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Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data, however the main focus is on unstructured data. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many exabytes of data.
Pre-requisites for the Big Data Hadoop Training Course?
There will be no pre-requisites but Knowledge of Java/ Python, SQL, Linux will be beneficial, but not mandatory. Ducat provides a crash course for pre-requisites required to initiate Big Data training.
Apache Hadoop on AWS Cloud
This module will help you understand how to configure Hadoop Cluster on AWS Cloud:
- Introduction to Amazon Elastic MapReduce
- AWS EMR Cluster
- AWS EC2 Instance: Multi Node Cluster Configuration
- AWS EMR Architecture
- Web Interfaces on Amazon EMR
- Amazon S3
- Executing MapReduce Job on EC2 & EMR
- Apache Spark on AWS, EC2 & EMR
- Submitting Spark Job on AWS
- Hive on EMR
- Available Storage types: S3, RDS & DynamoDB
- Apache Pig on AWS EMR
- Processing NY Taxi Data using SPARK on Amazon EMR[Type text]
Learning Big Data and Hadoop
This module will help you understand Big Data:
- Common Hadoop ecosystem components
- Hadoop Architecture
- HDFS Architecture
- Anatomy of File Write and Read
- How MapReduce Framework works
- Hadoop high level Architecture
- MR2 Architecture
- Hadoop YARN
- Hadoop 2.x core components
- Hadoop Distributions
- Hadoop Cluster Formation
Hadoop Architecture and HDFS
This module will help you to understand Hadoop & HDFS ClusterArchitecture:
- Configuration files in Hadoop Cluster (FSimage & editlog file)
- Setting up of Single & Multi node Hadoop Cluster
- HDFS File permissions
- HDFS Installation & Shell Commands
- Deamons of HDFS
- Node Manager
- Resource Manager
- Secondary NameNode
- YARN Deamons
- HDFS Read & Write Commands
- NameNode & DataNode Architecture
- HDFS Operations
- Hadoop MapReduce Job
- Executing MapReduce Job
Hadoop MapReduce Framework
This module will help you to understand Hadoop MapReduce framework:
- How MapReduce works on HDFS data sets
- MapReduce Algorithm
- MapReduce Hadoop Implementation
- Hadoop 2.x MapReduce Architecture
- MapReduce Components
- YARN Workflow
- MapReduce Combiners
- MapReduce Partitioners
- MapReduce Hadoop Administration
- MapReduce APIs
- Input Split & String Tokenizer in MapReduce
- MapReduce Use Cases on Data sets
Advanced MapReduce Concepts
This module will help you to learn:
- Job Submission & Monitoring
- Distributed Cache
- Map & Reduce Join
- Data Compressors
- Job Configuration
- Record Reader
This module will help you to understand Pig Concepts:
- Pig Architecture
- Pig Installation
- Pig Grunt shell
- Pig Running Modes
- Pig Latin Basics
- Pig LOAD & STORE Operators[Type text]
- Diagnostic Operators
- DESCRIBE Operator
- EXPLAIN Operator
- ILLUSTRATE Operator
- DUMP Operator
- Grouping & Joining
- GROUP Operator
- COGROUP Operator
- JOIN Operator
- CROSS Operator
- Combining & Splitting
- UNION Operator
- SPLIT Operator
- FILTER Operator
- DISTINCT Operator
- FOREACH Operator
- LIMIT Operator
- Built in Fuctions
- EVAL Functions
- LOAD & STORE Functions
- Bag & Tuple Functions
- String Functions
- Date-Time Functions
- MATH Functions
- Pig UDFs (User Defined Functions)
- Pig Scripts in Local Mode
- Pig Scripts in MapReduce Mode
- Analysing XML Data using Pig
- Pig Use Cases (Data Analysis on Social Media sites, Banking, Stock Market & Others)
- Analysing JSON data using Pig
- Testing Pig Sctipts
This module will build your concepts in learning:
- Hive Installation
- Hive Data types
- Hive Architecture & Components
- Hive Meta Store
- Hive Tables(Managed Tables and External Tables)
- Hive Partitioning & Bucketing
- Hive Joins & Sub Query
- Running Hive Scripts
- Hive Indexing & View
- Hive Queries (HQL); Order By, Group By, Distribute By, Cluster By, Examples
- Hive Functions: Built-in & UDF (User Defined Functions)
- Hive ETL: Loading JSON, XML, Text Data Examples
- Hive Querying Data
- Hive Tables (Managed & External Tables)
- Hive Used Cases
- Hive Optimization Techniques
- Partioning(Static & Dynamic Partition) & Bucketing
- Hive Joins > Map + BucketMap + SMB (SortedBucketMap) + Skew
- Hive FileFormats ( ORC+SEQUENCE+TEXT+AVRO+PARQUET)
- Indexing (Compact + BitMap)
- Integration with TEZ & Spark
- Hive SerDer ( Custom + InBuilt)
- Hive integration NoSQL (HBase + MongoDB + Cassandra)
- Thrift API (Thrift Server)
- UDF, UDTF & UDAF
- Hive Multiple Delimiters
- XML & JSON Data Loading HIVE.
