Big Data Architect Training Course

  Lecture- 1 Hadoop Installation and Setup 
·       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 with Cloudera Manager

·       Understanding Spark, Scala, Sqoop, Pig, and Flume

Lecture-2 Introduction to Big Data Hadoop and Understanding HDFS and MapReduce 
·       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

Lecture-3 Deep Dive in MapReduce 
·       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

Lecture-4 Introduction to Hive 
·       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

Lecture-5 Advanced Hive and Impala 
·       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

Lecture-6 Introduction to Pig 
·       Apache Pig introduction and its various features

·       Various data types and schema in Hive

·       The available functions in Pig, Hive Bags, Tuples, and Fields

Lecture-7 Flume, Sqoop and HBase 
·       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?

·       Deploying Disable, Scan, and Enable Table

Lecture-8 Writing Spark Applications Using Scala 
·       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

Lecture-9 Spark framework 
·       Detailed Apache Spark and its various features

·       Comparing with Hadoop

·       Various Spark components

·       Combining HDFS with Spark and Scalding

·       Introduction to Scala

·       Importance of Scala and RDD

Lecture-10 RDD in Spark 
·       Understanding the Spark RDD operations

·       Comparison of Spark with MapReduce

·       What is a Spark transformation?

·       Loading data in Spark

·       Types of RDD operations viz transformation and action

·       What is a Key/Value pair?

Lecture-11 Data Frames and Spark SQL 
·       The detailed Spark SQL

·       The significance of SQL in Spark for working with structured data processing

·       Spark SQL JSON support

·       Working with XML data and parquet files

·       Creating Hive Context

·       Writing Data Frame to Hive

·       How to read a JDBC file?

·       Significance of a Spark data frame

·       How to create a data frame?

·       What is schema manual inferring?

·       Work with CSV files, JDBC table reading, data conversion from Data Frame to JDBC, Spark SQL user-defined functions, shared variable, and accumulators

·       How to query and transform data in Data Frames?

·       How data frame provides the benefits of both Spark RDD and Spark SQL?

·       Deploying Hive on Spark as the execution engine

Lecture-12 Machine Learning Using Spark (MLlib) 
·       Introduction to Spark MLlib

·       Understanding various algorithms

·       What is Spark iterative algorithm?

·       Spark graph processing analysis

·       Introducing Machine Learning

·       K-Means clustering

·       Spark variables like shared and broadcast variables

·       What are accumulators?

·       Various ML algorithms supported by MLlib

·       Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Lecture-13 Integrating Apache Flume and Apache Kafka 
·       Why Kafka?

·       What is Kafka?

·       Kafka architecture

·       Kafka workflow

·       Configuring Kafka cluster

·       Basic operations

·       Kafka monitoring tools

·       Integrating Apache Flume and Apache Kafka

Lecture-14 Spark Streaming 
·       Introduction to Spark streaming

·       The architecture of Spark streaming

·       Working with the Spark streaming program

·       Processing data using Spark streaming

·       Requesting count and DStream

·       Multi-batch and sliding window operations

·       Working with advanced data sources

·       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 its uses

·       Important Windowed operators and Stateful operators

Lecture- 15 - Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2 
·       Create a 4-node Hadoop cluster setup

·       Running the MapReduce Jobs on the Hadoop cluster

·       Successfully running the MapReduce code

·       Working with the Cloudera Manager setup

Lecture-16 Hadoop Administration – Cluster Configuration 
·       Overview of Hadoop configuration

·       The importance of Hadoop configuration file

·       The various parameters and values of configuration

·       The HDFS parameters and MapReduce parameters

·       Setting up the Hadoop environment

·       The Include and Exclude configuration files

·       The administration and maintenance of name node, data node directory structures, and files

·       What is a File system image?

·       Understanding Edit log

Lecture-17 Hadoop Administration – Maintenance, Monitoring and Troubleshooting 
·       Introduction to the checkpoint procedure, name node failure

·       How to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes

Lecture-18 ETL Connectivity with Hadoop Ecosystem (Self-Paced) 
·       How ETL tools work in Big Data industry?

·       Introduction to ETL and data warehousing

·       Working with prominent use cases of Big Data in ETL industry

·       End-to-end ETL PoC showing Big Data integration with ETL tool

  Scala
Lecture-1 Introduction to Scala 
·       Introducing Scala

·       Deployment of Scala for Big Data applications and Apache Spark analytics

·       Scala REPL, lazy values, and control structures in Scala

·       Directed Acyclic Graph (DAG)

·       First Spark application using SBT/Eclipse

·       Spark Web UI

·       Spark in the Hadoop ecosystem.

