Certificate in Big Data & Hadoop

Description

Course Name: Certificate in Big Data & Hadoop
Course Id: CBDH/Q001.

Eligibility: Completion of 10+2 (higher Secondary) or equivalent.

Objective: A Certificate in Big Data & Hadoop is an excellent choice for those looking to build or advance their career in the Big Data field. The program offers valuable skills that are in high demand across a range of industries, and provides practical knowledge of how to use Hadoop and other Big Data tools to solve complex data processing problems.

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.

🌟 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 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.

Job Opportunities after completion of Certificate in Big Data & Hadoop course:

Graduates of the Certificate in Big Data & Hadoop program gain specialized skills in handling and processing large-scale datasets using Hadoop, an open-source framework that enables distributed storage and processing of big data. This certification provides in-depth knowledge of data analytics, data warehousing, and managing large databases, which are highly sought after in industries dealing with large volumes of data.

Career Options After Completion of the Certificate in Big Data & Hadoop Program:

1. Big Data Engineer

• Responsibilities: Big Data Engineers design, develop, and maintain systems that allow organizations to process large sets of data. They work with big data frameworks like Hadoop, Spark, and NoSQL databases to manage and optimize the data infrastructure.
• Key Skills: Hadoop ecosystem (HDFS, MapReduce, Hive, Pig), Spark, data pipelines, data storage solutions, cloud technologies (AWS, Azure, GCP).
• Industry: IT companies, financial services, e-commerce, healthcare, telecommunications.

2. Data Engineer

• Responsibilities: Data Engineers build and maintain data pipelines that gather, transform, and store data from multiple sources. They focus on making data available for analysis by structuring and organizing data efficiently.
• Key Skills: Data modeling, ETL processes, SQL/NoSQL databases, data warehousing, data integration tools.
• Industry: Tech companies, retail, finance, energy, and health tech companies.

3. Big Data Analyst

• Responsibilities: Big Data Analysts are responsible for analyzing large datasets to provide actionable insights. They use tools like Hadoop, Spark, and SQL to process and analyze data, helping businesses make data-driven decisions.
• Key Skills: Data analysis, statistical modeling, data visualization tools (Tableau, Power BI), Hadoop, machine learning algorithms.
• Industry: Marketing, finance, e-commerce, government, consultancy, and IT firms.

4. Data Scientist

• Responsibilities: Data Scientists use advanced analytics and machine learning techniques to interpret complex data. They apply statistical models, algorithms, and tools like Hadoop and Spark to analyze large datasets, create predictive models, and generate insights.
• Key Skills: Statistical analysis, Python/R programming, machine learning, data visualization, Hadoop, deep learning.
• Industry: Finance, healthcare, tech companies, marketing, and research firms.

5. Hadoop Developer

• Responsibilities: Hadoop Developers write the code necessary to manage, process, and analyze data stored in Hadoop clusters. They develop data processing applications using Hadoop components like MapReduce, Hive, and Pig.
• Key Skills: Hadoop, MapReduce, Hive, Pig, Java, Python, Scala, and data processing frameworks.
• Industry: IT companies, data analytics firms, telecoms, and research organizations.

6. Business Intelligence (BI) Developer

• Responsibilities: BI Developers design and develop systems that help businesses analyze and interpret data. They use big data tools to extract insights from large datasets and present them in a manner that is easy for business users to understand.
• Key Skills: BI tools (Tableau, Power BI), data warehousing, SQL, Hadoop, data integration, ETL tools.
• Industry: Finance, e-commerce, healthcare, telecommunications, and retail.

7. Cloud Data Engineer

• Responsibilities: Cloud Data Engineers work with cloud platforms (such as AWS, Azure, and Google Cloud) to build data storage, processing, and analytics systems. They ensure the cloud-based data infrastructure can handle big data applications and provide secure access to large datasets.
• Key Skills: Cloud platforms (AWS, Azure, GCP), Hadoop, Spark, cloud data warehouses, data pipelines, distributed computing.
• Industry: Cloud computing companies, financial services, tech firms, and e-commerce.

8. Data Architect

• Responsibilities: Data Architects design the data management systems that allow organizations to store, process, and analyze large datasets. They create blueprints for data infrastructure and ensure the data storage is secure, scalable, and easy to access.
• Key Skills: Data modeling, Hadoop, cloud technologies, SQL/NoSQL databases, ETL processes, data governance.
• Industry: Tech companies, financial services, healthcare, manufacturing, and energy.

