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The Apache Spark Programming with Databricks course is designed to provide learners with a comprehensive understanding of the Apache Spark framework and its integration with the Databricks platform. This course is particularly beneficial for those seeking to gain expertise in big data processing and analytics, aiming for an Apache Spark Databricks certification.
Starting with a Spark overview in Module 1, the curriculum delves into the specifics of the Databricks platform in Module 2, setting the stage for advanced concepts. Modules 3 through 12 cover a wide range of topics including Spark SQL, DataFrame operations, handling date-time data, complex data types, user-defined functions (UDFs), and the internal workings of Spark. Learners will also explore query optimization, partitioning strategies, the Streaming API for real-time data processing, and Delta Lake for reliable data storage.
By the end of this Apache Spark programming with Databricks course, participants will have a solid foundation to build scalable data applications and pursue professional certification.
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Classroom Training price is on request
USD 199+
USD 19+
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To ensure success in the Apache Spark Programming with Databricks course, the following prerequisites are recommended for participants:
These prerequisites are intended to provide you with the foundational skills necessary to grasp the course material effectively. If you are new to some of these concepts, we encourage you to explore introductory resources or courses provided by Koenig Solutions to prepare you for a more advanced study of Apache Spark with Databricks.
The Apache Spark Programming with Databricks course equips participants with advanced data processing and optimization skills using Spark and Databricks.
Target Audience and Job Roles:
Introduction: This Apache Spark Programming with Databricks course equips students with the skills to harness the full potential of Apache Spark for big data processing and analytics on the Databricks platform.
Learning Objectives and Outcomes: