Big Data Hadoop Training Institutes in Noida, Delhi- Fee Is 14000 Rs (Self Practice Video 1500 Rs only)

Best Big Data Hadoop Training Institute In Noida
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Webtrackker Training Centre


Best Big Data Hadoop Training Institute In Noida & Big Data Hadoop Training Institute In Noida

Big Data Hadoop Training Institutes in Noida- with 100% placement support - Fee Is 15000 Rs - Hadoop is a software framework open-source store data and run application on clusters of commodity hardware. It provides huge storage capacity for each type of data, the tremendous processing power and the ability to simultaneously tasks or processes to manage virtually unlimited. Big Data represents a really big data, is a collection of large data that cannot be treated by conventional techniques. Big data is not only a fact; rather it has become a complete topic, with different tools, techniques and structures. Big data technologies are important for a more accurate analysis, which can lead to more concrete decisions resulting in provide better operational efficiency, lower costs and reduced business risk.Big Data Hadoop Training Institutes in Noida- Delhi

Big Data is a collection of large and complex data sets that cannot be treated with regular database management tools or processing applications. Many challenges such as catch, tent, storage, search, sharing, analysis and visualization can be detected when handling big data.

Webtrackker has scheduled a series of educational programs as necessary popular and time. This special course is so arranged as to complete the formation absolute within a short time a saving of valuable time for people. It can be very useful for people who are already working. Staff Webtrackker believes construction at the base, and an expert on them. Various forms of Webtrackkerion be performed; Test simulated activities and practices of issue solving lessons are taken. The training modules are provided in the first place for a realistic basis Webtrackker to bring a specialist of all off. if you are looking for great training Hadoop data, Oracle large training data, Oracle great course dates, great training camp of the data, forming the NoSQL database.Big Data Hadoop Training Institutes in Noida, sector 63, 64, 65, 18, 15.2, 3, 5, Meerut, Delhi NCR, Ghaziabad please contact us.

Top 20 Reasons to Choose WEBTRACKKER for Hadoop Training in Noida

  • Hadoop training in Noida is designed according to current IT market.
  • Offer the best Hadoop training and placement in Noida with well defined training modules and course sessions.
  • Facilitate regular, weekend and customized Hadoop training in Noida..
  • One of the biggest team of Certified Expert Trainers with 5 to 15 years of Real Industry Experience.
  • Mentors of Hadoop training in Noida helps in major project training, minor project training, live project preparation, interview preparation and job placement support.
  • Smart Labs with Real Latest Equipments.
  • 24x7 Lab Facilities. Students are free to access the labs for unlimited number of hours as per their own preferred timings.
  • Smart classrooms fully equipped with projectors, live racks, Wi-Fi connectivity, Digital Pads.
  • Silent and Discussion Zone areas in Labs to enhance Self Study and Group Discussions.
  • Free of Cost Personality Development sessions including Spoken English, Group Discussions, Mock Interviews, Presentation skills.
  • Free of Cost Seminars for Personality Development & Personal Presentation.
  • Varity of Study Material: Books, PDF’s, Video Lectures, Sample questions, Interview Questions (Technical and HR), and Projects.
  • Hostel Facilities available at Rs 5,500/month for Hadoop Training in Noida students.
  • Free Study Material, PDFs, Video Trainings, Sample Questions, Exam Preparation, Interview Questions, Lab Guides.
  • Globally Recognized Course Completion Certificate.
  • Extra Time Slots (E.T.S.) for Practical's(Unlimited), Absolutely Free.
  • The ability to retake the class at no-charge as often as desired.
  • One-on-One attention by instructors.
  • Helps students to take knowledge of complex technical concepts.
  • Payment options: Cheque, Cash, Credit Card, Debit card, Net Banking.

WEBTRACKKER Trainer's Profile for Hadoop Training in Noida

WEBTRACKKER'S Hadoop Trainers are:

  • Are truly expert and fully up-to-date in the subjects they teach because they continue to spend time working on real-world industry applications.
  • Have received awards and recognition from our partners and various recognized IT Organizations.
  • Are working professionals working in multinational companies such as HCL Technologies, Birlasoft, TCS, IBM, Sapient, Agilent Technologies etc.
  • Are certified Professionals with 7+ years of experience.
  • Are Well connected with Hiring HRs in multinational companies.

