Data Ingestion with Python Cookbook

Deploy your data ingestion pipeline, orchestrate, and monitor efficiently to prevent loss of data and qualityPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesHarness best practices to create a Python and PySpark data ingestion pipelineSeamlessly automate and orchestrate your data pipelines using Apache AirflowBuild a monitoring framework by integrating the concept of data observability into your pipelinesBook DescriptionData Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.What you will learnImplement data observability using monitoring toolsAutomate your data ingestion pipelineRead analytical and partitioned data, whether schema or non-schema basedDebug and prevent data loss through efficient data monitoring and loggingEstablish data access policies using a data governance frameworkConstruct a data orchestration framework to improve data qualityWho this book is forThis book is for data engineers and data enthusiasts seeking a comprehensive understanding of the data ingestion process using popular tools in the open source community. For more advanced learners, this book takes on the theoretical pillars of data governance while providing practical examples of real-world scenarios commonly encountered by data engineers.]]>