Online Classroom Batches Preferred
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekend Regular
(Class 3hr - 3:30Hrs) / Per Session
Weekend Fasttrack
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Top Skills You Will Gain
- Big Data, HDFS
- YARN, Spark
- MapReduce
- PIG, HIVE
- HBase, Mahout
- Spark MLLib
- Solar, Lucene
- Zookeeper, Oozie
Hadoop Course Key Features 100% Money Back Guarantee
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5 Weeks Training
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Certificate of Training
From Industry Hadoop Experts -
Beginner Friendly
No Prior Knowledge Required -
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Placement Assistance
To Build Your Career
Top Companies Placement
A Hadoop Developer is a professional responsible for programming Hadoop applications and knows about all the components or pieces of the Hadoop Ecosystem, understands how the Hadoop components fit together, and have the ability to decide on which is the best Hadoop component for a specific task and are rewarded with substantial pay raises as shown below.
- Designation
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Annual SalaryHiring Companies
Hadoop Course Curriculam
Trainers Profile
LearnoVita trainers in available for Hadoop Online Course including 24/7 live support. The essence of the hadoop is affording recorded sessions, demos, and study materials. Our Instructors are working in Hadoop and with real time experienced for 10+ more years in MNC's . Our training will be focused on assisting in placements as well.
Pre-requisites
Basic prerequisites for learning Big Data Testing : Linux , Java , SQL.
Syllabus of Hadoop Course in Ahmedabad Download syllabus
- High Availability
- Scaling
- Advantages and Challenges
- What is Big data
- Big Data opportunities,Challenges
- CharLearnoVitaristics of Big data
- Hadoop Distributed File System
- Comparing Hadoop & SQL
- Industries using Hadoop
- Data Locality
- Hadoop Architecture
- Map Reduce & HDFS
- Using the Hadoop single node image (Clone)
- HDFS Design & Concepts
- Blocks, Name nodes and Data nodes
- HDFS High-Availability and HDFS Federation
- Hadoop DFS The Command-Line Interface
- Basic File System Operations
- Anatomy of File Read,File Write
- Block Placement Policy and Modes
- More detailed explanation about Configuration files
- Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
- How to add New Data Node dynamically,decommission a Data Node dynamically (Without stopping cluster)
- FSCK Utility. (Block report)
- How to override default configuration at system level and Programming level
- HDFS Federation
- ZOOKEEPER Leader Election Algorithm
- Exercise and small use case on HDFS
- Map Reduce Functional Programming Basics
- Map and Reduce Basics
- How Map Reduce Works
- Anatomy of a Map Reduce Job Run
- Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
- Job Completion, Failures
- Shuffling and Sorting
- Splits, Record reader, Partition, Types of partitions & Combiner
- Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots
- Types of Schedulers and Counters
- Comparisons between Old and New API at code and Architecture Level
- Getting the data from RDBMS into HDFS using Custom data types
- Distributed Cache and Hadoop Streaming (Python, Ruby and R)
- YARN
- Sequential Files and Map Files
- Enabling Compression Codec’s
- Map side Join with distributed Cache
- Types of I/O Formats: Multiple outputs, NLINEinputformat
- Handling small files using CombineFileInputFormat
- Hands on “Word Count” in Map Reduce in standalone and Pseudo distribution Mode
- Sorting files using Hadoop Configuration API discussion
- Emulating “grep” for searching inside a file in Hadoop
- DBInput Format
- Job Dependency API discussion
- Input Format API discussion,Split API discussion
- Custom Data type creation in Hadoop
- ACID in RDBMS and BASE in NoSQL
- CAP Theorem and Types of Consistency
- Types of NoSQL Databases in detail
- Columnar Databases in Detail (HBASE and CASSANDRA)
- TTL, Bloom Filters and Compensation
- HBase Installation, Concepts
- HBase Data Model and Comparison between RDBMS and NOSQL
- Master & Region Servers
- HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture
- Catalog Tables
- Block Cache and sharding
- SPLITS
- DATA Modeling (Sequential, Salted, Promoted and Random Keys)
- Java API’s and Rest Interface
- Client Side Buffering and Process 1 million records using Client side Buffering
- HBase Counters
