Data Ingestion with Python Cookbook A practical guide to ingesting, monitorin... - Printable Version +- Softwarez.Info - Software's World! (https://softwarez.info) +-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone) +--- Forum: E-Books (https://softwarez.info/Forum-E-Books) +--- Thread: Data Ingestion with Python Cookbook A practical guide to ingesting, monitorin... (/Thread-Data-Ingestion-with-Python-Cookbook-A-practical-guide-to-ingesting-monitorin) |
Data Ingestion with Python Cookbook A practical guide to ingesting, monitorin... - Farid - 06-07-2023 Data Ingestion with Python Cookbook | 616 | Gláucia Esppenchutz | 2023 | Packt Publishing Pvt Ltd | B0C3CQDYHW Deploy your own data ingestion pipeline, orchestrate, and monitor efficiently to prevent loss of data and quality. Key Features
Book Description Data Ingestion with Python Cookbook brings a practical way to design and apply data ingestion pipelines, providing real-world examples with the most reputed open-source tools available on the market, and bringing enlightenment to questions or obstacles. You will be introduced to designing and working with or without data schemas, and creating monitored pipelines with airflow and data observability principles, while following the best practices. The book will further address the challenges to read different data sources or data formats. You will then progress to gain a broad understanding of the best practices for logging errors, how to identify/solve them, data orchestration, monitoring, and where to store the logs for further consultation. By the end of the book, You will have a complete automated set to start ingesting and monitoring the pipeline, making it easier to plug into the further steps of the ETL process later. What you will learn
Who This Book Is For This book is for Data Engineers and data enthusiasts who want to have a better understanding of the process of ingesting data using the most popular tools in the open-source community. For more advanced learners, this book takes on the theoretical pillars of Data Governance and gives practical examples of real scenarios frequently seen on a daily basis by data engineers. Table of Contents
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