Lecture-2 Pattern Matching 
·       The importance of Scala

·       The concept of REPL (Read Evaluate Print Loop)

·       Deep dive into Scala pattern matching

·       Type interface, higher-order function, currying, traits, application space and Scala for data analysis

Lecture-3 Executing the Scala Code 
·       Learning about the Scala Interpreter

·       Static object timer in Scala and testing string equality in Scala

·       Implicit classes in Scala

·       The concept of currying in Scala

·       Various classes in Scala

Lecture-4 Classes Concept in Scala 
·       Learning about the Classes concept

·       Understanding the constructor overloading

·       Various abstract classes

·       The hierarchy types in Scala

·       The concept of object equality

·       The val and var methods in Scala

Lecture-5 Case Classes and Pattern Matching 
·       Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern

Lecture-6 Concepts of Traits with Example 
·       Understanding traits in Scala

·       The advantages of traits

·       Linearization of traits

·       The Java equivalent

·       Avoiding of boilerplate code

Lecture-7 Scala–Java Interoperability 
·       Implementation of traits in Scala and Java

·       Handling of multiple traits extending

Lecture-8 Scala Collections 
·       Introduction to Scala collections

·       Classification of collections

·       The difference between iterator and iterable in Scala

·       Example of list sequence in Scala

Lecture-9 Mutable Collections Vs. Immutable Collections 
·       The two types of collections in Scala

·       Mutable and immutable collections

·       Understanding lists and arrays in Scala

·       The list buffer and array buffer

·       Queue in Scala

·       Double-ended queue Deque, Stacks, Sets, Maps, and Tuples in Scala

Lecture-10 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  suite for Scala test

·       Packaging of Scala applications in the directory structure

·       Examples of Spark Split and Spark Scala

Spark
Lecture-11 Introduction to Spark 
·       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 and Cloudera distribution

Lecture-12 Spark Basics 
·       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

Lecture-13 Working with RDDs in 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

Lecture-14 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 and coarse-grained update

·       Spark stack

Lecture-15 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 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

Lecture-16 Parallel Processing 
·       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

Lecture-17 Spark RDD Persistence 
·       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

Lecture-18 Spark MLlib 
·       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

Lecture-19 Integrating Apache Flume and Apache Kafka 
·       Why Kafka and what is Kafka?

·       Kafka architecture

·       Kafka workflow

·       Configuring Kafka cluster

·       Operations

·       Kafka monitoring tools

·       Integrating Apache Flume and Apache Kafka

Lecture-20 Spark Streaming 
·       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

Lecture-21 Improving Spark Performance 
·       Introduction to various variables in Spark like shared variables and broadcast variables

·       Learning about accumulators

·       The common performance issues

·       Troubleshooting the performance problems

Lecture-22 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 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

Lecture-23 Scheduling/Partitioning 
·       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

  Lecture 1 - Splunk Development Concepts 
·       Introduction to Splunk and Splunk developer roles and responsibilities

Lecture 2 - Basic Searching 
·       Writing Splunk query for search

·       Auto-complete to build a search

·       Time range

·       Refine search

·       Working with events

·       Identifying the contents of search

Lecture 3 - Using Fields in Searches 
·       What is a Field

·       How to use Fields in search

·       Deploying Fields Sidebar and Field Extractor for REGEX field extraction

·       Delimiting Field Extraction using FX

Lecture 4 - Saving and Scheduling Searches 
·       Writing Splunk query for search, sharing, saving, scheduling and exporting search results

Lecture 5: Creating Alerts 
·       How to create alerts

·       Understanding alerts

·       Viewing fired alerts

Lecture 6 - Scheduled Reports 
·       Describe and configure scheduled reports

Lecture 7 - Tags and Event Types 
·       Introduction to Tags in Splunk

·       Deploying Tags for Splunk search

·       Understanding event types and utility

·       Generating and implementing event types in search

Lecture 8 - Creating and Using Macros 
·       What is a Macro

·       What are variables and arguments in Macros

Lecture 9 - Workflow 
·       Creating get, post and search workflow actions

Lecture 10 - Splunk Search Commands 
·       Studying the search command

·       The general search practices

·       What is a search pipeline

·       How to specify indexes in search

·       Highlighting the syntax

·       Deploying the various search commands like fields, tables, sort, rename, rex and erex

Lecture 11 - Transforming Commands 
·       Using top, rare and stats commands

Lecture 12 - Reporting Commands 
·       Using following commands and their functions: addcoltotals, addtotals, top, rare and stats

