The goal of the Apache Spark Basics course is to provide participants with a solid understanding of Apache Spark and its fundamental concepts. By the end of the course, participants should be able to understand the challenges of big data processing and the advantages of Spark. They will gain comprehension of Spark's architecture and its components, such as the driver, executor, and cluster manager. Participants will also learn how to work with Resilient Distributed Datasets (RDDs) and perform various transformations and actions on them. Additionally, they will acquire knowledge of Spark Streaming for real-time data processing and gain the ability to integrate Spark with other technologies like Flume, Kafka, and Cassandra. Through hands-on exercises using PySpark, participants will develop practical skills and gain the confidence to effectively utilize Apache Spark for big data processing and analytics tasks.
DStream (Discretized Stream) operations in Spark Streaming
Windowed operations
Stateful processing using updateStateByKey()
Handling data sources (Flume, Kafka) and sinks (HDFS, Cassandra) in Spark Streaming
Hands-on exercises with Spark Streaming
Integration with Flume, Kafka, and Cassandra
Introduction to Apache Flume and its integration with Spark
Overview of Flume's event-based data ingestion
Setting up Flume agents and Spark integration
Integration of Apache Kafka with Spark Streaming
Overview of Kafka's distributed publish-subscribe messaging system
Configuring Kafka and Spark integration for real-time data processing
Introduction to Apache Cassandra and its integration with Spark
Overview of Cassandra's distributed NoSQL database
Connecting Spark to Cassandra for data storage and retrieval
Dauer/zeitlicher Ablauf:
2 Tage
Zielgruppe:
Data Engineers: Data engineers responsible for processing and analyzing large datasets can benefit from learning Apache Spark to leverage its distributed computing capabilities.
Data Scientists: Data scientists looking to work with big data and perform advanced analytics can enhance their skills by gaining knowledge of Apache Spark and its machine learning library, MLlib.
Software Developers: Software developers interested in distributed computing and working with big data can expand their skill set by learning Apache Spark and PySpark.
Data Analysts: Data analysts who want to analyze and process large datasets efficiently can learn Apache Spark to improve their data processing workflows.
IT Professionals: IT professionals involved in managing big data infrastructure and processing can benefit from understanding Apache Spark's architecture and its integration with other technologies.
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