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Data Observability for Data Engineering

You're reading from Data Observability for Data Engineering

Product type Book

Published in Dec 2023

Publisher Packt

ISBN-13 9781804616024

Pages 228 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (2):

Michele Pinto

Sammy El Khammal

View More author details

Table of Contents (17) Chapters

Preface

Part 1: Introduction to Data Observability

Chapter 1: Fundamentals of Data Quality Monitoring

Chapter 2: Fundamentals of Data Observability

Part 2: Implementing Data Observability

Chapter 3: Data Observability Techniques

Chapter 4: Data Observability Elements

Chapter 5: Defining Rules on Indicators

Part 3: How to adopt Data Observability in your organization

Chapter 6: Root Cause Analysis

Chapter 7: Optimizing Data Pipelines

Chapter 8: Organizing Data Teams and Measuring the Success of Data Observability

Part 4: Appendix

Chapter 9: Data Observability Checklist

Chapter 10: Pathway to Data Observability

Index

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Getting the metadata of the data sources

The fuel for the data application is the data itself. The data sources that are used in the application have to be correctly identified in the logs. If an issue occurs at a data source and you need to perform deeper analyses, you would expect information that will help you retrieve the data. In this section, we will see how a data source can be identified.

Data source

To identify the data that’s used by an application, we need to define the metadata of the data source. The metadata represents the data on the data.

The metadata of the data source is all the elements that will allow you to recognize the data source. Let’s explore them:

The file’s location: This gives you the address of the data source and helps you retrieve the data in case you need it. The file location can be the path on your local filesystem or the filesystem of the company. It can also be a connection string if the data is in a table located...

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Authors (2)

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.

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See other products by Michele Pinto

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.

See other products by Sammy El Khammal

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