Use Case bobsrockets package
Introduction to Scala packages and imports, the selective imports, the Scala test classes, introduction to JUnit test class, JUnit interface via JUnit 3 suite for Scala test, packaging of Scala applications in Directory Structure, example of Spark Split and Spark Scala.
Spark Course Content
Introduction to Spark
Introduction to Spark, how Spark overcomes the drawbacks of working MapReduce, understanding in-memory MapReduce,interactive operations on MapReduce, Spark stack, fine vs. coarse grained update, Spark stack,Spark Hadoop YARN, HDFS Revision, YARN Revision, the overview of Spark and how it is better Hadoop, deploying Spark without Hadoop,Spark history server, Cloudera distribution.
Spark installation guide,Spark configuration, memory management, executor memory vs. driver memory, working with Spark Shell, the concept of Resilient Distributed Datasets (RDD), learning to do functional programming in Spark, the architecture of Spark.
Working with RDDs in Spark
Spark RDD, creating RDDs, RDD partitioning, operations & transformation in RDD,Deep dive into Spark RDDs, the RDD general operations, a read-only partitioned collection of records, using the concept of RDD for faster and efficient data processing,RDD action for Collect, Count, Collectsmap, Saveastextfiles, pair RDD functions.
Aggregating Data with Pair RDDs
Understanding the concept of Key-Value pair in RDDs, learning how Spark makes MapReduce operations faster, various operations of RDD,MapReduce interactive operations, fine & coarse grained update, Spark stack.
Writing and Deploying Spark Applications
Comparing the Spark applications with Spark Shell, creating a Spark application using Scala or Java, deploying a Spark application,Scala built application,creation of mutable list, set & set operations, list, tuple, concatenating list, creating application using SBT,deploying application using Maven,the web user interface of Spark application, a real world example of Spark and configuring of Spark.
Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions, file-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations,comparing repartition & coalesce, RDD actions.
Spark RDD Persistence
The execution flow in Spark, Understanding the RDD persistence overview,Spark execution flow & Spark terminology, distribution shared memory vs. RDD, RDD limitations, Spark shell arguments,distributed persistence, RDD lineage,Key/Value pair for sorting implicit conversion like CountByKey, ReduceByKey, SortByKey, AggregataeByKey
Spark Streaming & Mlib
Spark Streaming Architecture, Writing streaming programcoding, processing of spark stream,processing Spark Discretized Stream (DStream), the context of Spark Streaming, streaming transformation, Flume Spark streaming, request count and Dstream, multi batch operation, sliding window operations and advanced data sources. Different Algorithms, the concept of iterative algorithm in Spark, analyzing with Spark graph processing, introduction to K-Means and machine learning, various variables in Spark like shared variables, broadcast variables, learning about accumulators.
Improving Spark Performance
Introduction to various variables in Spark like shared variables, broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems.
Spark SQL and Data Frames
Learning about Spark SQL, the context of SQL in Spark for providing structured data processing, JSON support in Spark SQL, working with XML data, parquet files, creating HiveContext, writing Data Frame to Hive, reading JDBC files, understanding the Data Frames in Spark, creating Data Frames, manual inferring of schema, working with CSV files, reading JDBC tables, Data Frame to JDBC, user defined functions in Spark SQL, shared variable and accumulators, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.
Learning about the scheduling and partitioning in Spark,hash partition, range partition, scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling,Map partition with index, the Zip, GroupByKey, Spark master high availability, standby Masters with Zookeeper, Single Node Recovery With Local File System, High Order Functions.
Apache Storm Course Content
Understanding Architecture of Storm
Big Data characteristics, understanding Hadoop distributed computing, the Bayesian Law, deploying Storm for real time analytics, the Apache Storm features, comparing Storm with Hadoop, Storm execution, learning about Tuple, Spout, Bolt.
Installation of Apache storm
Installing the Apache Storm, various types of run modes of Storm.
Introduction to Apache Storm
Understanding Apache Storm and the data model.
Apache Kafka Installation
Installation of Apache Kakfa and its configuration.
Apache Storm Advanced
Understanding of advanced Storm topics like Spouts, Bolts, Stream Groupings, Topology and its Life cycle, learning about Guaranteed Message Processing.
Various Grouping types in Storm, reliable and unreliable messages, Bolt structure and life cycle, understanding Trident topology for failure handling, process, Call Log Analysis Topology for analyzing call logs for calls made from one number to another.
Overview of Trident
Understanding of Trident Spouts and its different types, the various Trident Spout interface and components, familiarizing with Trident Filter, Aggregator and Functions, a practical and hands-on use case on solving call log problem using Storm Trident.
Storm Components & classes
Various components, classes and interfaces in storm like – Base Rich Bolt Class, i RichBolt Interface, i RichSpout Interface, Base Rich Spout class and the various methodology of working with them.
Understanding Cassandra, its core concepts, its strengths and deployment.
Twitter Boot Stripping, detailed understanding of Boot Stripping, concepts of Storm, Storm Development Environment.
Apache Spark – Scala Project
Project 1: Movie RecommendationTopics – This is a project wherein you will gain hands-on experience in deploying Apache Spark for movie recommendation. You will be introduced to the Spark Machine Learning Library, a guide to MLlib algorithms and coding which is a machine learning library. Understand how to deploy collaborative filtering, clustering, regression, and dimensionality reduction in MLlib. Upon completion of the project you will gain experience in working with streaming data, sampling, testing and statistics.Project 2: Twitter API Integration for tweet AnalysisTopics – With this project you will learn to integrate Twitter API for analyzing tweets. You will write codes on the server side using any of the scripting languages like PHP, Ruby or Python, for requesting the Twitter API and get the results in JSON format. You will then read the results and perform various operations like aggregation, filtering and parsing as per the need to come up with tweet analysis.Project 3: Data Exploration Using Spark SQL – Wikipedia data setTopics – This project lets you work with Spark SQL. You will gain experience in working with Spark SQL for combining it with ETL applications, real time analysis of data, performing batch analysis, deploying machine learning, creating visualizations and processing of graphs.
Apache Storm Project
Project 1. Call Log Analysis using TridentTopics : In this project you will be working on call logs to decipher the data and gather valuable insights using Apache Storm Trident. You will extensively work with data about calls made from one number to another. The aim of this project is to resolve the call log issues with Trident stream processing and low latency distributed querying. You will gain hands-on experience in working with Spouts and Bolts along with various Trident functions, filters, aggregation, joins and grouping.Project 2. Twitter Data Analysis using TridentTopics : This is a project that involves working with Twitter data and processing it to extract patterns out of it. The Apache Storm Trident is the perfect framework for real-time analysis of tweets. Working with Trident you will be able to simplify the task of live Twitter feed analysis. In this project you will gain real world experience of working with Spouts, Bolts, and Trident filters, joins, aggregation, functions and grouping.Project 3. US Presidential Election Result analysis using Trident DRPC QueryTopics : This is a project that lets you work on the US presidential election results and predict who is leading and trailing on a real-time basis. For this you exclusively work with Trident distributed Remote Procedure Call server. After completion of the project you will learn how to access data residing in a remote computer or network and deploy it for real-time processing, analysis and prediction.