Lecture 13 - Mapping and Single Value Commands 
·       iplocation, geostats, geom and addtotals commands

Lecture 14 - Splunk Reports and Visualizations 
·       Explore the available visualizations

·       Create charts and time charts

·       Omit null values and format results

Lecture 15 - Analyzing, Calculating and Formatting Results 
·       Calculating and analyzing results

·       Value conversion

·       Roundoff and format values

·       Using the eval command

·       Conditional statements

·       Filtering calculated search results

Lecture 16 - Correlating Events 
·       How to search the transactions

·       Creating report on transactions

·       Grouping events using time and fields

·       Comparing transactions with stats

Lecture 17 - Enriching Data with Lookups 
·       Learning data lookups

·       Examples and lookup tables

·       Defining and configuring automatic lookups

·       Deploying lookups in reports and searches

Lecture 18 - Creating Reports and Dashboards 
·       Creating search charts, reports and dashboards

·       Editing reports and dashboards

·       Adding reports to dashboards

Lecture 19 - Getting Started with Parsing 
·       Working with raw data for data extraction, transformation, parsing and preview

Lecture 20 - Using Pivot 
·       Describe pivot

·       Relationship between data model and pivot

·       Select a data model object

·       Create a pivot report

·       Create instant pivot from a search

·       Add a pivot report to dashboard

Lecture 21 - Common Information Model (CIM) Add-On 
·       What is a Splunk CIM

·       Using the CIM Add-On to normalize data

  Lecture 1 - Overview of Splunk 
·       Introduction to the architecture of Splunk

·       Various server settings

·       How to set up alerts

·       Various types of licenses

·       Important features of Splunk tool

·       The requirements of hardware and conditions needed for installation of Splunk

Lecture 2 - Splunk Installation 
·       How to install and configure Splunk

·       The creation of index

·       Standalone server’s input configuration

·       The preferences for search

·       Linux environment Splunk installation

·       The administering and architecting of Splunk

Lecture 3- Splunk Installation in Linux 
·       How to install Splunk in the Linux environment

·       The conditions needed for Splunk

·       Configuring Splunk in the Linux environment

Lecture 4- Distributed Management Console 
·       Introducing Splunk distributed management console

·       Indexing of clusters

·       How to deploy distributed search in Splunk environment

·       Forwarder management

·       User authentication and access control

Lecture 5- Introduction to Splunk App 
·       Introduction to the Splunk app

·       How to develop Splunk apps

·       Splunk app management

·       Splunk app add-ons

·       Using Splunk-base for installation and deletion of apps

·       Different app permissions and implementation

·       How to use the Splunk app

·       Apps on forwarder

Lecture 6- Splunk Indexes and Users 
·       Details of the index time configuration file

·       The search time configuration file

Lecture 7- Splunk Configuration Files 
·       Understanding of Index time and search time configuration filesin Splunk

·       Forwarder installation

·       Input and output configuration

·       Universal Forwarder management

·       Splunk Universal Forwarder highlights

Lecture 8- Splunk Deployment Management 
·       Implementing the Splunk tool

·       Deploying it on the server

·       Splunk environment setup

·       Splunk client group deployment

Lecture 9- Splunk Indexes 
·       Understanding the Splunk Indexes

·       The default Splunk Indexes

·       Segregating the Splunk Indexes

·       Learning Splunk Buckets and Bucket Classification

·       Estimating Index storage

·       Creating new Index

Lecture 10- User Roles and Authentication 
·       Understanding the concept of role inheritance

·       Splunk authentications

·       Native authentications

·       LDAP authentications

Lecture 11- Splunk Administration Environment 
·       Splunk installation, configuration

·       Data inputs

·       App management

·       Splunk important concepts

·       Parsing machine-generated data

·       Search indexer and forwarder

Lecture 12- Basic Production Environment 
·       Introduction to Splunk Configuration Files

·       Universal Forwarder

·       Forwarder Management

·       Data management, troubleshooting and monitoring

Lecture 13- Splunk Search Engine 
·       Converting machine-generated data into operational intelligence

·       Setting up the dashboard, reports and charts

·       Integrating Search Head Clustering and Indexer Clustering

Lecture 14- Various Splunk Input Methods 
·       Understanding the input methods

·       Deploying scripted, Windows and network

·       Agentless input types and fine-tuning them all

Lecture 15- Splunk User and Index Management 
·       Splunk user authentication and job role assignment

