Microservice Patterns and Best Practices Explore patterns like CQRS and event sourcing to create scalable, maintainable, and testable microservices

Vinicius Feitosa Pacheco

ISBN 10: 1788474031 / ISBN 13: 9781788474030

Published by Packt Publishing Limited, 2018

About this Item

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Bibliographic Details

• Title: Microservice Patterns and Best Practices Explore patterns like CQRS and event sourcing to create scalable, maintainable, and testable microservices
• Publisher: Packt Publishing Limited
• Publication Date: 2018
• Language: English
• ISBN 10: 1788474031
• ISBN 13: 9781788474030
• Binding: PAP
• Condition: New
• Weight: 795 grams

About this title

Synopsis

With this book on OpenRefine, managing and cleaning your large datasets suddenly got a lot easier! With a cookbook approach and free datasheets included, you'll quickly and painlessly improve your data managing capabilities.

Key Features

• Create links between your dataset and others in an instant
• Effectively transform data with regular expressions and the General Refine Expression Language
• Spot issues in your dataset and take effective action with just a few clicks

Book Description

Data today is like gold - but how can you manage your most valuable assets? Managing large datasets used to be a task for specialists, but the game has changed - data analysis is an open playing field. Messy data is now in your hands! With OpenRefine the task is a little easier, as it provides you with the necessary tools for cleaning and presenting even the most complex data. Once it's clean, that's when you can start finding value.

Using OpenRefine takes you on a practical and actionable through this popular data transformation tool. Packed with cookbook style recipes that will help you properly get to grips with data, this book is an accessible tutorial for anyone that wants to maximize the value of their data.

This book will teach you all the necessary skills to handle any large dataset and to turn it into high-quality data for the Web. After you learn how to analyze data and spot issues, we'll see how we can solve them to obtain a clean dataset. Messy and inconsistent data is recovered through advanced techniques such as automated clustering. We'll then show extract links from keyword and full-text fields using reconciliation and named-entity extraction.

Using OpenRefine is more than a manual: it's a guide stuffed with tips and tricks to get the best out of your data.

What you will learn

• Import data in various formats
• Explore datasets in a matter of seconds
• Apply basic and advanced cell transformations
• Deal with cells that contain multiple values
• Create instantaneous links between datasets
• Filter and partition your data easily with regular expressions
• Use named-entity extraction on full-text fields to automatically identify topics
• Perform advanced data operations with the General Refine Expression Language

About the Author

Vinicius Feitosa Pacheco has been working as a software engineer since 2007. He has diverse experience with high-performance and high-availability software architectures, with an emphasis on microservices, and is passionate about teaching and talking about them.

In the last 4 years, he has worked as an instructor in the field of software engineering techniques (including design patterns) and programming languages, such as Python, Java, and Go. He has been a speaker at large conferences such as PyCon Argentina, Pycon Colombia, EuroPython, RubyConf Brazil, the MobileConf, and QConSP.