Introduction to Big Data and Distributed Computing :
Big data analysis is future. This section of course will help you to understand, the need of distributed computation.
Introduction to data.
Data Science a vision.
Big data Introduction.
Problem with parallel computation.
Traditional parallel computation systems.
Introduction to Hadoop.
HDFS and its architecture.
◦ rmdir and rm
fsimage and edits log files.
Hadoop property files.
Introduction to MapReduce.
Shortcoming of MapReduce.
Python : Refresher
Introduction to Python
Python variables and Data Type.
Operators in Python.
Interactive mode and script base programming introduction
Python Collections (List, Dictionaries etc)
Control Flow and looping in Python
Functions in Python (Declaration, Definition Types and calling)
Object oriented Python.
Spark Introduction :
Introduction to Spark.
Spark and Hadoop (Similarity and Differences)
Spark Execution (Master Slave System , Drive, Driver manager and Executors)
Resilient Distributed dataSet (RDD)
Operations On RDD :
Creation of RDD
Transformation and Action Introduction
Some Important Transformation :
Some Important Action
Creation of Paired RDD
Some important Transformation on pairRDD
Joines and their Type
Some Important action on pair RDD
Hands on all the functions
Fault tolerance and Persistence :
Benefit of persistence
Optimizing Spark program
Introduction to partitioning
Inbuilt partitioners (Hash and Range)
Benefits of partitioning
groupByKey and reduceBykey comparison
Spark broadcasting and accumulators
IO in Spark :
Data From HDFS
Spark Streaming :
Introduction to Spark Streaming
Reading from HDFS
Push Based Receiver and Pull Based receiver
Kafka integration with Streaming.
Introduction to SparkSQL
DataFrame an Introduction.
Creation of a dataframe.
Summary statistics on DataFrame.
Aggregation on Given Data.
SparkSQL and SQL
Introduction to Hive.
Using data from Hive and HiveQL.
Optimizing SparkSQL code.
Spark Code Deployment and cluster managers.
Submitting Spark code in local mode
Submitting Spark code on StandAlone cluster manager.
Submitting Spark code on YARN
Submitting Spark code on Mesos
Note : Every part of course will be associated with hands on . A number of objective questions will always help you in scratch your brain.
Project 1 : Spark core can be used for data preparation and aggregation. Aggregation will be implemented using Spark core APIs.
For data aggregation movie lance data will be used.
Project 2 : Implementing streaming data word frequency visualization. using Kafka and Spark streaming integration.
Project 3 : Implementation of Moving average using SparkSQL.
Project 4 : Data preprocessing, data manipulation and aggregation using SparkSQL. It will be done using Real time data.