·       Learning to manage, monitor and optimize Splunk Indexes

Lecture 16- Machine Data Parsing 
·       Understanding parsing of machine-generated data

·       Manipulation of raw data

·       Previewing and parsing

·       Data field extraction

·       Comparing single-line and multi-line events

Lecture 17- Search Scaling and Monitoring 
·       Distributed search concepts

·       Improving search performance

·       Large-scale deployment and overcoming execution hurdles

·       Working with Splunk Distributed Management Console for monitoring the entire operation

Lecture 18- Splunk Cluster Implementation 
·       Cluster indexing

·       Configuring individual nodes

·       Configuring the cluster behavior, index and search behavior

·       Setting node type to handle different aspects of cluster like master node, peer node and search head

  Lecture-1 Introduction to NoSQL and MongoDB 
·       RDBMS, types of relational databases,

·       challenges of RDBMS,

·       NoSQL database,

·       its significance,

·       how NoSQL suits Big Data needs,

·       introduction to MongoDB and its advantages,

·       MongoDB installation,

·       JSON features,

·       data types and examples

Lecture-2 MongoDB Installation 
·       Installing MongoDB,

·       basic MongoDB commands and operations,

·       MongoChef (MongoGUI) installation and MongoDB data types

Lecture-3 Importance of NoSQL 
·       The need for NoSQL,

·       types of NoSQL databases,

·       OLTP,

·       OLAP,

·       limitations of RDBMS,

·       ACID properties,

·       CAP Theorem,

·       Base property,

·       learning about JSON/BSON,

·       database collection and documentation,

·       MongoDB uses,

·       MongoDB write concern—acknowledged,

·       replica acknowledged,

·       unacknowledged,

·       journaled—and Fsync

Lecture-4 CRUD Operations 
·       Understanding CRUD and its functionality,

·       CRUD concepts,

·       MongoDB query and syntax and read and write queries and query optimization

Lecture-5 Data Modeling and Schema Design 
·       Concepts of data modelling,

·       difference between MongoDB and RDBMS modelling,

·       model tree structure,

·       operational strategies,

·       monitoring and backup

Lecture-6 Data Management and Administration 
·       In this module, you will learn MongoDB Administration activities such as health check, backup, recovery, database sharding and profiling, data import/export, performance tuning, etc.

Lecture-7 Data Indexing and Aggregation 
·       Concepts of data aggregation and types and data indexing concepts,

·       properties and variations

Lecture-8 MongoDB Security 
·       Understanding database security risks,

·       MongoDB security concept and security approach and MongoDB integration with Java and Robomongo

Lecture-9 Working with Unstructured Data 
·       Implementing techniques to work with variety of unstructured data like images, videos, log data and others and understanding GridFS MongoDB file system for storing data

  Lecture-1 Understanding the Architecture of Storm 
·       Big Data characteristics,

·       understanding Hadoop distributed computing,

·       the Bayesian Law,

·       deploying Storm for real-time analytics,

·       Apache Storm features, comparing Storm with Hadoop,

·       Storm execution and learning about Tuple, Spout and Bolt.

Lecture-2 Installation of Apache Storm 
·       Installing Apache Storm and various types of run modes of Storm.

Lecture-3 Introduction to Apache Storm 
·       Understanding Apache Storm and the data model.

Lecture-4 Apache Kafka Installation 
·       Installation of Apache Kafka and its configuration.

Lecture-5 Apache Storm Advanced 
·       Understanding advanced Storm topics like Spouts, Bolts, Stream Groupings and Topology and its life cycle and learning about guaranteed message processing

Lecture-6 Storm Topology 
·       Various grouping types in Storm, reliable and unreliable messages,

·       Bolt structure and life cycle,

·       understanding Trident topology for failure handling,

·       process and call log analysis topology for analyzing call logs for calls made from one number to another.

Lecture-7 Overview of Trident 
·       Various grouping types in Storm, reliable and unreliable messages,

·       Bolt structure and life cycle,

·       understanding Trident topology for failure handling,

·       process and call log analysis topology for analyzing call logs for calls made from one number to another.

Lecture-8 Storm Components and Classes 
·       Various components, classes and interfaces in Storm like Base Rich Bolt Class, i RichBolt Interface, i RichSpout Interface and Base Rich Spout Class and various methodologies of working with them.

Lecture-9 Cassandra Introduction 
·       Understanding Cassandra, its core concepts, its strengths and deployment.

Lecture-10 Boot Stripping 
·       Twitter Boot Stripping,

·       detailed understanding of Boot Stripping,

·       concepts of Storm,

·       Storm development environment.

