Apache Spark 3 for Data Engineering and Analytics with Python: Learn how to use Python and PySpark 3.0.1 for Data Engineering/Analytics (Databricks) - Beginner to Ninja

David Mngadi

NZ$80.99

5 (2 Ratings)

Video Aug 2021 8hrs 30mins 1st Edition

David Mngadi

NZ$80.99

5 (2 Ratings)

Video Aug 2021 8hrs 30mins 1st Edition

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Key benefits

Apply PySpark and SQL concepts to analyze data
Understand the Databricks interface and use Spark on Databricks
Learn Spark transformations and actions using the RDD (Resilient Distributed Datasets) API

Description

Apache Spark 3 is an open-source distributed engine for querying and processing data. This course will provide you with a detailed understanding of PySpark and its stack. This course is carefully developed and designed to guide you through the process of data analytics using Python Spark. The author uses an interactive approach in explaining keys concepts of PySpark such as the Spark architecture, Spark execution, transformations and actions using the structured API, and much more. You will be able to leverage the power of Python, Java, and SQL and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Apache Spark architecture and how to set up a Python environment for Spark. Followed by the techniques for collecting, cleaning, and visualizing data by creating dashboards in Databricks. You will learn how to use SQL to interact with DataFrames. The author provides an in-depth review of RDDs and contrasts them with DataFrames. There are multiple problem challenges provided at intervals in the course so that you get a firm grasp of the concepts taught in the course. The code bundle for this course is available here: https://github.com/PacktPublishing/Apache-Spark-3-for-Data-Engineering-and-Analytics-with-Python-

Who is this book for?

This course is designed for Python developers who wish to learn how to use the language for data engineering and analytics with PySpark. Any aspiring data engineering and analytics professionals. Data scientists/analysts who wish to learn an analytical processing strategy that can be deployed over a big data cluster. Data managers who want to gain a deeper understanding of managing data over a cluster.