- Aggregation & Windowing Functions in Hive
- Hive Connect with Tableau
- Sqoop Installation
- Loading Data form RDBMS using Sqoop
- Sqoop Import & Import-All-Table
- Fundamentals & Architecture of Apache Sqoop
- Sqoop Job
- Sqoop Codegen
- Sqoop Incremental Import & Incremental Export
- Sqoop Merge
- Import Data from MySQL to Hive using Sqoop
- Sqoop: Hive Import
- Sqoop Metastore
- Sqoop Use Cases
- Sqoop- HCatalog Integration
- Sqoop Script
- Sqoop Connectors
This module will help you to learn Flume Concepts:
- Flume Introduction
- Flume Architecture
- Flume Data Flow
- Flume Configuration
- Flume Agent Component Types
- Flume Setup
- Flume Interceptors
- Multiplexing (Fan-Out), Fan-In-Flow
- Flume Channel Selectors
- Flume Sync Processors
- Fetching of Streaming Data using Flume (Social Media Sites: YouTube, LinkedIn, Twitter)
- Flume + Kafka Integration
- Flume Use Cases
This module will help you to learn Kafka concepts:
- Kafka Fundamentals
- Kafka Cluster Architecture
- Kafka Workflow
- Kafka Producer, Consumer Architecture
- Integration with SPARK
- Kafka Topic Architecture
- Zookeeper & Kafka
- Kafka Partitions
- Kafka Consumer Groups
- KSQL (SQL Engine for Kafka)
- Kafka Connectors
- Kafka REST Proxy
- Kafka Offsets
This module will help you to understand Oozie concepts:
- Oozie Introduction
- Oozie Workflow Specification
- Oozie Coordinator Functional Specification
- Oozie H-catalog Integration
- Oozie Bundle Jobs
- Oozie CLI Extensions
- Automate MapReduce, Pig, Hive, Sqoop Jobs using Oozie
- Packaging & Deploying an Oozie Workflow Application
This module will help you to learn HBase Architecture:
- HBase Architecture, Data Flow & Use Cases
- Apache HBase Configuration
- HBase Shell & general commands
- HBase Schema Design
- HBase Data Model
- HBase Region & Master Server
- HBase & MapReduce
- Bulk Loading in HBase
- Create, Insert, Read Tables in HBase
- HBase Admin APIs
- HBase Security
- HBase vs Hive
- Backup & Restore in HBase
- Apache HBase External APIs (REST, Thrift, Scala)
- HBase & SPARK
- Apache HBase Coprocessors
- HBase Case Studies
- HBase Trobleshooting
Data Processing with Apache Spark
Spark executes in-memory data processing & how Spark Job runs faster then Hadoop MapReduce Job. Course will also help you understand the Spark Ecosystem & it related APIs like Spark SQL, Spark Streaming, Spark MLib, Spark GraphX & Spark Core concepts as well. This course will help you to understand Data Analytics & Machine Learning algorithms applying to various datasets to process & to analyze large amount of data.
