Data Engineering Design Patterns: Recipes for Solving the Most Common Data Engineering Problems - Softcover
Inhaltsangabe
Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.
Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.
Throughout this journey, you'll use open source data tools and public cloud services to apply each pattern. You'll learn:
- Challenges data engineers face and their impact on data systems
- How these challenges relate to data system components
- Useful applications of data engineering patterns
- How to identify and fix issues with your current data components
- Technology-agnostic solutions to new and existing data projects, with open source implementation examples
Bartosz Konieczny is a freelance data engineer who's been coding since 2010. He's held various senior hands-on positions that allowed him to work on many data engineering problems in batch and stream processing.
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Über die Autorin bzw. den Autor
Bartosz is a freelance data engineer enthusiast who has been coding since 2010. He has held various senior hands-on positions that helped him work on many data engineering problems, such as sessionization, data ingestion, data cleansing, ordered data processing, or data migration. He enjoys solving data challenges with public cloud services and Open Source technologies, especially Apache Spark, Apache Kafka, Apache Airflow, and Delta Lake.
Besides that, you can read his blog posts at waitingforcode.com, or improve your data engineering skills with one of his courses or training. Bartosz is also an occasional speaker at conferences and meetups, including Data+AI Summit, Big Data Technology Warsaw Summit, or NDC Porto.
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Data Engineering Design Patterns: Recipes for Solving the Most Common Data Engineering Problems
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Paperback. Zustand: new. Paperback. Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies are still trying to solve problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.Throughout this journey, you'll use open source data tools and public cloud services to see how to put each pattern into practice. You'll learn:Challenges data engineers face and their impact on data systemsHow these challenges relate to data system componentsWhat data engineering patterns are forHow to identify and fix issues with your current data componentsTechnology-agnostic solutions to new and existing data projectsHow to implement patterns with Apache Airflow, Apache Spark, Apache Flink, and Delta LakeBartosz Konieczny is a freelance data engineer who's been coding for more than 15 years. He's held various senior hands-on positions that helped him work on many data engineering problems in batch and stream processing. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, and idempotency. Bartosz Konieczny shows you how to of build reliable end-to-end data engineering projects, from data ingestion to data observability. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781098165819
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Paperback. Zustand: New. Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies are still trying to solve problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.Throughout this journey, you'll use open source data tools and public cloud services to see how to put each pattern into practice. You'll learn:Challenges data engineers face and their impact on data systemsHow these challenges relate to data system componentsWhat data engineering patterns are forHow to identify and fix issues with your current data componentsTechnology-agnostic solutions to new and existing data projectsHow to implement patterns with Apache Airflow, Apache Spark, Apache Flink, and Delta LakeBartosz Konieczny is a freelance data engineer who's been coding for more than 15 years. He's held various senior hands-on positions that helped him work on many data engineering problems in batch and stream processing. Bestandsnummer des Verkäufers LU-9781098165819
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