Description
Course Name: Certificate in Big Data Analytics (Hadoop)
Course Id: CBDA/Q0001.
Eligibility: 10th Grade (high school) or Equivalent.
Objective: A Certificate in Big Data Analytics (Hadoop) course provides individuals with the skills and knowledge to analyze and manage large datasets using the Hadoop framework. The course covers data processing, distributed storage, and tools within the Hadoop ecosystem to extract insights from big data. It is ideal for aspiring data analysts, data scientists, and IT professionals who want to specialize in big data technologies.
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 Ecosystem: Definition and Characteristics of Big Data (5 Vs), Evolution of Data Management Systems, Introduction to Hadoop Architecture and Core Components, Distributed Computing Concepts and Challenges, Comparison of Hadoop vs. RDBMS, Overview of the Hadoop Ecosystem (Pig, Hive, HBase, Sqoop), Hadoop Daemons (NameNode, DataNode, ResourceManager), Installation and Configuration of Hadoop, Understanding the Master-Slave Architecture, Industry Use Cases and Big Data Strategy.
Hadoop Distributed File System (HDFS) : HDFS Architecture and Design Goals, Data Replication and Fault Tolerance, Read and Write Anatomy in HDFS, HDFS Command Line Interface (CLI), Managing Blocks and Metadata, The Role of the Secondary NameNode, HDFS Permissions and Security, Data Integrity and Checksumming, Federation and High Availability (HA) in Hadoop 2.x/3.x, HDFS Safe Mode and Troubleshooting.
MapReduce Programming Model: Introduction to Parallel Processing with MapReduce, The Map Phase: Splitting and Mapping Data, The Reduce Phase: Shuffling, Sorting and Aggregating, Writing MapReduce Jobs in Java/Python, Key-Value Pair Abstraction, Combiners and Partitioners for Performance, Managing Distributed Cache and Side Data, Handling Data Formats (TextInputFormat, SequenceFileInputFormat), Error Handling and Debugging MapReduce Jobs, Limitations of MapReduce and Evolution of YARN.
Data Ingestion and Processing with Sqoop and Flume: Introduction to Sqoop for RDBMS Integration, Importing Data from MySQL/Oracle to HDFS, Exporting Data from HDFS back to RDBMS, Incremental Imports and Joins in Sqoop, Architecture of Apache Flume for Log Data, Configuring Flume Sources, Channels and Sinks, Real-time Data Streaming with Flume, Interfacing Flume with HDFS and HBase, Best Practices for Data Ingestion Pipelines, Security and Optimization in Data Movement.
Data Warehousing and Querying with Hive and Pig: Introduction to Apache Hive and HiveQL, Creating Managed and External Tables in Hive, Data Partitioning and Bucketing for Optimization, Writing Complex Joins and Subqueries in Hive, Hive User Defined Functions (UDFs), Introduction to Apache Pig and Pig Latin, Pig’s Data Model: Relations, Bags and Tuples, Transforming Data with Pig Grunt Shell and Scripts, Comparison between Hive (SQL-like) and Pig (Dataflow), Integrating Hive/Pig with HDFS and MapReduce.
Advanced Analytics, NoSQL and YARN: Introduction to NoSQL and Apache HBase Architecture, HBase Data Model: Rows, Columns, and Column Families, CRUD Operations in HBase Shell, Integrating HBase with Hadoop and Hive, Introduction to YARN (Yet Another Resource Negotiator), Resource Management and Scheduling in YARN, Overview of Apache Spark Integration with Hadoop, Introduction to Big Data Analytics Lifecycle, Real-time Analytics Concepts (Introduction to Kafka/Storm), Future Trends in Big Data and Cloud Integration.
Job Opportunities after completion of Certificate in Big Data Analytics (Hadoop) course:
The Certificate in Big Data Analytics (Hadoop) program equips graduates with the essential skills to work with large datasets using Hadoop and related technologies. Big Data Analytics is a crucial aspect of modern data-driven businesses, and expertise in Hadoop—a widely used open-source framework for handling big data—can open various career opportunities. Graduates of this program are well-prepared to analyze complex data, derive insights, and work with big data platforms to support business decision-making.
Career Options for Graduates in Big Data Analytics (Hadoop):
1. Big Data Analyst
- Role: Big Data Analysts are responsible for collecting, processing, and analyzing large datasets to uncover patterns, trends, and insights that drive business decisions. They often work with Hadoop ecosystems and tools like Hive, Pig, and MapReduce.
- Industries: E-commerce, finance, healthcare, retail, telecommunications, IT companies, and research organizations.
- Salary Range: ₹4,00,000 to ₹8,00,000 per year (entry-level); ₹8,00,000 to ₹15,00,000 per year (experienced).
2. Hadoop Developer
- Role: Hadoop Developers design and implement solutions using Hadoop framework. They write code and build applications to store, process, and analyze large data sets in Hadoop clusters. They work with tools like Hive, HBase, and Spark.
- Industries: IT services, e-commerce, tech companies, banking, and finance.
- Salary Range: ₹5,00,000 to ₹9,00,000 per year (entry-level); ₹9,00,000 to ₹18,00,000 per year (experienced).
3. Data Engineer
- Role: Data Engineers design, build, and maintain data pipelines that collect and process large amounts of data. They integrate big data technologies like Hadoop with other systems and optimize the flow of data for analysis.
- Industries: IT, e-commerce, finance, media, tech startups.
- Salary Range: ₹5,50,000 to ₹10,00,000 per year (entry-level); ₹10,00,000 to ₹18,00,000 per year (experienced).
