Apache Spark™ - Lightning-Fast Cluster Computing
Now Make your SuperComputing Cluster 100x Faster and Real Time Analysis with Spark
Apache Spark is the next-generation successor to MapReduce. Apache Spark enables participants to build complete, unified Big Data applications combining batch, streaming, and interactive analytics on all their data. With Spark, developers 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.
Apache Spark : BigData Real Time Analysis
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Apache Spark is the next-generation successor to MapReduce. Spark is a powerful, opensource processing engine for data in the Hadoop cluster, optimized for speed, ease of use,and sophisticated analytics. The Spark framework supports streaming data processing and complex, iterative algorithms, enabling applications to run up to 100x faster than traditional Hadoop MapReduce programs.
- Why Spark?
- Spark Basics
- Working with RDDs
- The Hadoop Distributed File System
- Running Spark on a Cluster
- Parallel Programming with Spark
- Caching and Persistence
- Writing Spark Applications
- Spark Streaming
- Common Spark Algorithms
- Improving Spark Performance
- Writing Spark Applications
- Problems with Traditional Large-Scale Systems
- Introducing Spark
-What is Apache Spark? .
-Using the Spark Shell .
-Resilient Distributed Datasets (RDDs)
-Functional Programming with Spark
- RDD Operations
- Key-Value Pair RDDs
- MapReduce and Pair RDD Operations
- Why HDFS?
- HDFS Architecture
- Using HDFS
- Overview
- A Spark Standalone Cluster
- The Spark Standalone Web UI
-RDD Partitions and HDFS Data Locality
-Working With Partitions
-Executing Parallel Operations
- RDD Lineage.
- Caching Overview
- Distributed Persistence
-Spark Applications vs. Spark Shell
-Creating the SparkContext
-Configuring Spark Properties
-Example: Streaming Word Count>
-Other Streaming Operations
-Sliding Window Operations
-Developing Spark Streaming Applications
-Iterative Algorithms
-Graph Analysis
-Machine Learning
-Shared Variables: Broadcast Variables
-Shared Variables: Accumulators
-Common Performance Issues
-Spark Applications vs. Spark Shell
-Creating the SparkContext
-Configuring Spark Properties
-Building and Running a Spark Application
-Logging
- This course is best suited to developers and engineers who have programming experience
- Deep knowledge of Apache Spark
- Knowledge of Java / Scala is strongly recommended and is required to complete the hands-on exercises. Prior knowledge of Apache Hadoop is recommended
DELIVERANCES BY LINUXWORLD :
- Training Certificate by LinuxWorld - Training & Development Center
- Project Certificate by LinuxWorld Informatics Pvt. Ltd. (if prepared under any case study)
- Latest Software for Spark and Scala
- Resources - Software & Tools
- Life Time Support
BENEFITS @ LINUXWORLD:
- 24 x 7 Wi Fi Enabled Lab Facility
- Life Time Membership Card
- Expert faculty having 12 + yrs of industrial exposure
- Practical Implementation by having hands on experience on live demo and project
- Job Assistance
Further Information
If you would like to know more about this course please ping us @ :
call us on 0091 9829105960 / 0091 141 2501609
send an email to training@lwindia.com or training@linuxworldindia.org