- Enabling Replication and HBase RAW Scans
- HBase Filters
- Bulk Loading and Co processors (Endpoints and Observers with programs)
- Real world use case consisting of HDFS,MR and HBASE
- Hive Installation, Introduction and Architecture
- Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
- Meta store, Hive QL
- OLTP vs. OLAP
- Working with Tables
- Primitive data types and complex data types
- Working with Partitions
- User Defined Functions
- Hive Bucketed Tables and Sampling
- External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
- Dynamic Partition
- Differences between ORDER BY, DISTRIBUTE BY and SORT BY
- Bucketing and Sorted Bucketing with Dynamic partition
- RC File
- INDEXES and VIEWS
- MAPSIDE JOINS
- Compression on hive tables and Migrating Hive tables
- Dynamic substation of Hive and Different ways of running Hive
- How to enable Update in HIVE
- Log Analysis on Hive
- Access HBASE tables using Hive
- Hands on Exercises
- Pig Installation
- Execution Types
- Grunt Shell
- Pig Latin
- Data Processing
- Schema on read
- Primitive data types and complex data types
- Tuple schema, BAG Schema and MAP Schema
- Loading and Storing
- Filtering, Grouping and Joining
- Debugging commands (Illustrate and Explain)
- Validations,Type casting in PIG
- Working with Functions
- User Defined Functions
- Types of JOINS in pig and Replicated Join in detail
- SPLITS and Multiquery execution
- Error Handling, FLATTEN and ORDER BY
- Parameter Substitution
- Nested For Each
- User Defined Functions, Dynamic Invokers and Macros
- How to access HBASE using PIG, Load and Write JSON DATA using PIG
- Piggy Bank
- Hands on Exercises
- Sqoop Installation
- Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV, Compressing, Control Parallelism, All tables Import)
- Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
- Free Form Query Import
- Export data to RDBMS,HIVE and HBASE
- Hands on Exercises
- HCatalog Installation
- Introduction to HCatalog
- About Hcatalog with PIG,HIVE and MR
- Hands on Exercises
- Flume Installation
- Introduction to Flume
- Flume Agents: Sources, Channels and Sinks
- Log User information using Java program in to HDFS using LOG4J and Avro Source, Tail Source
- Log User information using Java program in to HBASE using LOG4J and Avro Source, Tail Source
- Flume Commands
- Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG
- HUE.(Hortonworks and Cloudera)
- Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.,to show how to schedule Sqoop Job, Hive, MR and PIG
- Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour
- Zoo Keeper
- HBASE Integration with HIVE and PIG
- Phoenix
- Proof of concept (POC)
- Spark Overview
- Linking with Spark, Initializing Spark
- Using the Shell
- Resilient Distributed Datasets (RDDs)
- Parallelized Collections
- External Datasets
- RDD Operations
- Basics, Passing Functions to Spark
- Working with Key-Value Pairs
- Transformations
- Actions
- RDD Persistence
- Which Storage Level to Choose?
- Removing Data
- Shared Variables
- Broadcast Variables
- Accumulators
- Deploying to a Cluster
- Unit Testing
- Migrating from pre-1.0 Versions of Spark
- Where to Go from Here
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Mock Interviews
- Mock interviews by Learnovita give you the platform to prepare, practice and experience the real-life job interview. Familiarizing yourself with the interview environment beforehand in a relaxed and stress-free environment gives you an edge over your peers.
- In our mock interviews will be conducted by industry best Hadoop Training in Ahmedabad experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!
How Learnovita Mock Interview Works?
Hadoop Training Objectives
- Learning to code is an essential skill for any Big Data analyst. To perform numerical and statistical analysis on large data sets, you must code. Python, R, Java, and C++ are just a few of the languages you should invest time and money in learning.
- In 2015, Big Data engineers can expect a 9.3 percent increase in starting pay, with average salaries ranging from $119,250 to $168,250." In San Francisco, CA, the average salary for a Hadoop Developer is $139,000. A Senior Hadoop developer, CA can expect to earn more than $178,000 per year on average.
Big data: 5 major benefits of Hadoop
- Adaptable. Hadoop is a highly scalable storage platform because it can store and distribute extremely large data sets across hundreds of low-cost servers running in parallel.
