Big Data Professional (BDP)
Big Data Professional (BDP) is a certification designed to introduce professionals to the world of Big Data—one of the most transformative technologies of the digital era and Industry 4.0. Based on the 7Vs (Volume, Velocity, Variety, Veracity, Value, Visualization, and Variability), this course explores how to work with large volumes of data from multiple sources, covering key concepts, historical evolution, tools, frameworks, real-world applications, and analytical methodologies.
ADDRESSED TO
This course is intended for anyone interested in acquiring or expanding their knowledge of Big Data and its practical applications in business and technology environments. It is ideal for professionals seeking to understand how Big Data addresses needs that traditional technologies cannot fulfill, especially in the areas of data storage, analysis, and large-scale management.
PURPOSE
The Big Data Professional certification aims to:
- Understand the fundamentals and market impact of Big Data.
- Distinguish between Big Data and traditional data.
- Learn the 7Vs of Big Data and their practical significance.
- Become familiar with major data frameworks and technologies.
- Explore Big Data architectures and ecosystems.
- Connect real-world use cases and trends with Big Data projects.
MAIN TOPICS
The course is organized into key thematic areas essential for Big Data proficiency:
Foundational Concepts
- What is Big Data and massive data sets.
- The 7Vs of Big Data.
- Introduction to MapReduce.
Data, Information, and Knowledge
- Differences between data, information, and knowledge.
- Databases and data models.
- Data processing and distributed clusters.
- Role of Artificial Intelligence in knowledge generation.
Evolution of Big Data
- Historical phases of Big Data.
- Key milestones and current trends.
Data Governance
- Definition and key activities.
- Benefits and roles in data governance.
Big Data Applications
- Big Data in Business Intelligence (BI) projects.
- Data warehouses and the evolution of BI toward analytics.
- Analytical methodologies like CRISP-DM and its comparison to SEMMA.
Data Analysis
- Types of analysis: supervised and unsupervised learning.
- Key skills for data analytics.
Big Data Technologies
- Architectures: NIST reference, Microsoft, MapReduce, NoSQL.
- Popular frameworks (Apache and others).
- Cloud computing as an enabling platform.
Details
Duration:
Number of questions:
Minimum passing:
Pre Requirements:
No
Available languages:
Third chance (free):
Take your exam online.
$150.00