  Lecture-1 What is Kafka – An Introduction 
·       Understanding what is Apache Kafka,

·       the various components and use cases of Kafka,

·       implementing Kafka on a single node.

Lecture-2 Multi Broker Kafka Implementation 
·       Learning about the Kafka terminology,

·       deploying single node Kafka with independent Zookeeper,

·       adding replication in Kafka,

·       working with Partitioning and Brokers,

·       understanding Kafka consumers,

·       the Kafka Writes terminology,

·       various failure handling scenarios in Kafka.

Lecture-3 Multi Node Cluster Setup 
·       Introduction to multi node cluster setup in Kafka,

·       the various administration commands,

·       leadership balancing and partition rebalancing,

·       graceful shutdown of kafka Brokers and tasks,

·       working with the Partition Reassignment Tool,

·       cluster expending,

·       assigning Custom Partition,

·       removing of a Broker and improving Replication Factor of Partitions.

Lecture-4 Integrate Flume with Kafka 
·       Understanding the need for Kafka Integration,

·       successfully integrating it with Apache Flume,

·       steps in integration of Flume with Kafka as a Source.

Lecture-5 Kafka API 
·       Detailed understanding of the Kafka and Flume Integration,

·       deploying Kafka as a Sink and as a Channel,

·       introduction to PyKafka API and setting up the PyKafka Environment.

Lecture-6 Producers & Consumers 
·       Connecting Kafka using PyKafka,

·       writing your own Kafka Producers and Consumers,

·       writing a random JSON Producer,

·       writing a Consumer to read the messages from a topic,

·       writing and working with a File Reader Producer,

·        writing a Consumer to store topics data into a file.

  Lecture-1 Advantages and Usage of Cassandra 
·       Introduction to Cassandra, its strengths and deployment areas

Lecture-2 CAP Theorem and No SQL DataBase 
·       Significance of NoSQL,

·       RDBMS Replication,

·       Key Challenges,

·       types of NoSQL,

·       benefits and drawbacks,

·       salient features of NoSQL database

·       CAP Theorem,

·       Consistency.

Lecture-3 Cassandra fundamentals, Data model, Installation and setup 
·       Installation,

·       introduction to Cassandra,

·       key concepts and deployment of non relational database,

·       column-oriented database,

·       Data Model – column,

·       column family,

Lecture-4 Cassandra Configuration 
·       Token calculation,

·       Configuration overview,

·       Node tool,

·       Validators,

·       Comparators,

·       Expiring column,

·       QA

Lecture-5 Summarization, node tool commands, cluster, Indexes, Cassandra & MapReduce, Installing Ops-center 
·       How Cassandra modelling varies from Relational database modelling,

·       Cassandra modelling steps,

·       introduction to Time Series modelling,

·       comparing Column family Vs. Super Column family,

·       Counter column family,

·       Partitioners,

·       Partitioners strategies,

·       Replication,

·       Gossip protocols,

·       Read operation,

·       Consistency,

·       Comparison

Lecture-6 Multi Cluster setup 
·       Creation of multi node cluster,

·       node settings,

·       Key and Row cache,

·       System Key space,

·       understanding of Read Operation,

·       Cassandra Commands overview,

·       VNodes,

·       Column family

Lecture-7 Thrift/Avro/Json/Hector Client 
·       JSON,

·       Hector client,

·       AVRO,

·       Thrift,

·       JAVA code writing method,

·       Hector tag

Lecture-8 Datastax installation part, Secondary index 
·       Cassandra management,

·       commands of node tool,

·       MapReduce and Cassandra,

·       Secondary index,

·       Datastax Installation

Lecture-9 Advance Modelling 
·       Rules of Cassandra data modelling,

·       increasing data writes,

·       duplication, and reducing data reads,

·       modelling data around queries,

·       creating table for data queries

Lecture-10 Deploying the IDE for Cassandra applications 
·       Understanding the Java application creation methodology,

·       learning key drivers,

·       deploying the IDE for Cassandra applications,

·       cluster connection and data query implementation

Lecture-11 Cassandra Administration 
·       Learning about Node Tool Utility,

·       cluster management using Command Line Interface,

·       Cassandra management and monitoring via DataStax Ops Center.

Lecture-12 Cassandra API and Summarization and Thrift 
·       Cassandra client connectivity,

·       connection pool internals,

·       API,

·       important features and concepts of Hector client,

·       Thrift,

·       JAVA code,

·        Summarization.