Certified Big Data and Hadoop Training

1.What is Big Data
2.Necessity of Big Data in the industry
3.Paradigm Shift - why industry is shifting to Big Data tools
4.Different dimensions of Big Data
5.Data explosion in industry
6.Various implementations of Big Data
7.Different technologies to handle Big Data
8.Traditional systems and associated problems
9.Future of Big Data in IT industry

1.Why Hadoop is at the heart of every Big Data solution
2.Introduction to Hadoop framework
3.Hadoop architecture and design principles
4.Ingredients of Hadoop
5.Hadoop characteristics and data-flow
6.Components of Hadoop ecosystem
7.Hadoop Flavors – Apache, Cloudera, Hortonworks etc.

Setup and Installation of Single-Node Hadoop Cluster

1.Setup of Hadoop environment and pre-requisites
2.Installation and configuration of Hadoop
3.Work with Hadoop in pseudo-distributed mode
4.Troubleshooting the encountered problems

Setup and Installation of Hadoop multi-node Cluster

1.Setup Hadoop environment on the cloud (Amazon cloud)
2.Install Hadoop pre-requisites on all the nodes
3.Configuration of Masters and Slaves on Cluster
4.Play with Hadoop in distributed mode

1.What is HDFS - Hadoop Distributed File System
2.HDFS daemons and its Architecture
3.HDFS data flow and its storage mechanism
4.Hadoop HDFS Characteristics and design principles
5.Responsibility of HDFS Master – NameNode
6.Storage mechanism of Hadoop meta-data
7.Work of HDFS Slaves – DataNodes
8.Data Blocks and distributed storage
9.Replication of blocks, reliability and high availability
10.Rack-awareness, Scalability and other features
11.Different HDFS APIs and terminologies
12.Commissioning of nodes and addition of more nodes
13.Expand the cluster in real-time
14.Hadoop HDFS web UI and HDFS explorer
15.HDFS Best Practices and hardware discussion

1.What is MapReduce - Processing layer of Hadoop
2.Need of distributed processing framework
3.Issues before MapReduce and its evolution
4.List processing Concepts
5.Components of MapReduce – Mapper and Reducer
6.MapReduce terminologies key, values, lists etc.
7.Hadoop MapReduce execution flow
8.Mapping data and reducing them based on keys
9.MapReduce word-count example to understand the flow
10.Execution of Map and Reduce together
11.Control the flow of mappers and reducers
12.Optimization of MapReduce Jobs
13.Fault-tolerance and data locality
14.Work with map-only jobs
15.Introduction to Combiners in MapReduce
16.How MR jobs can be optimized using Combiners

1.Anatomy of MapReduce
2.Hadoop MapReduce data-types
3.Develop custom data-types using Writable & WritableComparable
4.What is InputFormats in MapReduce
5.How InputSplit is unit of work
6.How Partitioners partition the data
7.Customization of RecordReader
8.Move data from mapper to reducer – shuffling & sorting
9.Distributed Cache and job chaining
10.Different Hadoop case-studies to customize each component
11.Job scheduling in MapReduce

1.Need of adhoc SQL based solution – Apache Hive
2.Introduction and architecture of Hadoop Hive
3.Play with Hive shell and run HQL queries
4.Hive DDL and DML operations
5.Hive execution flow
6.Schema Design and other Hive operations
7.Schema on read vs Schema on write in Hive
8.Meta-store management and need of RDBMS
9.Limitation of default meta-store
10.Using serde to handle different types of data
11.Optimization of performance using partitioning
12.Different Hive applications and use cases

1.Need of high level query language - Apache Pig
2.How pig complements Hadoop with scripting language
3.What is Pig
4.Pig execution flow
5.Different Pig operations like filter and join
6.Compilation of pig code into MapReduce
7.Comparison between Pig vs MapReduce

1.NoSQL Databases and their need in the industry
2.Introduction to Apache HBase
3.Internals of HBase architecture
4.HBase master and slave model
5.Column-oriented, 3 dimensional, schema-less datastore
6.Data modeling in Hadoop HBase
7.Store multiple versions of data
8.Data high-availability and reliability
9.Comparison between HBase vs HDFS
10.Comparison between HBase vs RDBMS
11.Data access mechanism
12.Work with HBase using shell

1.Need of Apache Sqoop
2.Introduction and working of Sqoop
3.Import data from RDBMS to HDFS
4.Export data to RDBMS from HDFS
5.Conversion of data import / export query into MapReduce job

1.What is Apache Flume
2.Flume architecture and aggregation flow
3.Understand Flume components like data Source and Sink
4.Flume channels to buffer the events
5.Reliable & scalable data collection tool
6.Aggregate streams using Fan-in
7.Separate streams using Fan-out
8.Internals of agent architecture
9.Production architecture of Flume
10.Collect data from different sources to Hadoop HDFS
11.Multi-tier flume flow for collection of volumes of data using avro

1.Need and evolution of Yarn
2.What is Yarn and its eco-system
3.Yarn daemon architecture
4.Master of Yarn – Resource Manager
5.Slave of Yarn – Node Manager
6.Resource request from Application master
7.Dynamic slots called containers
8.Application execution flow
9.MapReduce version 2 application over Yarn
10.Hadoop Federation and Namenode HA

1. Introduction to Apache Spark
2. Comparison between Hadoop MapReduce vs Apache Spark
2. Spark key Features
3. RDD and various RDD operations
4. RDD Abstraction, interface and creation of RDDs
5. Fault Tolerance in Spark
6. Spark Programming model
7. Data Flow in Spark
8. Spark Ecosystem, its Hadoop compatibility & integration
9. Installation & Configuration of Spark
10. Process Big Data using Spark

Live Hadoop project based on industry use-case using Hadoop components like Pig, HBase, MapReduce and Hive to solve real world problems in Big Data Analytics