4. Big Data Architect
- Role: Big Data Architects design the architecture of big data systems and ensure they are scalable, efficient, and secure. They are responsible for setting up Hadoop clusters and managing the overall infrastructure of data storage and processing.
- Industries: IT, financial services, telecommunications, e-commerce, healthcare.
- Salary Range: ₹8,00,000 to ₹14,00,000 per year (entry-level); ₹14,00,000 to ₹30,00,000 per year (experienced).
5. Data Scientist
- Role: Data Scientists analyze large datasets to extract valuable insights that can drive business strategies. They use advanced statistical and machine learning techniques to build models. They often work with big data tools like Hadoop to handle large datasets.
- Industries: E-commerce, IT services, retail, pharmaceuticals, finance, and healthcare.
- Salary Range: ₹6,00,000 to ₹12,00,000 per year (entry-level); ₹12,00,000 to ₹25,00,000 per year (experienced).
6. Business Intelligence Analyst
- Role: Business Intelligence (BI) Analysts use data analytics tools, including Hadoop, to provide insights into business performance. They work with large datasets to create reports, dashboards, and analytics that support decision-making processes.
- Industries: E-commerce, retail, healthcare, banking, and consulting firms.
- Salary Range: ₹4,50,000 to ₹8,00,000 per year (entry-level); ₹8,00,000 to ₹15,00,000 per year (experienced).
7. Machine Learning Engineer (with Big Data Focus)
- Role: Machine Learning Engineers focus on building algorithms that can learn from and make predictions on large datasets. They often leverage Hadoop to process and analyze vast amounts of data for machine learning model training and validation.
- Industries: E-commerce, tech startups, healthcare, finance, research, and AI companies.
- Salary Range: ₹7,00,000 to ₹14,00,000 per year (entry-level); ₹14,00,000 to ₹25,00,000 per year (experienced).
8. Data Analyst
- Role: Data Analysts extract and analyze data to identify trends, patterns, and correlations. They use tools like Excel, SQL, and Hadoop to work with large data sets and prepare data for analysis. They play a key role in business decision-making.
- Industries: E-commerce, retail, financial services, government, healthcare.
- Salary Range: ₹3,00,000 to ₹6,00,000 per year (entry-level); ₹6,00,000 to ₹12,00,000 per year (experienced).
9. Cloud Data Engineer
- Role: Cloud Data Engineers work with cloud-based big data technologies, including Hadoop, to process and analyze data stored in the cloud. They help in building scalable data pipelines, optimizing cloud resources, and ensuring data availability for analysis.
- Industries: IT services, cloud computing companies, e-commerce, finance.
- Salary Range: ₹6,00,000 to ₹12,00,000 per year (entry-level); ₹12,00,000 to ₹20,00,000 per year (experienced).
10. Data Visualization Specialist
- Role: Data Visualization Specialists transform complex data into interactive charts, graphs, and dashboards. They often use tools like Tableau, Power BI, and Hadoop for data extraction and visualization to make the insights accessible to business leaders.
- Industries: IT services, consulting, financial services, retail, marketing.
- Salary Range: ₹4,00,000 to ₹7,00,000 per year (entry-level); ₹7,00,000 to ₹15,00,000 per year (experienced).
11. ETL Developer (Extract, Transform, Load)
- Role: ETL Developers are responsible for creating systems that extract data from various sources, transform it into a suitable format, and load it into a data warehouse or Hadoop environment for analysis. They ensure data is cleaned and pre-processed before it can be analyzed.
- Industries: IT services, banking, retail, telecommunications, e-commerce.
- Salary Range: ₹5,00,000 to ₹9,00,000 per year (entry-level); ₹9,00,000 to ₹15,00,000 per year (experienced).
12. Hadoop Consultant
- Role: Hadoop Consultants provide expertise on implementing Hadoop frameworks and tools in business settings. They work with organizations to optimize their big data infrastructure and ensure that Hadoop environments are set up efficiently to handle large datasets.
- Industries: IT consulting firms, e-commerce, healthcare, and finance.
- Salary Range: ₹5,00,000 to ₹10,00,000 per year (entry-level); ₹10,00,000 to ₹20,00,000 per year (experienced).
Career Growth and Opportunities:
- Higher Demand for Big Data Professionals: As businesses generate more data and need to make data-driven decisions, the demand for professionals skilled in Hadoop and Big Data technologies continues to grow.
- Specialization: Graduates can specialize in areas like machine learning, artificial intelligence, or cloud-based data management, which are highly lucrative fields.
- Advanced Roles: With experience, professionals can move into senior roles like Big Data Architect, Data Science Lead, or even Chief Data Officer, which come with significantly higher salaries.
Factors Influencing Salaries:
- Experience: Professionals with hands-on experience in big data technologies like Hadoop typically earn higher salaries, particularly in leadership or specialized roles.
- Industry: Some industries, like finance, technology, and healthcare, tend to offer higher salaries due to the high demand for data expertise and the importance of data in these sectors.
- Skills and Tools: Knowledge of advanced tools (e.g., Apache Spark, Hive, HBase) alongside Hadoop can significantly boost a graduate’s salary potential.
- Location: Salaries for big data professionals in major cities like Bengaluru, Hyderabad, Mumbai, and Delhi tend to be higher due to the concentration of tech companies and startups.
Conclusion:
Graduates of the Certificate in Big Data Analytics (Hadoop) program can find rewarding opportunities across a variety of industries, particularly in data-driven organizations. The rapidly growing demand for professionals skilled in Big Data technologies ensures a strong job market with excellent salary potential. As the field evolves, those who stay updated with the latest tools and techniques can progress into higher-paying, specialized roles.