documentation for Azure Data Factory contains a tutorial which explains how to access an On-Premises SQL Server from Azure Data Factory which is inside a Managed Vnet. You can go through that article here: Access on-premises SQL Server from Data Factory Managed Vnet using Private Endpoint – Azure Data Fac….

Although based upon the article’s solution, to meet our requirements we needed to substitute On-Prem Apache Kafka for On-Prem SQL Server and instead of an Azure Data Factory inside a Managed Vnet, we used a Synapse Workspace inside a Managed Vnet. The “Forwarding Vnet” concept explained in the above tutorial remains as-is in our approach.

As soon as you turn on Data Exfiltration Protection (DEP), the lockdown is real. Click through to see what the process of exfiltrating data through an approved mechanism looks like.

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Argument {0} is Null or Empty in Azure Data Factory

Richard Swinbank diagnoses a problem:

I encountered this error recently while using Azure Data Factory’s (ADF) new(ish) Script activity to run a SQL query. It took me a while to find a fix, and when it happened again two weeks later I’d completely forgotten it. With a bit of luck, writing it down will help me remember next time – and if it helps you too, great

Read on to see what the problem was and how Richard solved it.

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Working with Synapse Link for SQL

Steve Howard gives us an overview of a preview:

Azure Synapse Link for SQL greatly simplifies analytics pipelines as Microsoft manages the orchestration process for you. Since being announced at Microsoft Build, many of you have had the opportunity to try it out in a POC so now seems like a good time to take a deep dive on some implementation aspects that may save you time later.

This blog post will assume you have experience with Synapse dedicated SQL pools and that you have some basic working knowledge of Synapse Link for SQL from following the quick start or from doing an initial POC.

Looking at this, I am a bit concerned about what it means to sync actively changing tables, especially ones large enough to benefit from being in a dedicated SQL pool. “Just reload all the data” may be the right answer but it doesn’t sound like a convenient one.

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