- Spark RDDs.
- Spark RDDs Actions & Transformations.
- Spark SQL : Connectivity with various Relational sources & its convert it into Data Frame using Spark SQL.
- Spark Streaming
- Understanding role of RDD
- Spark Core concepts : Creating of RDDs: Parrallel RDDs, MappedRDD, HadoopRDD, JdbcRDD.
- Spark Architecture & Components.
BIG DATA PROJECTS
Project #1: Working with MapReduce, Pig, Hive & Flume
Problem Statement : Fetch structured & unstructured data sets from various sources like Social Media Sites, Web Server & structured source like MySQL, Oracle & others and dump it into HDFS and then analyze the same datasets using PIG,HQL queries & MapReduce technologies to gain proficiency in Hadoop related stack & its ecosystem tools.
Data Analysis Steps in :
- Dump XML & JSON datasets into HDFS.
- Convert semi-structured data formats(JSON & XML) into structured format using Pig,Hive & MapReduce.
- Push the data set into PIG & Hive environment for further analysis.
- Writing Hive queries to push the output into relational database(RDBMS) using Sqoop.
- Renders the result in Box Plot, Bar Graph & others using R & Python integration with Hadoop.
Project #2: Analyze Stock Market Data
Data : Data set contains stock information such as daily quotes ,Stock highest price, Stock opening price on New York Stock Exchange. Problem Statement: Calculate Co-variance for stock data to solve storage & processing problems related to huge volume of data.
- Positive Covariance, If investment instruments or stocks tend to be up or down during the same time periods, they have positive covariance.
- Negative Co-variance, If return move inversely,If investment tends to be up while other is down, this shows Negative Co-variance.
Project #3: Hive,Pig & MapReduce with New York City Uber Trips
- Problem Statement: What was the busiest dispatch base by trips for a particular day on entire month?
- What day had the most active vehicles.
- What day had the most trips sorted by most to fewest.
- Dispatching_Base_Number is the NYC taxi & Limousine company code of that base that dispatched the UBER.
- active_vehicles shows the number of active UBER vehicles for a particular date & company(base). Trips is the number of trips for a particular base & date.
Project #4: Analyze Tourism Data
Data: Tourism Data comprises contains : City Pair, seniors travelling,children traveling, adult traveling, car booking price & air booking price. Problem Statement: Analyze Tourism data to find out :
- Top 20 destinations tourist frequently travel to: Based on given data we can find the most popular destinations where people travel frequently, based on the specific initial number of trips booked for a particular destination
- Top 20 high air-revenue destinations, i.e the 20 cities that generate high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations.
- Top 20 locations from where most of the trips start based on booked trip count.
Project #5: Airport Flight Data Analysis : We will analyze Airport Information System data that gives information regarding flight delays,source & destination details diverted routes & others.
- Industry: Aviation Problem Statement: Analyze Flight Data to:
- List of Delayed flights.
- Find flights with zero stop.
- List of Active Airlines all countries.
- Source & Destination details of flights.
- Reason why flight get delayed.
- Time in different formats.
Project #6: Analyze Movie Ratings
Data: Movie data from sites like rotten tomatoes, IMDB, etc. Problem Statement: Analyze the movie ratings by different users to:
- Get the user who has rated the most number of movies
- Get the user who has rated the least number of movies
- Get the count of total number of movies rated by user belonging to a specific occupation
- Get the number of underage users
Project #7: Analyze Social Media Channels :
- Industry: Social Media
- Data: DataSet Columns : VideoId, Uploader, Internal Day of establishment of You tube & the date of uploading of the video,Category,Length,Rating, Number of comments.
- Problem Statement: Top 5 categories with maximum number of videos uploaded.
- Problem Statement: Identify the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, and the top 10 most viewed videos.
- Apart from these there are some twenty more use-cases to choose: Twitter Data Analysis
- Market data Analysis