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Category: ETL / ELT

Using The ROWVERSION Type For ETL

Published 2019-02-13 by Kevin Feasel

Max Vernon shows us how to use the ROWVERSION data type to tell how much work you have to do to ETL data over from one table to another:

The OLTP table implements a rowversion column that is automatically updated whenever a row is updated or inserted. The rowversion number is unique at the database level, and increments monotonically for all transactions that take place within the context of that database. The dbo.OLTP_Updates table is used to store the minimum row version available inside the transaction used to copy data from the OLTP table into the OLAP table. Each time this code runs it captures incremental changes. This is far more efficient than comparing all the rows in both tables using a hashing function since this method doesn’t require reading any data other than the source data that is either new, or has changed.

I think this is the first time I’ve seen someone use ROWVERSION types successfully.

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Integrating Azure Data Factory With GitHub

Published 2019-02-07 by Kevin Feasel

Rayis Imayev shows us how to tie Azure Data Factory pipelines with GitHub, allowing automatic check-in based on ADF pipeline changes:

Working with Azure Data Factory (ADF) enables me to build and monitor my Extract Transform Load (ETL) workflows in Azure. My ADF pipelines is a cloud version of previously used ETL projects in SQL Server SSIS.

And prior to this point, all my sample ADF pipelines were developed in so-called “Live Data Factory Mode” using my personal workspace, i.e. all changes had to be published in order to be saved. This hasn’t been the best practice from my side, and I needed to start using a source control tool to preserve and version my development code.

Click through for a detailed demo.

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Azure Data Factory Data Flows

Published 2019-01-31 by Kevin Feasel

Joost van Rossum takes a look at data flows in Azure Data Factory:

2) Create Databricks Service
Yes you are reading this correctly. Under the hood Data Factory is using Databricks to execute the Data flows, but don’t worry you don’t have to write code.
Create a Databricks Service and choose the right region. This should be the same as your storage region to prevent high data movement costs. As Pricing Tier you can use Standard for this introduction. Creating the service it self doesn’t cost anything.

Joost shows the work you have to do to build out a data flow. This has been a big hole in ADF—yeah, ADF seems more like an ELT tool than an ETL tool but even within that space, there are times when you need to do a bit more than pump-and-dump.

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Data Transformation Tools In The Azure Space

Published 2019-01-11 by Kevin Feasel

James Serra gives us an overview of the major tools you would use for ETL and ELT in Azure:

If you are building a big data solution in the cloud, you will likely be landing most of the source data into a data lake.  And much of this data will need to be transformed (i.e. cleaned and joined together – the “T” in ETL).  Since the data lake is just storage (i.e. Azure Data Lake Storage Gen2 or Azure Blob Storage), you need to pick a product that will be the compute and will do the transformation of the data.  There is good news and bad news when it comes to which product to use.  The good news is there are a lot of products to choose from.  The bad news is there are a lot of products to choose from :-).  I’ll try to help your decision-making by talking briefly about most of the Azure choices and the best use cases for each when it comes to transforming data (although some of these products also do the Extract and Load part

The only surprise is the non-mention of Azure Data Lake Analytics, and there is a good conversation in the comments section explaining why.

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Apache Airflow Now A Top-Level Project

Published 2019-01-10 by Kevin Feasel

Fokko Driesprong announces that Apache Airflow is now a top-level Apache project:

Today is a great day for Apache Airflow as it graduates from incubating status to a Top-Level Apache project. This is the next step of maturity for Airflow. For those unfamiliar, Airflow is an orchestration tool to schedule and orchestrate your data workflows. From ETL to training of models, or any other arbitrary tasks. Unlike other orchestrators, everything is written in Python, which makes it easy to use for both engineers and scientists. Having everything in code means that it is easy to version and maintain.

Airflow has been getting some hype lately, especially in the AWS space.

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Generating SQL With Biml

Published 2019-01-09 by Kevin Feasel

Cathrine Wilhelmsen shows us you can do a lot more with Biml than just generating SSIS packages:

This actually happened to me in a previous job. We had a fairly complex ETL solution for the most critical part of our Data Warehouse. Many SSIS packages, views, and stored procedures queried the tables that were replicas of the source system tables. One day, we found out that the source system would be deploying a new version of their database the following day. In every single table, some columns were removed, others added, and many changed data types.
Oh.
There was no way that we could manually update all our SSIS packages, views, and stored procedures in less than a day. Thousands of users depended on our solution. It was too late to pause the source system changes.
Oh.

That story ends up with a happy ending.

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Exporting To Text With SQL Server: Comparing Methods

Published 2018-12-27 by Kevin Feasel

Phil Factor shows us several ways of exporting data from SQL Server to files and gives us size and time comparisons:

I enjoy pulling the data out of AdventureWorks. It is a great test harness. What is the quickest way of doing it? Well, everyone knows it is native BCP, but how much faster is that format than tab-delimited or comma-delimited BCP? Can we quickly output data in XML? Is there a way of outputting array-in-array JSON reasonably quickly? Of course, the answer is going to vary from system to system, and across versions, but any data about this is usually welcome.
In addition to these questions, I wanted to know more about how much space these files take up, either raw or zipped. We’re about to find out. We’ll test all that, using good ol’ BCP and SQLCMD.
My motivation for doing this was to explore ways of quickly transferring data to MongoDB. to test out a way of producing array-in-array JSON at a respectable turn of speed. It turned out to be tricky. The easy and obvious ways were slow.

As is usual for Phil, this article is done quite well.

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Implementing A Change Tracking Solution In SQL Server

Published 2018-11-29 by Kevin Feasel

Jon Shaulis shows us how we can use Change Tracking to detect when rows get modified:

This allows you to detect changes in a lightweight manner via the Transaction Log in SQL Server in combination with T-SQL. Change Data Capture is more about auditing or creating a historical view and Temporal Tables are the next step up from there which became available in 2016 versions of SQL Server. Change Tracking is primarily used for finding only things that have changed. Not necessarily why, how, or who changed it, but what has changed and what it is now.

So why would you want this technology implemented? I find this technology is best suited for tasks where I want as light of a footprint as possible and I want to bring over incremental changes.

Click through for a long and complete walkthrough.  If you’re thinking to implement change tracking, this is a good link to check out.

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Tracking Errors In Power BI

Published 2018-11-27 by Kevin Feasel

Reza Rad has a lengthy post covering how you can track errors in Power Query:

To build a robust BI system, you need to cater for errors and handle errors carefully. If you build a reporting solution that the refresh of that fails everytime an error occurs, it is not a robust system. Errors can happen by many reasons, In this post, I’ll show you a way to catch potential errors in Power Query and how to build an exception report page to visualize the error rows for further investigation. The method that you learn here, will save your model from failing at the time of refresh. Means you get the dataset updated, and you can catch any rows caused the error in an exception report page. To learn more about Power BI, read Power BI book from Rookie to Rock Star.

There’s a lot of work, but also a lot of value in doing that work.

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Kafka Connect Converters And Serialization

Published 2018-11-15 by Kevin Feasel

Robin Moffatt goes into great detail on Apache Kafka Connect converters and serialization techniques:

Kafka Connect is modular in nature, providing a very powerful way of handling integration requirements. Some key components include:

  • Connectors – the JAR files that define how to integrate with the data store itself
  • Converters – handling serialization and deserialization of data
  • Transforms – optional in-flight manipulation of messages

One of the more frequent sources of mistakes and misunderstanding around Kafka Connect involves the serialization of data, which Kafka Connect handles using converters. Let’s take a good look at how these work, and illustrate some of the common issues encountered.

Read on for a good overview of the topic.

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