Course Name: Certificate in Big Data & Hadoop
Course Id: CBDH/Q001.
Education Qualification: Graduate.
Duration: 90 Hrs.
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.
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 on your email id.
Step 7- After Completion of Course Study give Online Examination.
Step 8- After Online Examination you will get Diploma Certificate soft copy(Scan Copy) and Hard Copy(Original With Seal and Sign).
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- 100, 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.
Introduction to Big Data Hadoop: Bing data Hadoop, Data analytics, Hadoop distributed file system, apache open source hadoop ecosystem elements, advantages of Hadoop, flexible, Hadoop cluster, replication and rack awareness, Hadoop map reduce, IBM infoshpere big insights, Basic and enterprise, adaptive map reduce. Hadoop cluster set up, Hadoop cluster architecture, single Hadoop cluster vs. multi node Hadoop cluster.
Components of Spark: concepts of big data and what it, hadoop key components, understanding HDFS, map, educe, YARN, setting up HDGs- distributed storage technique, working with HDFS, starting deep dive with distributed com. Spark architecture.
Spark framework: stateful strem processing, existing streaming systems, discretized stream processing, discretized stream processing, Get hash tag from twitter, java example, fault tolerance, key concepts, other interesting operations, real application: mobile millennium project, spark program vs spark streaming program, alpha release with spark 0.7.
Resilient Distributed Datasets: Introduction, Resilient distributed datasets (RDDs), spark programming interface, representing RDDs, implementation, evaluation, related work, expressing existing programming models.
Introduction to Hive: Installing Hive, Hive services, hive clients, comparison with traditional data base, SQL- on- Hadoop alternatives, primitive types, importing data, user defined functions, the Metastore, updates, transactions and indexes.
RDD in Spark: Distributed processing, resilient distributed database, a distributed datasets, a distributed query processing engine, the spark counterpart to hadoop map reduce designed for in memory processing, Spark-high-level architecture, Types of dependencies.