- It is inexpensive. Hadoop also provides a low-cost storage solution for businesses' ever-expanding data sets.
- Adaptable.
- Quickly.
- Resilient in the face of failure.
- By delving into any of the Apache projects and other big data software offerings, one can easily learn and code on new big data technologies. The problem is that we are not robots and cannot learn everything. It is extremely difficult to become proficient in every tool, technology, or programming language.
- Programming is a type of programming. While traditional Data Analysts may be able to function without being a full-fledged programmer, a Big Data Analyst must be extremely comfortable with coding.
- Data Warehousing is the process of storing and analyzing data.
- Frameworks for computation.
- Statistics and Quantitative Aptitude.
- Knowledge of Business.
- Visualization of data.
- Java, I believe, is the fundamental big data programming language because it is used by all key big data technologies such as Apache Hadoop, Apache Hive, Apache HBase, Apache Cassandra, and others. Python and R are two other significant programming languages.
- Though Data Science Jobs is a broad word, there are numerous sub-roles available within it. Data Scientists, Data Architects, BI Engineers, Business Analysts, Data Engineers, Database Administrators, and Data- and Analytics Managers are in high demand.
- Although the Hadoop framework is written in Java, Hadoop programs can be written in Python or C++ as well. This means that data architects who are already familiar with Python do not need to learn Java. Because there aren't many Java programmers in the world of analytics, Python stands out as one of the most user-friendly, easy-to-learn, flexible languages that are also extremely powerful for end-to-end advanced analytics applications. We can write MapReduce programs in Python without having to translate the code into Java jar files.
The Very Best Big Data Tools and Software:
- Hadoop is a big data platform built on the Apache Hadoop software library.
- HPCC: LexisNexis Risk Solution created HPCC, a big data tool.
- A storm is a free and open-source big data computing framework.
- Qubole.
- Cassandra.
- Starting.
- CouchDB.
- Pentaho.
Here are five companies that are making good use of Hadoop:
- Marks & Spencer Marks and Spencer implemented Cloudera Enterprise in 2015 to analyze data from various sources.
- The Royal Mail.
- The Royal Bank of Scotland is a Scottish bank.
- British Airways is a British airline.
- Expedia.
Exam & Certification
At LearnoVita, You Can Enroll in Either the instructor-led Hadoop Online Course, Classroom Training or Online Self-Paced Training.
Hadoop Online Training / Class Room:
- Participate and Complete One batch of Hadoop Training Course
- Successful completion and evaluation of any one of the given projects
Hadoop Online Self-learning:
- Complete 85% of the Hadoop Certification Training
- Successful completion and evaluation of any one of the given projects
These are the Different Kinds of Certification levels that was Structured under the Cloudera Hadoop Certification Path.
- Cloudera Certified Professional - Data Scientist (CCP DS)
- Cloudera Certified Administrator for Hadoop (CCAH)
- Cloudera Certified Hadoop Developer (CCDH)
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LernoVita Hadoop Certification Training in Ahmedabad That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
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- 100% Placement Assistance* - We have strong relationship with over 650+ Top MNCs, When a student completes his/ her course successfully, LearnoVita Placement Cell helps him/ her interview with Major Companies like Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, HCL, Infosys, MindTree and MPhasis etc...
- LearnoVita is the Legend in offering placement to the students. Please visit our Placed Students's List on our website.
- More than 5400+ students placed in last year in India & Globally.
- LearnoVita is the Best Hadoop Training Institute in Ahmedabad Offers mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
- 85% percent placement record
- Our Placement Cell support you till you get placed in better MNC
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- LearnoVita is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (nie) Singapore
- Also, LearnoVita Technical Experts Help's People Who Want to Clear the National Authorized Certificate in Specialized IT Domain.
- LearnoVita is offering you the most updated Hadoop certification training in Ahmedabad, relevant, and high-value real-world projects as part of the training program.
- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
- You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc.
- After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
- We will reschedule the Hadoop classes in Ahmedabad as per your convenience within the stipulated course duration with all such possibilities.
- View the class presentation and recordings that are available for online viewing.
- You can attend the missed session, in any other live batch.