Description

Certification Name: Certified Data Engineer (ETL/ELT)

Global Occupational Skill Standard – GOSS ID: GOSS/CIT/CDE/V1

Duration: One Month

Eligibility: Graduation or Equivalent is required.

Objective: The Certified Data Engineer (ETL/ELT) 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.

Certification: Within 5 days after Completion of Online Assessment.

Get ready to join the Journey to become a GSDCI Certified Professional  – International Certification and Assessment Body.

Steps to become a GSDCI Certified Professional:

Step 1: Select your certification you want to pursue.

Step 2: Click on get certified tab, new pop up window will open.

Step 3: Click on pay certification fee, you will be redirected to billing details page.

Step 4: Fill your details and click on pay certification fee, you will be redirected to payment gateway, pay fee by any available options like Card (Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.

Step 5: You will get Login Credentials of Online E-Books and Online assessment link on your email id, within 48 hrs of payment.

Step 6: After completion of online assessment, you can download your Certificate Immediately.

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

GSDCI Online Assessment Detail:

• Duration- 60 minutes.
• Number of Questions- 30.
• Number of Questions from each module: 5.
• Language: English.
• Exam Type: Multiple Choice Questions.
• Maximum Marks- 100, Passing Marks- 50%.
• There is no negative marking in any module.

Benefits of Certification:

🌍 1. Global Recognition & Credibility – Stand out worldwide with a certification that opens doors across borders. Trusted by employers, respected by institutions, and recognized in over 100 countries.

📜 2. Quality Assurance through ISO Certification – Certified to global ISO standards, our programs deliver excellence, consistency, and a benchmarked learning experience that speaks for itself.

💼 3. Career Advancement & Employability – Enhances your resume and increases chances of promotions or job offers.

🤝 4. Non-Profit Trust Factor – Certifications from non-profit organizations are mission-driven rather than profit-driven.

📚 5. Access to Verified Learning & Resources – Often includes e-books, mock tests, and online support without hidden costs.

🔍 6. Transparency & Online Verification – Certifications come with a unique Enrolment ID for easy online verification by employers and institutions.

⏳ 7. Lifetime or Long-Term Validity – Certifications usually have lifetime validity or long-term recognition, reducing the need for frequent renewals.