Apache Spark Programming with Databricks Course Overview

Apache Spark Programming with Databricks Course Overview

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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  • Live Online Training (Duration : 16 Hours)
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Classroom Training price is on request

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Course Prerequisites

To ensure success in the Apache Spark Programming with Databricks course, the following prerequisites are recommended for participants:


  • Basic understanding of programming principles and data structures.
  • Familiarity with a programming language, preferably Scala or Python, as these are commonly used with Apache Spark.
  • Knowledge of SQL and relational database concepts.
  • An introductory level of knowledge in big data concepts and distributed computing.
  • Experience with command-line interfaces and basic Linux commands can be helpful.

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.


Target Audience for Apache Spark Programming with Databricks

  1. The Apache Spark Programming with Databricks course equips participants with advanced data processing and optimization skills using Spark and Databricks.


  2. Target Audience and Job Roles:


  • Data Engineers
  • Data Scientists
  • Big Data Analysts
  • Software Developers with a focus on data processing
  • Machine Learning Engineers
  • Data Architects
  • IT Professionals interested in big data technologies
  • Technical Team Leads managing data processing projects
  • System Administrators who manage and maintain Apache Spark clusters
  • Database Administrators looking to expand into big data solutions
  • DevOps Engineers involved in deployment of data processing pipelines
  • Graduates and Professionals seeking to upskill in distributed computing


Learning Objectives - What you will Learn in this Apache Spark Programming with Databricks?

  1. 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.

  2. Learning Objectives and Outcomes:

  • Gain an understanding of Apache Spark’s architecture and its place in the big data ecosystem.
  • Navigate and utilize the Databricks platform for Spark application development and deployment.
  • Master the use of Spark SQL for performing complex data analysis and querying structured data.
  • Learn to read, write, transform, and aggregate data effectively using DataFrames and Datasets.
  • Manipulate date and time data for time-based analyses and processing.
  • Work with complex data types, such as arrays, maps, and structs within Spark.
  • Create and deploy User-Defined Functions (UDFs) and leverage vectorized UDFs for optimized performance.
  • Comprehend Spark's internal execution mechanisms to write efficient and performant Spark applications.
  • Understand and apply techniques for query optimization to improve Spark job execution times.
  • Implement the right partitioning strategies to enhance application scalability and parallelism.
  • Develop robust streaming applications using Spark's Structured Streaming API.
  • Integrate with Delta Lake for reliable data storage and to enable ACID transactions in Spark.