Certified Data Engineer Certification (CDE-DS2150)

  • CDE is an intermediate to expert level certification that can be pursued by both working professionals and aspiring beginners.
  • One of the most respected data science certificates based on the EU EDISON framework.
  • The certification programs are aligned with Industry requirements.
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CDE is an intermediate to expert level certification that can be pursued by both working professionals and aspiring beginners.


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Certified Data Engineer

Test Perparation Study Guide

The Benefits

International Credential

IABAC® is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

Global Opportunities

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.


IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

Relevant and updated

IABAC® CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

Higher Salaries

On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

Summits & Webinars

In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.


Data Engineering Fundamentals

  • Introduction to data engineering
  • Role and responsibilities of a data engineer
  • Data engineering lifecycle and methodologies
  • Data governance and data management principles

Data Modeling and Database Systems

  • Relational database concepts and design
  •  Dimensional modeling and data warehousing
  • NoSQL databases and data modeling
  • Database optimization and performance tuning

Big Data Technologies

  • Introduction to big data technologies (e.g., Hadoop, Spark)
  • Hadoop ecosystem components (HDFS, MapReduce, Hive, Pig)
  • Apache Spark for large-scale data processing and analytics
  • Data streaming frameworks (e.g., Kafka, Apache Flink)

Data Integration and ETL Processes

  • Extract, Transform, Load (ETL) concepts and methodologies
  • Data ingestion techniques (batch processing, real-time streaming)
  • Data integration tools (e.g., Apache NiFi, Talend, Informatica)
  • ETL pipeline design and implementation best practices

Data Pipelines and Workflow Orchestration

  • Workflow orchestration tools (e.g., Apache Airflow, Luigi)
  • Designing and implementing data pipelines
  • Data pipeline monitoring and error handling
  • Scalability and fault tolerance considerations

Cloud Computing and Data Platforms

  • Introduction to cloud computing platforms (e.g., AWS, Azure, GCP) .
  • Cloud-based data storage and processing solutions 
  • Serverless computing and managed services
  • Data security and privacy in the cloud

Data Quality and Data Governance

  • Data quality assessment and improvement techniques
  • Data lineage and metadata management
  • Data cataloging and data governance frameworks
  • Compliance and regulatory considerations

Real-time Data Analytics and Machine Learning

  • Real-time data processing and analytics frameworks (e.g., Apache Kafka, Apache Flink)
  • Streaming data analytics and complex event processing
  • Introduction to machine learning and model deployment
  • Integrating machine learning pipelines into data workflows

Data Visualization and Reporting

  • Data visualization principles and best practices
  • Data visualization tools and libraries (e.g., Tableau, Power BI, Matplotlib)
  • Creating interactive dashboards and reports
  • Storytelling with data and communicating insights effectively

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