9. Machine Learning Engineer

• Responsibilities: Machine Learning Engineers use big data tools and algorithms to develop systems that can automatically learn from and make predictions based on large datasets. They often work with big data technologies like Hadoop and Spark to train and deploy machine learning models.
• Key Skills: Machine learning algorithms, Python, Hadoop, Spark, neural networks, natural language processing.
• Industry: Tech firms, finance, healthcare, automotive, and e-commerce.

10. Data Visualization Specialist

• Responsibilities: Data Visualization Specialists are responsible for transforming complex datasets into visual stories. They create dashboards and data visualizations that help business leaders and stakeholders understand the trends and insights in big data.
• Key Skills: Data visualization tools (Tableau, Power BI), data analysis, Hadoop, Excel, and programming languages (Python, R).
• Industry: Consulting firms, finance, marketing, healthcare, and e-commerce.

11. Business Analyst (with Big Data expertise)

• Responsibilities: Business Analysts who specialize in Big Data analyze large datasets to uncover trends and insights that can help businesses make informed decisions. They use big data tools to work with massive datasets and report on findings that support business goals.
• Key Skills: Business analysis, Hadoop, SQL, data modeling, reporting tools, statistics.
• Industry: Finance, marketing, retail, healthcare, and consulting firms.

12. IT Consultant (Big Data & Hadoop)

• Responsibilities: IT consultants in the Big Data field advise businesses on how to integrate Hadoop and other big data tools into their existing infrastructure. They provide expertise in setting up scalable systems for processing and analyzing large datasets.
• Key Skills: Hadoop, cloud platforms, system integration, project management, consulting.
• Industry: IT consulting firms, software vendors, and large organizations.

13. ETL Developer

• Responsibilities: ETL (Extract, Transform, Load) Developers are responsible for designing and maintaining data extraction, transformation, and loading systems. They work with big data tools like Hadoop to move and process data between systems and databases.
• Key Skills: ETL tools, Hadoop, data warehousing, SQL, Python, Java.
• Industry: IT firms, data centers, consulting, and business intelligence companies.

14. Data Operations Manager

• Responsibilities: Data Operations Managers oversee teams that work with big data systems. They are responsible for managing the data processing and storage workflows, ensuring efficiency and optimizing data management systems.
• Key Skills: Big data technologies, Hadoop, team management, data governance, project management.
• Industry: Large enterprises, IT services, cloud service providers, and analytics firms.

Industries for Graduates of Big Data & Hadoop:

Graduates with expertise in Big Data and Hadoop can explore opportunities in a variety of industries:

• IT & Technology: Tech companies and startups that work with big data applications.
• Finance & Banking: Financial institutions that use big data for fraud detection, risk management, and customer insights.
• E-Commerce: Retailers and e-commerce platforms that use big data for personalized marketing, inventory management, and sales forecasting.
• Healthcare: Hospitals, health tech companies, and pharma companies using big data for medical research, patient care, and drug development.
• Telecommunications: Telecom companies leveraging big data for customer behavior analysis, network management, and service optimization.
• Government: Public sector institutions using big data for policy planning, public safety, and infrastructure management.
• Consulting & Analytics: Firms that offer data-driven insights to businesses in various sectors.

Salary Range:

The salary for graduates of the Certificate in Big Data & Hadoop program can vary depending on the role, experience, and location. Here is the approximate salary range in India:

• Entry-Level (0–2 years): ₹3 – 6 LPA (for roles like Big Data Analyst, Data Engineer, and Hadoop Developer)
• Mid-Level (2–5 years): ₹6 – 12 LPA (for roles like Data Scientist, Hadoop Developer, Business Intelligence Developer)
• Senior-Level (5+ years): ₹12 – 20 LPA (for roles like Big Data Architect, Machine Learning Engineer, Data Architect)

In countries like the US, salaries might range from:

• Entry-Level: $60,000 – $90,000
• Mid-Level: $90,000 – $130,000
• Senior-Level: $130,000 – $180,000

Conclusion:

Graduates of the Certificate in Big Data & Hadoop program have access to a wide array of career opportunities in industries that rely heavily on large-scale data processing and analysis. With the increasing demand for data professionals in today’s digital world, the expertise gained from this certification ensures that graduates can embark on rewarding careers with strong growth potential in the ever-expanding field of Big Data.