Hadoop Online Training

• The architecture of Hadoop cluster
• What is High Availability and Federation?
• How to setup a production cluster?
• Various shell commands in Hadoop
• Understanding configuration files in Hadoop
• Installing a single node cluster
• Understanding Spark, Scala, Sqoop, Pig, and Flume

• Introducing Big Data and Hadoop
• What is Big Data and where does Hadoop fit in?
• Two important Hadoop ecosystem components, namely, MapReduce and HDFS
• In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and node manager

• Learning the working mechanism of MapReduce
• Understanding the mapping and reducing stages in MR
• Various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort

• Introducing Hadoop Hive
• Detailed architecture of Hive
• Comparing Hive with Pig and RDBMS
• Working with Hive Query Language
• Creation of a database, table, group by and other clauses
• Various types of Hive tables, HCatalog
• Storing the Hive Results, Hive partitioning, and Buckets

• Indexing in Hive
• The ap Side Join in Hive
• Working with complex data types
• The Hive user-defined functions
• Introduction to Impala
• Comparing Hive with Impala
• The detailed architecture of Impala

• Apache Pig introduction and its various features
• Various data types and schema in Hive
• The available functions in Pig, Hive Bags, Tuples, and Fields

• Apache Sqoop introduction
• Importing and exporting data
• Performance improvement with Sqoop
• Sqoop limitations
• Introduction to Flume and understanding the architecture of Flume
• What is HBase and the CAP theorem?

• Using Scala for writing Apache Spark applications
• Detailed study of Scala
• The need for Scala
• The concept of object-oriented programming
• Executing the Scala code
• Various classes in Scala like getters, setters, constructors, abstract, extending objects, overriding methods
• The Java and Scala interoperability
• The concept of functional programming and anonymous functions
• Bobsrockets package and comparing the mutable and immutable collections
• Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI, Spark in Hadoop ecosystem.

• 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 the directory structure
• Examples of Spark Split and Spark Scala

• Introduction to Spark
• Spark overcomes the drawbacks of working on MapReduce
• Understanding in-memory MapReduce
• Interactive operations on MapReduce
• Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
• The overview of Spark and how it is better than Hadoop
• Deploying Spark without Hadoop
• Spark history server

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

• Spark RDD
• Creating RDDs
• RDD partitioning
• Operations and transformation in RDD
• Deep dive into Spark RDDs
• The RDD general operations
• Read-only partitioned collection of records
• Using the concept of RDD for faster and efficient data processing
• RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

• Understanding the concept of key-value pair in RDDs
• Learning how Spark makes MapReduce operations faster
• Various operations of RDD
• MapReduce interactive operations
• Fine and coarse-grained update
• Spark stack

• Comparing the Spark applications with Spark Shell
• Creating a Spark application using Scala or Java
• Deploying a Spark application
• Scala built application
• Creation of the mutable list, set and set operations, list, tuple, and concatenating list
• Creating an application using SBT
• Deploying an application using Maven
• The web user interface of Spark application
• A real-world example of Spark
• Configuring of Spark

• Working towards the solution of the Hadoop project solution
• Its problem statements and the possible solution outcomes
• Points to focus on scoring the highest marks
• Tips for cracking Hadoop interview questions

• 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 and coalesce
• RDD actions

• The execution flow in Spark
• Understanding the RDD persistence overview
• Spark execution flow, and Spark terminology
• Distribution shared memory vs. RDD
• RDD limitations
• Spark shell arguments
• Distributed persistence
• RDD lineage
• Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

• Introduction to Machine Learning
• Types of Machine Learning
• Introduction to MLlib
• Various ML algorithms supported by MLlib
• Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

• Why Kafka and what is Kafka?
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
• Kafka monitoring tools
• Integrating Apache Flume and Apache Kafka

• Introduction to Spark Streaming
• Features of Spark Streaming
• Spark Streaming workflow
• Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
• Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
• Important windowed operators and stateful operators

• Introduction to various variables in Spark like shared variables and broadcast variables
• Learning about accumulators
• The common performance issues
• Troubleshooting the performance problems

• 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 Hive context
• 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 variables and accumulators
• Learning to query and transform data in data frames
• 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, and fair scheduling
• Map partition with index, the Zip, and GroupByKey
• Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions