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Spark V2 for Developers

2,100 USD
3 Days
Classroom Training, Online Training

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Prices reflect a 22.5% discount for IBM employees (wherever applicable).
Prices reflect a 24% discount for Kyndryl employees (wherever applicable).
Prices reflect the Accenture employee discount.
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Course Description


This Spark for Developers course is designed to to use Spark for data analysis and write Spark applications. This course covers the latest Spark v2 features.


Upon completion of the Spark for Developers course, students will be able to learn Spark eco-system:
  • Spark Shell
  • Spark internals
  • Spark Data structures : RDDs, Dataframes, Datasets
  • Spark APIs
  • Spark SQL
  • Spark and Hadoop
  • Spark MLLib
  • Spark Graphx
  • Spark streaming


  • Developer / Data Analysts


    • Familiarity with either Java, Scala, Python (labs in Scala and Python)
    • Basic understanding of Linux development environment (command line navigation / running commands)


Scala primer
  • A quick introduction to Scala
    • Labs : Getting know Scala
  • Spark Basics
  • Big Data, Hadoop, Spark
  • What’s new in Spark v2
  • Spark concepts and architecture
  • Spark eco system (core, spark sql, mlib, streaming)
    • Labs : Installing and running Spark
  • Spark Shell
  • Spark shell
  • Spark web UIs
  • Analyzing dataset – part 1
    • Labs: Spark shell exploration
RDDs (Condensed coverage)
  • RDDs concepts
  • Partitions
  • RDD Operations / transformations
  • More detailed coverage if required : RDD types, Key-Value pair RDDs, MapReduce on RDD
    • Labs : Unstructured data analytics using RDDs
Data Model Concepts
  • Partitions
  • Distributed processing
  • Failure handling
  • Caching and Persistence
Spark Dataframes & Datasets
  • Instroduction to Dataframe / Dataset
  • Programming in Dataframe / Dataset API
  • Loading structured data using Dataframes
    • Labs : Dataframes, Datasets, Caching
Spark SQL
  • Spark SQL concepts and overview
  • Defining tables and importing datasets
  • Querying data using SQL
  • Handling various storage formats : JSON / Parquet / ORC
    • Labs : querying structured data using SQL; evaluating data formats
Spark API programming (Scala / Python)
  • Introduction to Spark API
  • Submitting the first program to Spark
  • Debugging / logging
  • Configuration properties
    • Labs : Programming in Spark API, Submitting jobs
Spark and Hadoop
  • Hadoop Primer : HDFS / YARN
  • Hadoop + Spark architecture
  • Running Spark on Hadoop YARN
  • Processing HDFS files using Spark
  • Spark & Hive
Machine Learning (ML / MLib)
  • Machine Learning primer
  • Machine Learning in Spark : MLib / ML
  • Spark ML overview (newer Spark2 version)
  • Algorithms : Clustering, Classifications, Recommendations
    • Labs : Writing ML applications
  • GraphX library overview
  • GraphX APIs
    • Labs : Processing graph data using Spark
Spark Streaming
  • Streaming concepts
  • Evaluating Streaming platforms
  • Spark Streaming library overview
  • Streaming operations
  • Sliding window operations
  • Structured Streaming
  • Continuous Streaming
  • Spark & Kafka Streaming
    • Labs : Writing spark streaming applications
Spark in real world
  • Highlight some Spark use cases in real world
  • Solr for Developers

    • Duration: 3 Days
    • Delivery Format: Classroom Training, Online Training
    • Price: 2,100.00 USD
  • Machine Learning Essentials

    • Duration: 3 Days
    • Delivery Format: Classroom Training, Online Training
    • Price: 2,100.00 USD
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Self-Paced Training Info

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