Description
Certification Name: Certificate in Data Engineer
Course Id: CDE/Q0001.
Eligibility: Graduation or Equivalent.
Objective: The Certified Data Engineer course is designed to equip professionals with the technical expertise required to design, build, manage, and optimize scalable data pipelines and infrastructure for data collection, storage, processing, and analysis. The course covers the full data engineering lifecycle, including data modeling, ETL/ELT processes, data warehousing, and big data processing frameworks such as Apache Spark, Hadoop, and Kafka.
Duration: Three 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.
Assessment Modules:
Module 1: Data Engineering Fundamentals: Introduction to Data Engineering and Lifecycle, Data Types and Sources (Structured, Semi-structured, Unstructured), Data Formats (CSV, JSON, Parquet, Avro), Basics of Data Modeling and Schemas, Understanding ETL vs ELT Processes, Overview of Data Engineering Tools and Ecosystem.
Module 2: Relational and Non-Relational Databases: Designing Relational Databases (ER Models, Normalization), SQL for Data Manipulation and Query Optimization, Introduction to NoSQL Databases (Key-Value, Document, Column-Family, Graph), Hands-on with MongoDB and Cassandra, Indexing and Partitioning Strategies, Data Warehousing Concepts (Star/Snowflake Schema).
Module 3: Data Pipeline Development: Introduction to Batch and Stream Data Pipelines, ETL Development using Apache NiFi and Talend, Real-time Data Ingestion with Apache Kafka and Flume, Data Processing with Apache Spark and Spark SQL, Workflow Orchestration using Apache Airflow, Monitoring and Logging Data Pipelines.
Module 4: Big Data Technologies: Hadoop Ecosystem and HDFS Architecture, Data Processing using MapReduce and YARN, Apache Hive and Pig Basics, Introduction to Distributed Computing with Spark, Handling Large Datasets using Dask, Performance Tuning in Big Data Environments.
Module 5: Cloud Data Engineering: Cloud Platforms Overview (AWS, GCP, Azure), Cloud Storage (S3, Google Cloud Storage, Azure Blob), Managed Databases and Warehousing (Redshift, BigQuery, Snowflake), Building Data Pipelines using Cloud Services (AWS Glue, GCP Dataflow), Infrastructure as Code (Terraform, CloudFormation), Cloud Data Security and Compliance.
Module 6: Data Governance, Security & Real-world Projects: Data Quality and Lineage, Metadata Management Tools (Apache Atlas, DataHub), Data Security (Encryption, Access Control, Role Management), Compliance (GDPR, HIPAA), Capstone Project: Building Scalable Data Engineering Pipeline, Industry Use Cases and Best Practices in Data Engineering.
Job Opportunities After Certificate in Data Engineer
Careers in data engineering, data pipelines, cloud data platforms, big data, and data infrastructure across IT, BFSI, e-commerce, SaaS, analytics, and technology sectors.
Top Roles: Data Engineer, Big Data Engineer, ETL Developer, Cloud Data Engineer, Data Warehouse Engineer, Analytics Engineer, Data Platform Engineer, Freelance Data Engineer
Key Skills: SQL & NoSQL, Python, ETL/ELT, data pipelines, data warehousing, Hadoop/Spark/Kafka, cloud platforms (AWS/Azure/GCP), data integration, data quality, workflow automation
Earnings (India):
Entry / Mid-level: ₹6 – 15 LPA
Experienced / Senior: ₹12 – 25 LPA
Leadership: ₹25+ LPA
Freelance / Contract: ₹50,000 – ₹2,50,000+ per month, project-dependent
Industries: IT & software services, product & SaaS companies, BFSI, e-commerce & retail, cloud & data platforms, telecom, analytics firms, and AI/ML projects
Scope: Strong demand for professionals who can build reliable data pipelines and scalable data platforms. Career progression can lead from junior data engineering and ETL roles to Cloud Data Engineer, Big Data Engineer, Data Platform Lead, and senior data leadership positions, with additional opportunities in cloud projects, consulting, and contract work.
