Description
Course Name: Certificate in Big Data & Hadoop
Course Id: CBDH/Q001.
Eligibility: Completion of 10+2 (higher Secondary) or equivalent.
Objective: The Certificate in Big Data & Hadoop is designed to provide learners with a strong foundation in big data concepts, technologies, and tools, focusing on the Hadoop ecosystem. The course covers data storage, processing, and analysis techniques using Hadoop components such as HDFS, MapReduce, and related frameworks.
Duration: Two Month.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
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.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Syllabus:-
Introduction to Big Data and Hadoop: Understanding Big Data and its Characteristics, Challenges in Traditional Data Processing, Overview of Hadoop Ecosystem, Evolution of Hadoop and Its Importance, Hadoop Distributed File System (HDFS) Architecture, MapReduce Fundamentals, Introduction to YARN (Yet Another Resource Negotiator), Hadoop Cluster Architecture, Key Applications of Big Data, Hands-on Installation of Hadoop.
Hadoop Distributed File System (HDFS): HDFS Architecture and Components, Data Blocks and Replication in HDFS, NameNode, DataNode, and Secondary NameNode, Read/Write Mechanism in HDFS, Fault Tolerance and High Availability in HDFS, HDFS Commands and Operations, HDFS Access Control and Security, Hadoop File Formats (Sequence, Avro, Parquet), Importing and Exporting Data in HDFS, Hands-on Working with HDFS.
MapReduce Programming Model: Introduction to MapReduce, Map and Reduce Functions, Writing and Executing MapReduce Programs, Combiner and Partitioner in MapReduce, InputFormat and OutputFormat in MapReduce, Performance Tuning and Optimization Techniques, Understanding Counters and Joins in MapReduce, Real-World Use Cases of MapReduce, Limitations of MapReduce and Alternatives, Hands-on MapReduce Development.
Hadoop Ecosystem Components: Introduction to Hadoop Ecosystem, Apache Pig for Data Transformation, Apache Hive for Data Warehousing, Apache HBase for NoSQL Storage, Apache Sqoop for Data Migration, Apache Flume for Data Ingestion, Apache Oozie for Workflow Scheduling, Apache ZooKeeper for Coordination, Real-Time Processing with Apache Storm, Hands-on Hadoop Ecosystem Tools.
Apache Hive and SQL on Hadoop: Introduction to Apache Hive, Hive Architecture and Components, Hive Query Language (HQL), Partitioning and Bucketing in Hive, Optimizing Hive Queries with Indexing, User-Defined Functions (UDF) in Hive, Integrating Hive with HDFS and HBase, Data Serialization in Hive, Real-World Hive Use Cases, Hands-on Hive Query Execution.
Apache Pig for Data Processing: Introduction to Apache Pig, Pig Latin Programming Language, Data Processing with Pig Scripts, Working with Relational Operators in Pig, Advanced Transformations and Joins in Pig, Handling Complex Data Types in Pig, Performance Optimization in Pig, Integrating Pig with HDFS and Hive, Pig vs. Hive vs. MapReduce Comparison, Hands-on Apache Pig Development.
