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What you'll be learning

  • Installing and configuring the Spark Cluster
  • Use DataFrames and Structured Streaming in Spark 3
  • Understand how Spark Streaming lets your process continuous streams of data in real time
  • Use Spark's Resilient Distributed Datasets to process and analyze large data sets across many CPU's
  • Understand how Spark SQL lets you work with structured data
  • Share information between nodes on a Spark cluster using broadcast variables and accumulators

  • Use the MLLib machine learning library to answer common data mining questions
  • Implement iterative algorithms such as breadth-first-search using Spark
  • Tune and troubleshoot large jobs running on a cluster
  • Understand how the GraphX library helps with network analysis problems
  • Test and troubleshoot Apache Spark.

 Spark Course Content

  • Introduction to Apache Spark 
  • Standalone deployment mode
  • Spark with kubernetes
  • Spark SQL, Datasets, Dataframes
  • Structured Streaming
  • Spark Streaming
  • MLLib Applying machine learning algorithms
  • GraphX processing graphs
  • SparkR Processing data with R
  • PySpark Processing data using Python
  • Cluster Concepts and deployment
  • Submitting Applications packaging and deploying applications
  • Monitoring
  • Tuning
  • Job Scheduling
  • Security of Spark Cluster
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