Certified Big Data and Data Analytics Practitioner (CBDDAP)

This course provides a practical and structured introduction to big data and data analytics, focusing on how organizations can design, implement, and govern scalable data and analytics environments. Participants will gain a clear understanding of distributed data storage, modern processing engines, cloud and managed analytics platforms, and analytics architectures, with an emphasis on real-world application rather than deep technical programming. The course combines conceptual explanations with practical exercises to demonstrate how big data platforms support analytics, insight generation, and data-driven decision making. Key themes include platform selection, architectural design, performance considerations, and governance and ethics. This practical course is designed to equip professionals with the knowledge and confidence to contribute effectively to big data and data analytics initiatives, engage constructively with technical teams, and align data and analytics solutions with organizational objectives. PrerequisitesNone — open to managers at all levels

5 Days

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Course Methodology

This course uses facilitator-led interactive sessions with practical business explanations, complemented by group discussions that share experiences and address real organizational challenges. Effective learning is reinforced through real-world big data and analytics case studies, scenario-based problem solving, and regular knowledge checks to strengthen understanding and consolidate key concepts.

Course Objectives

By the end of the course, participants will be able to:

Explain core big data and data analytics concepts, enabling informed participation in data-driven discussions and initiatives
Identify appropriate big data platforms and architectures, supporting effective technology selection aligned with business requirements
Apply distributed data storage and processing principles, improving the ability to design scalable and resilient data solutions
Evaluate cloud-based big data solutions and deployment options, supporting cost-effective and scalable implementation decisions
Design high-level big data and analytics architectures, enabling alignment between technical solutions and organizational objectives

Target Audience

This course is designed for data analysts, business analysts, data engineers, analytics engineers, business intelligence professionals, solution architects, and technology managers who are involved in data-driven decision making and analytics initiatives.

It is also suitable for IT managers, digital transformation leads, cloud and data platform specialists, and project managers responsible for implementing or overseeing big data, analytics, and cloud data platforms. Furthermore, the course supports professionals in transition toward data and analytics roles, including consultants and technical professionals supporting enterprise data, analytics, and Artificial Intelligence initiatives.

Target Competencies

Big data architecture
Distributed data processing
Cloud-based data platform design
Data-driven decision making
Analytical thinking

Course Outline

Big Data Fundamentals and the Modern Data Ecosystem
- Big data characteristics and the Five V Framework
- Evolution from traditional databases to big data platforms
- Business drivers for big data and analytics
- Overview of the big data technology landscape
- Data analytics lifecycle
- Roles in big data and analytics initiatives
- Open source and managed analytics platforms
Distributed data storage and cloud data lakes
- Principles of Distributed Data Storage (DDS)
- Hadoop distributed file system as a historical foundation
- Transition from on-premise Hadoop to cloud data lakes
- Cloud object storage concepts and architectures
  - Azure Data Lake storage
  - Amazon S3 storage
  - Google Cloud storage
Apache Spark and modern distributed processing
- Distributed Data Processing and Parallel Execution
- Apache Spark architecture and execution model
- Resilient distributed datasets as a processing foundation
- Limitations of low-level processing models
- Spark DataFrames for structured data processing
- Spark structured query language for analytics
- Batch and interactive processing patterns
Cloud Analytics Platforms and Lakehouse Architectures
- Cloud-native analytics platform concepts
- Managed Spark environments
- Databricks and the Lakehouse architecture
- Separation of storage and compute
- Snowflake as a cloud data warehouse
- Integration between data lakes, Spark, and data warehouses
- Platform selection and architectural trade-offs
Analytics Architecture, Governance, and Operationalization
- Analytics use case identification
- End-to-end analytics architecture design
- Data pipelines and orchestration concepts
- Batch and streaming analytics patterns
- Monitoring and performance management
- Data governance and data quality management
- Ethics, privacy, and regulatory considerations

2026 Schedule & Fees

Course Fees Starting From

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