Placement Assistance after Hadoop Training in Noida

WEBTRACKKER'S Placement Assistance

  • WEBTRACKKER is the leader in offering placement to the students, as it has a dedicated placement wing which caters to the needs of the students during placements.
  • WEBTRACKKER helps the students in the development of their RESUME as per current industry standards.
  • WEBTRACKKER conducts Personality Development sessions including Spoken English, Group Discussions, Mock Interviews, Presentation skills to prepare students to face challenging interview situation with ease.
  • WEBTRACKKER has prepared its students to get placed in top IT FIRMS like HCL, TCS, Infosys, Wipro, Accenture and many more.

webtrackker Course duration for Hadoop Training in Noida

  • Fast Track Training Program (4+ hours Saturday And Sunday)
  • Demo Classes (Free Demo Class Time 1 pm Saturday And Sunday)
  • Weekend Training Classes (Saturday, Sunday & Holidays)
Webtrackker Projects

Webtrackker is IT based company in many countries. Webtrackker will provide you a real time projects based traning on big data

Modules About Modules
Hadoop Course Content: Hadoop Overview, Architecture Considerations, Infrastructure, Platforms and Automation
Use case walkthrough ETL, Log Analytics, Real Time Analytics
Modules Hbase for Developers
NoSQL Introduction Traditional RDBMS approach, NoSQL introduction, Hadoop & Hbase positioning
Hbase Introduction What it is, what it is not, its history and common use-cases, Hbase Client – Shell, exercise
Hbase Architecture Building Components, Storage, B+ tree, Log Structured Merge Trees, Region Lifecycle, Read/Write Path
Hbase Schema Design Introduction to hbase schema, Column Family, Rows, Cells, Cell timestamp, Deletes, Exercise - build a schema, load data, query data
Hbase Java API – Exercises Connection, CRUD API, Scan API, Filters, Counters, Hbase MapReduce, Hbase Bulk load
Hbase Operations, cluster management Performance Tuning, Advanced Features, Exercise, Recap and Q&A
MapReduce for Developers
Introduction Traditional Systems / Why Big Data / Why Hadoop, Hadoop Basic Concepts/Fundamentals
Hadoop in the Enterprise Where Hadoop Fits in the Enterprise, Review Use Cases
Architecture Hadoop Architecture & Building Blocks, HDFS and MapReduce
Hadoop CLI Walkthrough, Exercise
MapReduce Programming Fundamentals, Anatomy of MapReduce Job Run, Job Monitoring, Scheduling, Sample Code Walk Through, Hadoop API Walk Through, Exercise
MapReduce Formats Input Formats, Exercise, Output Formats, Exercise
Hadoop File Formats
MapReduce Design Considerations
Hadoop File Formats
MapReduce Algorithms
Walkthrough of 2-3 Algorithms
MapReduce Features Counters, Exercise, Map Side Join, Exercise, Reduce Side Join, Exercise, Sorting, Exercise
Use Case A (Long Exercise) Input Formats, Exercise, Output Formats, Exercise
MapReduce Testing
Hadoop Ecosystem Oozie, Flume, Sqoop, Exercise 1 (Sqoop), Streaming API, Exercise 2 (Streaming API), Hcatalog, Zookeeper
HBase Introduction Introduction, HBase Architecture
VIEW Types Default Views, Overriden Views, Normal Views
MapReduce Performance Tuning
Development Best Practice and Debugging
Apache Hadoop for Administrators
Hadoop Fundamentals and Architecture Why Hadoop, Hadoop Basics and Hadoop Architecture, HDFS and Map Reduce
Hadoop Ecosystems Overview Hive, Hbase, ZooKeeper, Pig, Mahout, Flume, Sqoop, Oozie
Hardware and Software requirements Hardware, Operating System and Other Software, Management Console
Deploy Hadoop ecosystem services Hive, ZooKeeper, HBase, Administration, Pig, Mahout, Mysql, Setup Security
