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.
image

Download Syllabus

 200/-
(Including all taxes)
Exam Request Enquiry Now

CDE is an intermediate to expert level certification that can be pursued by both working professionals and aspiring beginners.

image
image

Find Authorized Training Providers

Prerequisite Certification

  • Strong knowledge of databases, data modeling, and SQL.
  • Experience with big data technologies like Hadoop, Spark, and NoSQL databases.
  • Understanding of data warehousing concepts and ETL (Extract, Transform, Load) processes.
  • Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
  • Ability to design and implement scalable data pipelines and data integration solutions.
  • Knowledge of data governance, data quality, and data security principles.

Who can pursue this certification?

The Certified Data Engineer Certification is designed for professionals who work with big data and data engineering. Individuals who can pursue this certification include:

Data Engineers: Data engineers who design, build, maintain, and troubleshoot data processing systems and infrastructure can benefit from pursuing this certification. They work with tools and technologies such as Hadoop, Spark, and various data storage systems.

Data Architects: Data architects who are responsible for designing and managing data structures and architectures can also pursue this certification. They typically work on data modeling, database design, and data integration.

ETL Developers: Extract, Transform, and Load (ETL) developers who are involved in designing and implementing ETL processes to extract data from various sources, transform it, and load it into a data warehouse or another destination can benefit from this certification.

Big Data Professionals: Professionals who work extensively with big data technologies, such as Hadoop, Spark, and distributed computing frameworks, can pursue this certification to enhance their skills and validate their expertise.

Database Administrators: Database administrators who are responsible for managing and maintaining databases, ensuring data security and availability, and optimizing database performance can also pursue this certification to broaden their knowledge in data engineering.

Data Scientists: While the Certified Data Engineer Certification is more focused on the engineering aspects of data, data scientists who want to strengthen their understanding of data engineering principles and gain hands-on experience with data engineering tools can also pursue this certification.

 

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.

Specialization

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.

COURSE SYLLABUS

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

If you are Confused Get a Free Counselling

DATA SCIENCE CERTIFICATIONS

image

Data Science Foundation
image

Certified Data Science Developer
image

Certified Machine Learning Expert
image

Certified Data Scientist
image

Certified Data Scientist - Operations

image

Certified Data Scientist - Finance

image

Certified Data Scientist - HR

image

Certified Data Scientist - Marketing
image

Data Science Certified Manager
image

Certified MLOps Engineer