Description
Certification Name: Certificate in Data Engineering
Course Id: CDE/Q0001.
Eligibility: Graduation or Equivalent.
Objective: The Certificate in Data Engineering course is designed to equip professionals with the knowledge and skills to design, implement, and manage data pipelines for efficient extraction, transformation, and loading (ETL/ELT) of data. The course covers data modeling, integration, cleansing, workflow automation, and handling both structured and unstructured data across modern data platforms.
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 Foundations & Ecosystem: Introduction to data engineering and role of the data engineer, Data lifecycle: ingestion, storage, processing, delivery, Batch vs streaming vs real‑time data flows, Overview of ETL vs ELT processes and when to apply each, Modern data architecture: data lakes, data warehouses, lakehouses, Key tools and technologies: SQL, Python, Spark, data pipeline frameworks
Module 2 – Programming, Scripting & Data Manipulation: Programming fundamentals for data engineers (Python, Java/Scala) and version control, SQL for data engineering: complex queries, window functions, joins, performance tuning, Data extraction from multiple sources (APIs, flat files, databases) and formats (CSV, JSON, Avro, Parquet), Data transformation logic: cleansing, normalization, enrichment, mapping, Data loading techniques: bulk loads, incremental loads, upserts, scheduling
Module 3 – Data Storage & Warehousing: Relational databases: design, schema, indexing, normalization/denormalization, NoSQL databases: key‑value, document, columnar stores; when to use, Data warehouse concepts: star/snowflake schema, fact & dimension tables, OLAP vs OLTP systems, Data lake and lakehouse architectures and integration with warehouses, Storage formats & file systems: HDFS, S3, ADLS, Parquet/ORC, Data partitioning, indexing and performance considerations
Module 4 – ETL/ELT Pipeline Design & Orchestration: Designing efficient ETL or ELT pipelines: requirements gathering, mapping source to target, extraction strategies, transformation patterns, loading strategies, Workflow orchestration and scheduling tools (e.g., Apache Airflow, cron‑jobs, cloud pipeline services), Metadata management, logging, error handling, retry logic and monitoring, Change‑data capture (CDC), incremental extraction & delta loads, Best practices for scalability, fault‑tolerance and maintainability
Module 5 – Big Data, Streaming & Cloud Platforms: Big data processing frameworks (e.g., Apache Spark, Hadoop ecosystem) and streaming platforms (Apache Kafka, Flink), Real‑time ingestion vs micro‑batch, Cloud data services (AWS, Azure, GCP) for ETL/ELT: data lakes, data warehouses, serverless offerings, Containerisation and orchestration for data pipelines (Docker, Kubernetes), Data pipeline scalability, cost‑optimization in cloud environment, Hybrid / multi‑cloud data architectures
Module 6 – Data Quality, Governance, Security & Emerging Trends: Data quality dimensions, validation rules, profiling, cleansing and reconciliation, Data governance: metadata, lineage, cataloguing, master data management, Security, privacy and compliance for data engineering (encryption, access controls, GDPR, HIPAA), Monitoring, observability, logging and alerting for data pipelines, Performance tuning & optimisation of pipelines and storage, Emerging trends: data mesh, lakehouse architecture, serverless pipelines, AI/ML integration in data engineering
Job Opportunities in Certificate in Data Engineering
Professionals design, build, and maintain data pipelines, warehouses, and platforms, ensuring reliable, high-quality data for analytics, reporting, and AI across IT services, BFSI, e-commerce, SaaS, and analytics sectors.
Top Roles: Data Engineer (ETL/ELT), Big Data Engineer, Cloud Data Engineer, Data Warehouse Engineer, ETL Developer, Analytics Engineer, Data Platform Engineer, BI Data Engineer, Streaming Data Engineer, Data Engineering Architect
Key Skills: ETL/ELT, SQL, Python/Java/Scala, big data frameworks (Hadoop, Spark), cloud platforms (AWS, Azure, GCP), data modeling, data warehousing, streaming & real-time pipelines, BI integration, performance optimization
Salary Range (India):
- Entry Level: ₹4.5–9 LPA
- Mid Level: ₹8–20 LPA
- Senior Level: ₹14–50+ LPA
Industries: IT services & consulting, BFSI, e-commerce & retail, SaaS & product companies, telecom & media, analytics & AI firms
Scope: Career growth includes senior data engineering, platform architecture, real-time data systems, BI/analytics specialization, cloud data engineering, and progression to data engineering leadership or chief data officer roles.
