Explore Apache Spark and harness its power for rapid large-scale data processing.
Apache Spark has become a popular technology for big data processing and analysis in many industries./academy/contact
Who is this for?
Our Spark course is designed for beginner and intermediate professionals who are already familiar with general programming and (big) data concepts and want to learn how to use Apache Spark to process and analyze large datasets.
Program goal - What you will take away from the course
The goal of this course is to provide you with a comprehensive understanding of Apache Spark and its ecosystem. By the end of the course, you will be able to:
Understand the benefits of distributed computing and how Apache Spark works
Use Apache Spark to process and analyze large datasets in a scalable and fault-tolerant way
Understand the key concepts of Apache Spark, such as RDDs, DataFrames, and Spark SQL
Use Apache Spark to build data pipelines and perform ETL operations
Optimize Apache Spark performance and tune Spark applications
Introduction to Apache Spark and distributed computing
Embark on your journey into Apache Spark, the powerful distributed computing platform, and gain an understanding of its role in processing and analyzing large-scale data sets.
Apache Spark architecture and components
Delve into the architecture and core components of Apache Spark, providing a comprehensive understanding of how the platform enables efficient distributed data processing and analytics.
Processing and analyzing data with Apache Spark RDDs
Master the art of using Resilient Distributed Datasets (RDDs) in Apache Spark, learning how to process and analyze data across distributed computing clusters effectively.
Structured data processing with DataFrames and Spark SQL
Explore structured data processing with DataFrames and Spark SQL, harnessing the power of these features to simplify data manipulation and querying tasks in Spark.
Building data pipelines and performing ETL operations with Apache Spark
Learn how to build robust data pipelines and perform Extract, Transform, Load (ETL) operations with Apache Spark, streamlining your data processing and analytics workflows.
Optimizing Apache Spark
Become proficient in optimizing Spark performance and tuning applications, ensuring that your distributed computing tasks run efficiently and effectively.
Meet the Creators
Chief Technology Officer & Principal Big Data Solutions Architect Lead, Ultra Tendency
Professional Software Architect, Ultra Tendency
Senior Lead Big Data Developer & Berlin Territory Manager, Ultra Tendency
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