Apache Spark is an open-source distributed general purpose cluster-computing framework. It is an unified analytics engine for the big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It is a data processing framework which quickly performs processing tasks on very large data sets, and it can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools.
Apache Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, which is maintained in a fault-tolerant way.
Apache Spark Online Training Course Content
Apache Spark
Introduction to Apache Spark
Why Spark
Batch Vs. Real Time Big Data Analytics
Batch Analytics – Hadoop Ecosystem Overview
Real Time Analytics Options
Streaming Data – Storm
In Memory Data – Spark, What is Spark?
Spark benefits to Professionals
Limitations of MR in Hadoop
Components of Spark
Spark Execution Architecture
Benefits of Apache Spark
Hadoop vs Spark
Introduction to Scala
Features of Scala
Basic Data Types of Scala
Val vs Var
Type Inference
REPL
Objects & Classes in Scala
Functions as Objects in Scala
Anonymous Functions in Scala
Higher Order Functions
Lists in Scala
Maps
Pattern Matching
Traits in Scala
Collections in Scala
Spark Core Architecture
Spark & Distributed Systems
Spark for Scalable Systems
Spark Execution Context
What is RDD
RDD Deep Dive
RDD Dependencies
RDD Lineage
Spark Application In Depth
Spark Deployment
Parallelism in Spark
Caching in Spark
Spark Internals
Spark Transformations
Spark Actions
Spark Cluster
Spark SQL Introduction
Spark Data Frames
Spark SQL with CSV
Spark SQL with JSON
Spark SQL with Database
Spark Streaming
Features of Spark Streaming
Micro Batch
Dstreams
Transformations on Dstreams
Spark Streaming Use Case 1
Spark Streaming Use Case 2
Spark Streaming Use Case 3
Spark GraphX Programming
Introduction to Graph Parallel Systems
Introduction to GraphX
Features of GraphX
GraphX Deep Dive
Graph Builder
Introducing Mllib
Using Mllib for Movie Recommendations
Analyzing Recommendation Results using Spark
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