Practical MongoDB Aggregations (eBook)
312 Seiten
Packt Publishing (Verlag)
978-1-83508-684-1 (ISBN)
Officially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks.
This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale.
Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you'll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.
Begin your journey toward efficient data manipulation with this robust technical guide and enhance your aggregation skills while building efficient pipelines for a variety of tasksKey FeaturesBuild effective aggregation pipelines for increased productivity and performanceSolve common data manipulation and analysis problems with the help of practical examplesLearn essential strategies to aggregate time series data in financial datasets and IoTPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionOfficially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks. This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale. Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you ll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.What you will learnDevelop dynamic aggregation pipelines tailored to changing business requirementsMaster essential techniques to optimize aggregation pipelines for rapid data processingAchieve optimal efficiency for applying aggregations to vast datasets with effective sharding strategiesEliminate the performance penalties of processing data externally by filtering, grouping, and calculating aggregated values directly within the databaseUse pipelines to help you secure your data access and distributionWho this book is forThis book is for intermediate-level developers, architects, analysts, engineers, and data scientists who are interested in learning about aggregation capabilities in MongoDB. Working knowledge of MongoDB is needed to get the most out of this book.]]>
Erscheint lt. Verlag | 7.9.2023 |
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Vorwort | Asya Kamsky |
Sprache | englisch |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Mathematik / Informatik ► Informatik ► Theorie / Studium | |
ISBN-10 | 1-83508-684-5 / 1835086845 |
ISBN-13 | 978-1-83508-684-1 / 9781835086841 |
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