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Certificate Course in Big Data & Hadoop

Rs.3,000.00 Rs.1,500.00

This is an industry-recognized Big Data certification training course that is a combination of the training courses.

in Hadoop developer, Hadoop administrator, Hadoop testing and analytics with Apache Spark.

Description

Course Name: Certificate Course in Big Data & Hadoop
Course Id: CCBDH/Q001.
Education Qualification: Graduate.
Course and Exam Fee: 3000.

How You will Get Diploma Certificate:

Step 1- Select your Course for Certification.

Step 2- Click on Enroll Now.

Step 3- Proceed to Enroll Now.

Step 4- Fill Your Billing Details and Proceed to Pay.v

Step 5- You Will be Redirected to Payment Gateway, Pay Course and Exam Fee by Following Options.

Card(Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.

Step 6- After Payment You will receive Study Material and Online Examination link on your email id.

Step 7- After Completion of  Course Study give Online Examination.

Step 8- After Online Examination within 7 days you will get Diploma Certificate soft copy(Scan Copy) and Hard Copy(Original With Seal and Sign) within 30 days.

Step 9- After Certification you will receive Prospect Job Opportunities as per your Interest Area.

Online Examination Detail:

Duration- 60 minutes.

No. of Questions- 30. (Multiple Choice Questions).

Maximum Marks- 600, Passing Marks- 40%.

There is No Negative marking in this module.

Benefits of Certification:

  • Government Authorized Assessment Agency Certification.
  • Certificate Valid for Lifetime.
  • Lifetime Verification of Certificate.
  • Free Job Assistance as per your Interest Area.

Or You Can Fill below Enquiry form For Regular Course Training from our Training Centers located in India.

Student Course Enquiry Form

Syllabus

 

Certificate Course in Big Data &Hadoop
The Architecture of Hadoop cluster
What is High Availability and Federation
How to setup a production cluster
Various shell commands in Hadoop
Understanding configuration files in Hadoop
Installing a single node cluster with Cloudera Manager

 

The Architecture of Hadoop cluster

Introducing Big Data and Hadoop, what is Big Data and where does Hadoop, fit in two important Hadoop, ecosystem, components, namely, Map Reduce and HDFS, Two important Hadoop ecosystem, components, namely, Map Reduce and HDFS, working mechanism, Data replication process, How to determine the size of the block? Understanding a data node and name, node Learning the working mechanism of Map, Reduce, Understanding the mapping and reducing stages in MR, various terminologies in MR, like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort. How to write a Word Count program in Map Reduce? How to write a Custom Partitioner, What is a Map Reduce Combiner, How to run a job in a local job runner, deploying a unit test what is a map side join and reduce side join.

What is High Availability and Federation

What is a tool runner, How to use counters, dataset joining with map side and reduce side joins? Introducing Hadoop, Hive Detailed, architecture of Hive Comparing, Hive with Pig and RDBMS, Working with Hive Query Language Creation of a database, table, group by and other clauses, Various types of Hive tables, HC atalog Storing the Hive Results, Hive partitioning, and Buckets Database, creation in Hive Dropping a database, Hive table creation, How to change the database? Data loading, Dropping and altering table, pulling data by writing, Hive queries with filter conditions, table partitioning in Hive.

How to setup a production cluster

Storing the data into files and working with Group By, Filter By, Distinct, Cross, Split in Hive Apache Sqoop, introduction, Importing and exporting data Performance, improvement with Sqoop limitations, Introduction to Flume and understanding the architecture of Flume, What is HBase and the CAP theorem, Working with Flume to generate Sequence, Number and consume it, Using the Flume Agent to consume the Twitter, data Using AVRO to create Hive Table AVRO with Pig Creating, Table in HBase Deploying Disable, Scan, and Enable, Table Using Scala for writing Apache Spark applications, Detailed study of Scala, The need for Scala.

Various shell commands in Hadoop

Writing Spark application using Scala, Understanding the robustness of Scala for Spark, real-time analytics operation, Detailed Apache, Spark and its various features, Comparing with Hadoop Various Spark components, Combining HDFS with Spark and Scalding, Introduction to Scala, Importance of Scala and RDD, The Resilient Distributed, Dataset (RDD) in Spark, How does it help to speed up Big Data processing? Understanding the Spark RDD operations, Comparison of Spark with Map , operations viz. transformation and action, what is a Key/Value pair? How to deploy RDD with HDFS? using the in-memory dataset.

Understanding configuration files in Hadoop

What is schema, manual inferrin, Work with CSV files, JDBC table reading, data conversion from Data Frame to JDBC, Spark SQL user-defined functions, shared variable, and accumulators, How to query and transform, data in Data Frames, How data frames provide the benefits of both, Spark RDD and Spark SQL Deploying, Hive on Spark as the execution, engine Data querying and transformation, using Data Frames, Finding out the benefits of Data Frames, over Spark SQL and Spark RDD,I Introduction to Spark  MLlib, Understanding various algorithms, What is Spark iterative algorithm? Spark graph processing analysis.

Installing a single node cluster with Cloudera Manager

Learn about Cloudera Manager, Installing and Upgrading, Managing CDH using Cloudera Manager, Monitoring CDH using Cloudera Manager, Managing CDH using the Cloudera Manager API, Health issue, Cloudera Management Service, Cloudera Manager API, Cloudera Manager Server, Activity Monitor, Navigator Audit Server, Navigator Metadata Server, Reports Manager, Sending Usage and Diagnostic Data to Cloudera, Specifying the Diagnostic Data Directory, Exporting and Importing Cloudera Manager Configuration, Installation, Add Cluster, Add Service, Upgrade, Static Service Pools, Import MapReduce.