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You're reading from  Data Observability for Data Engineering

Product typeBook
Published inDec 2023
PublisherPackt
ISBN-139781804616024
Edition1st Edition
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Authors (2):
Michele Pinto
Michele Pinto
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Michele Pinto

Michele Pinto is the Head of Engineering at Kensu. With over 15 years of experience, Michele has a great knack for understanding how data observability and data engineering are closely linked. He started his career as a software engineer and has worked since then in various roles, such as big data engineer, big data architect, head of data and until recently he was a Head of Engineering. He has a great community presence and believes in giving back to the community. He has also been a teacher for Digital Product Management Master TAG Innovation School in Milan, Italy. His collaboration on the book has been prompt, swift, eager, and very invested.
Read more about Michele Pinto

Sammy El Khammal
Sammy El Khammal
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Sammy El Khammal

Sammy El Khammal works at Kensu. He started off as a field engineer and worked his way up to the position of product manager. In the past, he has also worked with Mercedes as their Business Development Analyst – Intern. He has also been an O'Reilly teacher for 3 workshops on data quality, lineage monitoring, and data observability. During that time, he provided some brilliant insights, very responsive behaviour, and immense talent and determination.
Read more about Sammy El Khammal

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Measuring success

Having established the plan, we now need to measure the success of our data observability initiative. While this can be difficult, it is important to understand the effectiveness of the initiative and identify areas for improvement. Here are some ways to measure the success of a data observability initiative:

  • Reduce data incidents: One of the main goals of a data observability initiative is to reduce the number and severity of data incidents. You can measure the success of the initiative by tracking the number of incidents before and after the initiative is implemented, as well as the severity of those incidents. Ideally, you should see a decrease in both areas.
  • Time to resolution: Another important metric you should track is the time it takes to resolve data incidents. The faster you can identify and resolve issues, the better. You can measure the success of the initiative by tracking the average time it takes to resolve incidents before and after the...
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Data Observability for Data Engineering
Published in: Dec 2023Publisher: PacktISBN-13: 9781804616024

Authors (2)

author image
Michele Pinto

Michele Pinto is the Head of Engineering at Kensu. With over 15 years of experience, Michele has a great knack for understanding how data observability and data engineering are closely linked. He started his career as a software engineer and has worked since then in various roles, such as big data engineer, big data architect, head of data and until recently he was a Head of Engineering. He has a great community presence and believes in giving back to the community. He has also been a teacher for Digital Product Management Master TAG Innovation School in Milan, Italy. His collaboration on the book has been prompt, swift, eager, and very invested.
Read more about Michele Pinto

author image
Sammy El Khammal

Sammy El Khammal works at Kensu. He started off as a field engineer and worked his way up to the position of product manager. In the past, he has also worked with Mercedes as their Business Development Analyst – Intern. He has also been an O'Reilly teacher for 3 workshops on data quality, lineage monitoring, and data observability. During that time, he provided some brilliant insights, very responsive behaviour, and immense talent and determination.
Read more about Sammy El Khammal