Hadoop Training Objectives
- If you would like to figure with big data, then learning Hadoop may be a must because it is becoming the de facto standard for giant processing. The challenge with this is often that we aren't robots and can't learn everything. it's very difficult to master every tool, technology or programing language.
- Although Hadoop may be a Java-encoded open-source software framework for distributed storage and processing of huge amounts of knowledge , Hadoop doesn't require much coding. All you've got to try to to is enroll during a Hadoop certification course and learn Pig and Hive, both of which require only the essential understanding of SQL.
- Hadoop isn't a kind of database, but rather a software ecosystem that permits for massively parallel computing. it's an enabler of certain types NoSQL distributed databases (such as HBase), which may leave data to be spread across thousands of servers with little reduction in performance.
- Future Scope of Hadoop. As per the Forbes report, the Hadoop and therefore the Big Data market will reach $99.31B attaining a 28.5% CAGR. The below image describes the dimensions of Hadoop and large Data Market worldwide. From the above image, we will easily see the increase in Hadoop and therefore the big data market.
- If you're attempting to find out Hadoop on your own, it'll take tons of your time. So Enroll with our Hadoop course then you'll expect it'll take a minimum of 4-6 months to master Hadoop certification and begin your big data training.
- So, if you would like to become a knowledge scientist, learning Hadoop is beneficial to hurry up the method of becoming a knowledge scientist. However, not knowing Hadoop will in no way disqualify you as a knowledge scientist.
- Knowledge of Java isn't mandatory to find out Hadoop. you would possibly remember that Hadoop is written in Java, but, on contrary, I might wish to tell you, the Hadoop ecosystem is fairly designed to cater different professionals who are coming from different backgrounds.
- Yes you'll gain In-depth knowledge of massive Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) MapReduce.
- Also we teach you about the potential to ingest data in HDFS using Sqoop Flume, and analyze those large datasets stored within the HDFS.
- Also you'll work with Projects which are diverse in nature covering various data sets from multiple domains like banking, telecommunication, social media, insurance, and e-commerce.
- Hadoop skills are in demand – this is often an undeniable fact! Hence, there's an urgent need for IT professionals to stay themselves in trend with Hadoop and large Data technologies. Apache Hadoop provides you with means to build up your career and provides you the subsequent advantages: Accelerated career growth.
- Since Apache Hadoop was written in Java, the developers at Hortonworks use Java for several of the sub-projects and other open source products that structure the Hortonworks Data Platform (HDP). It also programs in Java for Hortonworks Data Flow (HDF), which is predicated on the Java-based Apache NiFi.
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Top Companies Placement
- Designation
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Annual SalaryHiring Companies
Top Skills You Will Gain
- Big Data, HDFS
- YARN, Spark
- MapReduce
- PIG, HIVE
- HBase, Mahout
- Spark MLLib
- Solar, Lucene
- Zookeeper, Oozie
Online Classroom Batches Preferred
No Interest Financing start at ₹ 5000 / month
Corporate Training
- Customized Learning
- Enterprise Grade Learning Management System (LMS)
- 24x7 Support
- Enterprise Grade Reporting
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 Visakhapatnam 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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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 Visakhapatnam 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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Hadoop Course FAQ's
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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 Visakhapatnam 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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- 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 Visakhapatnam, 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 Visakhapatnam 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.
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