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.