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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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Analyzing the application

A common way to understand what happens in an application is to replay its course after it’s run. A good example would be a SQL application. When you query a SQL database, for instance, through a JDBC connector, you are creating access logs in the database. These logs may contain lots of information, especially regarding who has queried the database, what they queried, when it was executed, and sometimes information on how long it took to process the query, how many bytes were retrieved, and so on.

This situation is explained in Figure 3.4. Users are continuously querying a central SQL database. This creates a log file, which is a kind of journal that contains the records of the queries:

Figure 3.4 – Logging strategy for a SQL logs analyzer

Figure 3.4 – Logging strategy for a SQL logs analyzer

This said, these logs can be extremely valuable for observability purposes. By using strategies to retrieve and analyze the logs, the data team can rebuild data transformation...

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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