Big Data with Apache Spark

LIVE

36 Hours

Course offered by CourseZoo.com

0 review

overview batches reviews

This course is best suited for those who want to gain Big Data analytics skills to:

Analyze huge datasets quickly
Build, deploy and run Spark applications on Spark clusters
Process continual streams of data with Spark Streaming
Frame big data analysis problems as Apache Spark scripts
Develop distributed code using the Scala programming language
Optimize Spark jobs through partitioning, caching, and other techniques
Transform structured data using SparkSQL and DataFrames
Traverse and analyze graph structures using GraphX

Â

Course Outline

Introduction to Big Data & Apache Spark
Introduction of Big Data Introduction & a brief history of Apache Spark Components of Apache Spark unified stack Who uses Apache Spark?
Getting started with Apache Spark
Downloading & installing Apache Spark Running the examples & shell (Python & Scala) Introduction to core Apache Spark concepts
Introduction to Online Lab
What is Online Lab Components of Online Lab Logging into Online Lab First Hands-On using Online Lab
Understanding resilient distributed datasets (RDD)
RDD basics Creating RDDs Working with RDD operations Passing functions to Apache Spark Common transformations and actions Persistence (caching)
Working with key/value pairs
Creating pair RDDs Transformations on pair RDDs Actions available on pair RDDs Data partitioning (advanced)
Loading and saving your data
Various file formats & file systems Structured data with Apache Spark SQL Databases
Advanced Apache Spark programming
Introduction Accumulators Broadcast variables Working on a per-partition basis Piping to external programs Numeric RDD operations
Running Apache Spark on a cluster
Introduction Apache Spark runtime architecture Submitting applications with Apache Spark-submit Packaging your code and dependencies Scheduling within and between Apache Spark applications Cluster managers
Introduction to Apache Spark libraries
Understanding Apache Spark SQL Using Apache Spark SQL in applications Machine learning basics Machine learning with MLlib, GraphX
Apache Spark streaming
A simple example Architecture and abstraction Transformations & output operations Input sources Streaming UI Performance considerations Kafka basics