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Cloudera Developer Training for Apache Spark

Course content updated by LearnQuest
2,595 USD
3 Days
Classroom Training, Online Training
Cloudera Training
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Course Description


Cloudera Universityâ??s three-day training course for Apache Spark enables participants to build complete, unified big data applications combining batch, streaming, and interactive analytics on all their data. With Spark, evelopers can write sophisticated parallel applications to execute faster decisions, better decisions, and real-time actions, applied to a wide variety of use cases, architectures, and industries.



  • Using the Spark shell for interactive data analysis
  • The features of Sparkâ??s Resilient Distributed Datasets
  • How Spark runs on a cluster
  • How Spark parallelizes task execution
  • Writing Spark applications
  • Processing streaming data with Spark
  • Audience





    Introduction to Spark

    • What is Spark?
    • Review: From Hadoop MapReduce to Spark
    • Review: HDFS
    • Review: YARN
    • Spark Overview

    Spark Basics

    • Using the Spark Shell
    • RDDs (Resilient Distributed Datasets)
    • Functional Programming in Spark

    Working with RDDs in Spark

    • Creating RDDs
    • Other General RDD Operations

    Aggregating Data with Pair RDDs

    • Key-Value Pair RDDs
    • Map-Reduce
    • Other Pair RDD Operations

    Writing and Deploying Spark Applications

    • Spark Applications vs. Spark Shell
    • Creating the SparkContext
    • Building a Spark Application (Scala and Java)
    • Running a Spark Application
    • The Spark Application Web UI
    • Hands-On Exercise: Write and Run
    • Spark Application
    • Configuring Spark Properties
    • Logging

    Parallel Processing

    • Review: Spark on a Cluster
    • RDD Partitions
    • Partitioning of File-based RDDs
    • HDFS and Data Locality
    • Executing Parallel Operations
    • Stages and Tasks

    Spark RDD Persistence

    • RDD Lineage
    • RDD Persistence Overview
    • Distributed Persistence

    Basic Spark Streaming

    • Spark Streaming Overview
    • Example: Streaming Request Count
    • DStreams
    • Developing Spark Streaming Applications

    Advanced Spark Streaming

    • Multi-Batch Operations
    • State Operations
    • Sliding Window Operations
    • Advanced Data Sources

    Common Patterns in Spark Data Processing

    • Common Spark Use Cases
    • Iterative Algorithms in Spark
    • Graph Processing and Analysis
    • Machine Learning
    • Example: k-means

    Improving Spark Performance

    • Shared Variables: Broadcast Variables
    • Shared Variables: Accumulators
    • Common Performance Issues
    • Diagnosing Performance Problems

    Spark SQL and DataFrames

    • Spark SQL and the SQL Context
    • Creating DataFrames
    • Transforming and Querying DataFrames
    • Saving DataFrames
    • DataFrames and RDDs
    • Comparing Spark SQL, Impala and Hive-on-Spark


    • HBase for Developers

      • Duration: 3 Days
      • Delivery Format: Classroom Training, Online Training
      • Price: 2,100.00 USD
    2020 Top 20 Training Industry Company - IT Training

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