Enable Security – Configure Users, Groups, Secure HDFS, MapReduce, HBase and Hive Configuring User and Groups, Configuring Secure HDFS, Configuring Secure MapReduce, Configuring Secure HBase and Hive
Manage and Monitor your cluster
Command Line Interface
Troubleshooting your cluster
Introduction to Big Data and Hadoop
Hadoop Overview Why Hadoop, Hadoop Basic Concepts, Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout, Where Hadoop fits in the Enterprise, Review use cases
Apache Hive & Pig for Developers
Overview of Hadoop Why Hadoop, Hadoop Basic Concepts, Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout, Where Hadoop fits in the Enterprise, Review use cases
Overview of Hadoop Big Data and the Distributed File System, MapReduce
Hive Introduction Why Hive?, Compare vs SQL, Use Cases
Hive Architecture – Building Blocks Hive CLI and Language (Exercise), HDFS Shell, Hive CLI, Data Types, Hive Cheat-Sheet, Data Definition Statements, Data Manipulation Statements, Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins, Built-in Functions, Union, Sub Queries, Sampling, Explain
Hive Architecture – Building Blocks Hive CLI and Language (Exercise) HDFS Shell, Hive CLI, Data Types, Hive Cheat-Sheet, Data Definition Statements, Data Manipulation Statements, Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins, Built-in Functions, Union, Sub Queries, Sampling, Explain
Hive Architecture – Building Blocks Hive CLI and Language (Exercise), HDFS Shell, Hive CLI, Data Types, Hive Cheat-Sheet, Data Definition Statements, Data Manipulation Statements, Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins, Built-in Functions, Union, Sub Queries, Sampling, Explain
Hive Usecase implementation -(Exercise) Use Case 1, Use Case 2, Best Practices
Advance Features Transform and Map-Reduce Scripts, Custom UDF, UDTF, SerDe, Recap and Q&A
Pig Introduction Position Pig in Hadoop ecosystem, Why Pig and not MapReduce, Simple example (slides) comparing Pig and MapReduce, Who is using Pig now and what are the main use cases, Pig Architecture, Discuss high level components of Pig, Pig Grunt - How to Start and Use
Pig Latin Programming Data Types, Cheat sheet, Schema, Expressions, Commands and Exercise, Load, Store, Dump, Relational Operations,Foreach, Filter, Group, Order By, Distinct, Join, Cogroup,Union, Cross, Limit, Sample, Parallel
Use Cases (working exercise) Use Case 1, Use Case 2, Use Case 3 (compare pig and hive)
Advanced Features, UDFs
Best Practices and common pitfalls
Mahout & Machine Learning Mahout Overview, Mahout Installation, Introduction to the Math Library, Vector implementation and Operations (Hands-on exercise), Matrix Implementation and Operations (Hands-on exercise), Anatomy of a Machine Learning Application
Classification Introduction to Classification, Classification Workflow, Feature Extraction, Classification Techniques (Hands-on exercise)
Evaluation (Hands-on exercise) Clustering, Use Cases, Clustering algorithms in Mahout, K-means clustering (Hands-on exercise), Canopy clustering (Hands-on exercise)
Clustering Mixture Models, Probabilistic Clustering – Dirichlet (Hands-on exercise), Latent Dirichlet Model (Hands-on exercise), Evaluating and Improving Clustering quality (Hands-on exercise), Distance Measures (Hands-on exercise)
Recommendation Systems Overview of Recommendation Systems, Use cases, Types of Recommendation Systems, Collaborative Filtering (Hands-on exercise), Recommendation System Evaluation (Hands-on exercise), Similarity Measures, Architecture of Recommendation Systems, Wrap Up


