Big Data Architect Training Course

BIT's extensive Big Data Hadoop Architect training is curated by Hadoop experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as Spark, Scala, Splunk, Storm, Kafka and...

  • All levels
  • English

Course Description

BIT's extensive Big Data Hadoop Architect training is curated by Hadoop experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as Spark, Scala, Splunk, Storm, Kafka and Cassandra. Masters Program is a structured learning path recommended by leading industry experts and ensures that you transform into an expert Big Data Architect. Being a Big Data Architect requires y...

BIT's extensive Big Data Hadoop Architect training is curated by Hadoop experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as Spark, Scala, Splunk, Storm, Kafka and Cassandra. Masters Program is a structured learning path recommended by leading industry experts and ensures that you transform into an expert Big Data Architect. Being a Big Data Architect requires you to be a master of multitude skills, and this program aims at providing you an in-depth knowledge of the entire Big Data Ecosystem.Big Data Hadoop architects have evolved to become vital links between businesses and technology. They’re responsible for planning and designing next-generation big-data systems and managing large-scale development and deployment of Hadoop applications. Hadoop architects are among the highest-paid professionals in the IT industry.

What you’ll learn
  • Live Class Practical Oriented Training
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
  • Introduction to Hadoop ecosystem
  • Real-time analytics with Apache Spark
  • Working on large amounts of data with NoSQL databases
  • Working with HDFS and MapReduce
  • ETL in Business Intelligence domain
  • Real-time message brokering system. Hadoop analysis and testing

Covering Topics

1
Big Data Hadoop & Spark

2
Apache Spark & Scala

3
Splunk Developer

4
Splunk Admin

5
Mongo DB

6
Apache Storm

7
Apache Kafka

8
Apache Cassandra

Curriculum

      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.

Frequently Asked Questions

There are no prerequisites for taking up this training program.

The course offers a variety of online training options, including: • Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. • 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. • Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. • Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively.