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Karl Wilhelm
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A practical guide to building scalable data pipelines and advanced analytics workflows using Databricks and Apache Spark.
It provides a blend of theory and practical examples with PySpark, SQL, and Databricks.
This guide is a hands-on manual for leveraging Databricks' unified platform atop Apache Spark to engineer robust data systems. It demystifies Spark's distributed computing for ETL pipelines, real-time analytics, and ML workflows. The book discusses collapsing data silos, accelerating time-to-insight, and building automated systems that scale.
A practical guide to building scalable data pipelines and advanced analytics workflows using Databricks and Apache Spark.
It provides a blend of theory and practical examples with PySpark, SQL, and Databricks.
This guide is a hands-on manual for leveraging Databricks' unified platform atop Apache Spark to engineer robust data systems. It demystifies Spark's distributed computing for ETL pipelines, real-time analytics, and ML workflows. The book discusses collapsing data silos, accelerating time-to-insight, and building automated systems that scale.
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