Cloudera Admin Certification Program





Cloudera Certified Administrator for Hadoop100% Clearance Guaranty

(CCAH) Exam Code: CCA-410

hadoop_Certification

Cloudera Certified Administrator for Apache Hadoop Exam :

  • Number of Questions: 60
  • Item Types: multiple-choice & short-answer questions
  • Exam time: 90 Mins.
  • Passing score: 70%
  • Price: $295 USD

Syllabus Cloudera Administrator Certification Exam

HDFS 38%
  • Describe the function of all Hadoop Daemons
  • Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing.
  • Identify current features of computing systems that motivate a system like Apache Hadoop.
  • Classify major goals of HDFS Design
  • Given a scenario, identify appropriate use case for HDFS Federation
  • Identify components and daemon of an HDFS HA-Quorum cluster
  • Analyze the role of HDFS security (Kerberos)
  • Determine the best data serialization choice for a given scenario
  • Describe file read and write paths
  • Identify the commands to manipulate files in the Hadoop File System Shell.
MapReduce 10%
  • Understand how to deploy MapReduce MapReduce v1 (MRv1)
  • Understand how to deploy MapReduce v2 (MRv2 / YARN)
  • Understand basic design strategy for MapReduce v2 (MRv2)
Hadoop Cluster Planning 12%
  • Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster.
  • Analyze the choices in selecting an OS
  • Understand kernel tuning and disk swapping
  • Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
  • Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
  • Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
  • Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
Hadoop Cluster Installation and Administration 17%
  • Given a scenario, identify how the cluster will handle disk and machine failures.
  • Analyze a logging configuration and logging configuration file format.
  • Understand the basics of Hadoop metrics and cluster health monitoring.
  • Identify the function and purpose of available tools for cluster monitoring.
  • Identify the function and purpose of available tools for managing the Apache Hadoop file system.
Resource Management 06%
  • Understand the overall design goals of each of Hadoop schedulers.
  • Given a scenario, determine how the FIFO Scheduler allocates cluster resources.
  • Given a scenario, determine how the Fair Scheduler allocates cluster resources.
  • Given a scenario, determine how the Capacity Scheduler allocates cluster resources
Monitoring and Logging 12%
  • Understand the functions and features of Hadoop’s metric collection abilities
  • Analyze the NameNode and JobTracker Web UIs
  • Interpret a log4j configuration
  • Understand how to monitor the Hadoop Daemons
  • Identify and monitor CPU usage on master nodes
  • Describe how to monitor swap and memory allocation on all nodes
  • Identify how to view and manage Hadoop’s log files
  • Interpret a log file
The Hadoop Ecosystem 05%
  • Understand Ecosystem projects and what you need to do to deploy them on a cluster.


Cloudera Certified Developer for Hadoop100% Clearance Guaranty

(CCDH) Exam Code: CCD-410

hadoop_Certification

Cloudera Certified Developer for Apache Hadoop Exam:

  • Number of Questions: 50 – 55 live questions
  • Item Types: multiple-choice & short-answer questions
  • Exam time: 90 Mins.
  • Passing score: 70%
  • Price: $295 USD

Syllabus Cloudera Develpoer Certification Exam

Infrastructure Objectives 25%
  • Recognize and identify Apache Hadoop daemons and how they function both in data storage and processing.
  • Understand how Apache Hadoop exploits data locality.
  • Identify the role and use of both MapReduce v1 (MRv1) and MapReduce v2 (MRv2 / YARN) daemons.
  • Analyze the benefits and challenges of the HDFS architecture.
  • Analyze how HDFS implements file sizes, block sizes, and block abstraction.
  • Understand default replication values and storage requirements for replication.
  • Determine how HDFS stores, reads, and writes files.
  • Identify the role of Apache Hadoop Classes, Interfaces, and Methods.
  • Understand how Hadoop Streaming might apply to a job workflow
Data Management Objectives 30%
  • Import a database table into Hive using Sqoop.
  • Create a table using Hive (during Sqoop import).Successfully use key and value types to write functional MapReduce jobs.
  • Given a MapReduce job, determine the lifecycle of a Mapper and the lifecycle of a Reducer.
  • Analyze and determine the relationship of input keys to output keys in terms of both type and number, the sorting of keys, and the sorting of values.
  • Given sample input data, identify the number, type, and value of emitted keys and values from the Mappers as well as the emitted data from each Reducer and the number and contents of the output file(s).
  • Understand implementation and limitations and strategies for joining datasets in MapReduce.
  • Understand how partitioners and combiners function, and recognize appropriate use cases for each.
  • Recognize the processes and role of the the sort and shuffle process.
  • Understand common key and value types in the MapReduce framework and the interfaces they implement.
  • Use key and value types to write functional MapReduce jobs.
Job Mechanics Objectives 25%
  • Construct proper job configuration parameters and the commands used in job submission.
  • Analyze a MapReduce job and determine how input and output data paths are handled.
  • Given a sample job, analyze and determine the correct InputFormat and OutputFormat to select based on job requirements.
  • Analyze the order of operations in a MapReduce job.
  • Understand the role of the RecordReader, and of sequence files and compression.
  • Use the distributed cache to distribute data to MapReduce job tasks.
    Build and orchestrate a workflow with Oozie.
Querying Objectives 20%
  • Write a MapReduce job to implement a HiveQL statement.
  • Write a MapReduce job to query data stored in HDFS.
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