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Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala

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Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala Eric Tome, Rupam Bhattacharjee, David Radford - okladka książki

Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala Eric Tome, Rupam Bhattacharjee, David Radford - okladka książki

Serie wydawnicze:
Hands-on
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
300
Dostępne formaty:
     PDF
     ePub

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Most data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount.
This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users.
By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.
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O autorach książki

Eric Tome has over 25 years of experience working with data. He has contributed to and led teams that ingested, cleansed, standardized, and prepared data used by business intelligence, data science, and operations teams. He has a background in Mathematics and currently works as a Solutions Architect for Databricks, helping customers solve their data and AI challenges.
Rupam Bhattacharjee works as a Lead Data Engineer at IBM. He has architected and developed data pipelines processing massive structured and unstructured data using Spark and Scala for on-prem Hadoop and k8s clusters on the public cloud. He has a degree in Electrical Engineering.
David Radford has worked in big data for over ten years with a focus on cloud technologies. He led consulting teams for multiple years completing migrations from legacy systems to modern data stacks. He holds a Master's degree in Computer Science and works as a Solutions Architect at Databricks.

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