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Introduction To Big Data And Spark
Learn how to apply data science techniques using parallel programming during Spark training, to explore big (and small) data.
- Introduction to Big Data
- Challenges with Big Data
- 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?
- Modes of Spark
- Spark Installation Demo
- Overview of Spark on a cluster
- Spark Standalone Cluster
Spark Baby Steps
Learn how to invoke spark shell, build spark project with sbt, distributed persistence and much more…in this module.
- Invoking Spark Shell
- Creating the Spark Context
- Loading a File in Shell
- Performing Some Basic Operations on Files in Spark Shell
- Building a Spark Project with sbt
- Running Spark Project with sbt
- Caching Overview
- Distributed Persistence
- Spark Streaming Overview
- Example: Streaming Word Count
Playing With RDDs In Spark
The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel.
- Spark Transformations in RDD
- Actions in RDD
- Loading Data in RDD
- Saving Data through RDD
- Spark Key-Value Pair RDD
- Map Reduce and Pair RDD Operations in Spark
- Scala and Hadoop Integration Hands on
Shark – When Spark Meets Hive
Shark is a component of Spark, an open source, distributed and fault-tolerant, in-memory analytics system, that can be installed on the same cluster as Hadoop. This module of spark training, will give insights about Shark.
- Why Shark?
- Installing Shark
- Running Shark
- Loading of Data
- Hive Queries through Spark
- Testing Tips in Scala
- Performance Tuning Tips in Spark
- Shared Variables: Broadcast Variables
- Shared Variables: Accumulators