- Up to the year 2003, all over the production of data was 6 billion gigabytes.
- In 2011, the same amount of data was generated in every two days
- Very surprisingly, in 2013, the same amount of data generated in every 2 minutes.
- Just think about now and upcoming years!!
Learning Outcomes of our Hadoop Course:
On successfully completing our 60 hours of Hadoop Training Program, You will be an expert with the below skills to match the industrial expectations,
- Firstly, Strong knowledge in Hadoop Fundamental concepts.
- Secondly, Deep Understanding of Hadoop Distributed File System(HDFS) and MapReduce concepts.
- Morever, Installation and deployment of Apache Hadoop.
- Most Importantly, Become an Expert in Map Reduce Programs and Implementation of HBase.
- On the other words, Hands-on knowledge in Data loading techniques using sqoop and flume.
- Likewise, Gain an depth knowledge of Big Data Framework using Hadoop and Apache Spark.
- Further, Best practices in buliding, optimizing and debugging the Hadoop solutions.
- In the conclusion, Over all understanding of Big Data Hadoop and be equipped to clear Big Data Hadoop Certification.
Hadoop Training Highlights
- Learn Hadoop training from our Expert by working on hands-on real time projects.
- Most importantly our Hadoop training in Chennai will start from complete scratch which includes spark with scala.
- Further, Hands-on Practical assignments for each and every topic which makes you strong in technically.
- On Successfully completion of this Hadoop training via online, classroom and corporate an individual. Also it will acquire the complete skillset required to be a professional Hadoop Developer.
- In addition,Guidance for Hadoop Developer Certification.
- Similarly, Special Combo Course (Hadoop and Spark) is available with Combo offer for interested candidates.
- In addition, Latest Hadoop Job Openings will be shared with our trained Candidates.
Course Features
- Duration60 hours
- Skill levelAll level
- Batch Strength15
- AssessmentsYes
- Mock InterviewsYes
- Resume BuildingYes
- PlacementsYes
- Flexible TimingYes
- Fee InstallmentsYes
- LanguageTamil/English
- Overview of Hadoop Ecosystem
- Role of Hadoop in Big data– Overview of other Big Data Systems
- Who is using Hadoop
- Hadoop integrations into Exiting Software Products
- Current Scenario in Hadoop Ecosystem
- Installation
- Configuration
- Use Cases ofHadoop (HealthCare, Retail, Telecom)
- Concepts
- Architecture
- Data Flow (File Read , File Write)
- Fault Tolerance
- Shell Commands
- Data Flow Archives
- Coherency -Data Integrity
- Role of Secondary NameNode
- Theory
- Data Flow (Map – Shuffle - Reduce)
- MapRed vs MapReduce APIs
- Programming [Mapper, Reducer, Combiner, Partitioner]
- Writables
- InputFormat
- Outputformat
- Streaming API using python
- Inherent Failure Handling using Speculative Execution
- Magic of Shuffle Phase
- FileFormats
- Sequence Files
- Introduction to NoSQL
- CAP Theorem
- Classification of NoSQL
- Hbase and RDBMS
- HBASE and HDFS
- Architecture (Read Path, Write Path, Compactions, Splits)
- Installation
- Configuration
- Role of Zookeeper
- HBase Shell Introduction to Filters
- RowKeyDesign -What's New in HBase Hands On
- Architecture
- Installation
- Configuration
- Hive vs RDBMS
- Tables
- DDL
- DML
- UDF
- Partitioning
- Bucketing
- Hive functions
- Date functions
- String functions
- Cast function Meta Store
- Joins
- Real-time HQL will be shared along with database migration project
- Architecture
- Installation
- Hive vs Pig
- Pig Latin Syntax
- Data Types
- Functions (Eval, Load/Store, String, DateTime)
- Joins
- UDFs- Performance
- Troubleshooting
- Commonly Used Functions
- Architecture , Installation, Commands(Import , Hive-Import, EVal, Hbase Import, Import All tables, Export)
- Connectors to Existing DBs and DW
- SQOOP to import Real Time Weblogs from application to DB and try to export the same to MySQL
- Kafka introduction
- Data streaming Introduction
- Producer-consumer-topics
- Brokers
- Partitions
- Unix Streaming via kafka
-
Kafka
- Producer and Subscribers setup and publish a topic from Producer to subscriber
- Architecture
- Installation
- Workflow
- Coordinator
- Action (Mapreduce, Hive, Pig, Sqoop)
- Introduction to Bundle
- Mail Notifications
- Limitations in Hadoop
- 1.0 - HDFS Federation
- High Availability in HDFS
- HDFS Snapshots
- Other Improvements in HDFS2
- Introduction to YARN aka MR2
- Limitations in MR1
- Architecture of YARN
- MapReduce Job Flow in YARN
- Introduction to Stinger Initiative and Tez
- BackWard Compatibility for Hadoop 1.X
- Spark Fundamentals
- RDD- Sample Scala Program- Spark Streaming
- Difference between SPARK1.x and SPARK2.x
- PySpark program to create word count program in pyspark
- Hadoop
- HDFS architecture and usage
- MapReduce Architecture and real time exercises
- Hadoop Eco systems
- Sqoop - mysql Db Migration
- Hive. -- Deep drive
- Pig - weblog parsing and ETL
- Oozie - Workflow scheduling
- Flume - weblogs ingestion
- No SQL
- HBase
- Apache Kafka
- Pentaho ETL tool integration & working with Hadoop eco system
- Apache SPARK
- Introduction and working with RDD.
- Multinode Setup Guidance
- Hadoop latest version Pros & cons discussion
- Ends with Introduction of Data science.
- Getting applications web logs
- Getting user information from my sql via sqoop
- Getting extracted data from Pig script
- Creating Hive SQL Table for querying
- Creating Reports from Hive QL
Click Stream Data Analytics Report Project
ClickStream Data
ClickStream data could be generated from any activity performed by the user over a web application. What could be the user activity over any website? For example, I am logging into Amazon, what are the activities I could perform? In a pattern, I may navigate through some pages; spend some time over certain pages and click on certain things. All these activities, including reaching that particular page or application, clicking, navigating from one page to another and spending time make a set of data. All these will be logged by a web application. This data is known as ClickStream Data. It has a high business value, specific to e-commerce applications and for those who want to understand their users’ behavior.
More formally, ClickStream can be defined as data about the links that a user clicked, including the point of time when each one of them were clicked. E-commerce businesses mine and analyse ClickStream data on their own websites. Most of the E-commerce applications have their built-in system, which mines all this information.
ClickStream Analytics
Using the ClickStream data adds a lot of value to businesses, through which they can bring many customers or visitors. It helps them understand whether the application is right, and the application experience of users is good or bad, based on the navigation patterns that people take. They can also predict which page you are most likely to visit next and can-do Ad Targeting as well. With this, they can understand the needs of users and come up with better recommendations. Several other things are possible using the ClickStream Data.
Project Scope
In this project candidates are given with sample click stream data which is taken from a web application in a text file along with problem statements.
- Users information in MySQL database.
- Click stream data in text file generated from Web application.
Each candidate has to come up with high level system architecture design based upon the Hadoop eco systems covered during the course. Each candidate has to table the High-level system architecture along with designed eco systems and pros and cons will be discussed with all the other candidates. Finally, will choose the best possible optimal system design approach for implementation.
Candidates are given instructions to create an oozie work flow with the respective Hadoop Eco systems finalized based on the discussion. Candidates has to submit the project for the given problem statement and this will be validated by the trainer individually before course completion.
ECO System involved in click stream analytics Project
HDFS, Sqoop, Pig, Hive, Oozie
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