Enterprise Big Data Architect

The Enterprise Big Data Architect Training equips participants with the advanced knowledge, strategic insights, and practical skills necessary to design and implement scalable big data solutions. Upon completion, participants will be able to:

• Explain the foundational principles of big data architecture, including key frameworks like lambda and kappa, and their use within modern enterprise settings.
• Describe the core technologies and tools used in big data systems, such as distributed file systems, data lakes, and processing frameworks including Apache Hadoop and Apache Spark.
• Design and implement scalable big data architectures that account for critical factors like data volume, velocity, variety, and veracity, while ensuring high performance and alignment with business needs.
• Understand and apply the NIST Big Data Reference Architecture (NBDRA) to structure and guide the design of big data systems, ensuring they meet industry standards for interoperability and scalability.
• Integrate cloud-based solutions from platforms like AWS, Azure, and Google Cloud into big data environments to achieve greater scalability, flexibility, and cost-effectiveness.
• Apply best practices in data governance, security, and compliance to ensure the protection of sensitive information and adherence to global regulatory standards within enterprise systems.
• Analyze and evaluate big data architectures against specific business requirements, delivering strategic recommendations for enhancing system performance and scalability.
• Collaborate with data engineers, scientists, and business leaders to ensure big data solutions are aligned with organizational goals, maximizing the value of data-driven initiatives.

These objectives provide learners with the comprehensive knowledge and skills required to excel as Enterprise Big Data Architects, empowering them to design future-ready big data solutions that drive business transformation and innovation.

The Enterprise Big Data Architect Training is designed to equip participants with the mastery and competencies required to design, implement, and optimize big data solutions within enterprise settings. The program is structured into four core modules, each addressing a vital facet of big data architecture. Delivered through 30 hours of instructor-led training, this course ensures participants acquire both the theoretical foundation and hands-on experience necessary to excel in architecting big data systems.

Module 1: Foundational Principles of Big Data Architecture (FPBDA)

• Big Data Fundamentals: Grasp the core concepts of big data, including the 4Vs (Volume, Velocity, Variety, and Veracity), and their impact on architectural design decisions.
• Architectural Frameworks: Examine foundational frameworks like lambda and kappa architectures, learning their applications, advantages, and constraints for managing both streaming and batch data.
• NIST Big Data Reference Architecture (NBDRA): Acquire knowledge of the NIST Big Data Reference Architecture, its core components, and its application as a model for building interoperable and scalable big data systems.
• Introduction to Key Technologies: Become acquainted with the fundamental tools and technologies prevalent in big data environments, including distributed file systems, data lakes, and processing frameworks such as Apache Hadoop and Spark.

Module 2: Designing Scalable Big Data Architectures (DSBDA)

• Data Pipeline Design: Master the design of efficient data pipelines to guarantee seamless data ingestion, processing, and flow, all while upholding scalability and high performance.
• Real-Time and Batch Processing: Understand the fundamentals of ETL, real-time stream processing, and batch processing to effectively integrate and analyze diverse data types.
• Cloud Integration: Learn how to incorporate cloud platforms (AWS, Azure, Google Cloud) into big data environments to create scalable and economically efficient architectural solutions.
• Big Data Storage Solutions: Delve into the design and optimization of storage solutions for both structured and unstructured data, utilizing technologies like HDFS and NoSQL databases.

Module 3: Data Governance, Security, and Compliance (DGSC)

• Data Governance: Learn to implement strong governance frameworks that ensure data quality, consistency, and availability across all enterprise systems.
• Security in Big Data Architectures: Comprehend industry best practices for securing big data environments, covering encryption, access control, and proactive monitoring mechanisms.
• Compliance Standards: Develop an understanding of regulatory mandates like GDPR, HIPAA, and CCPA, ensuring your big data solutions comply with global standards.
• Ethical Considerations: Investigate the ethical dimensions of big data, with a focus on privacy issues, bias mitigation, and the adoption of responsible data practices.

Module 4: Applied Big Data Architectures in Enterprise Contexts (ABDCE)

• Business-Driven Design: Learn to analyze and assess big data architectures based on specific business requirements and strategic objectives, ensuring alignment with organizational goals.
• Real-World Use Cases: Apply architectural principles to address real-world challenges in sectors such as finance, healthcare, and retail, including use cases like predictive analytics, fraud detection, and customer segmentation.
• Collaboration Across Roles: Cultivate the skills needed to collaborate effectively with data engineers, scientists, and business leaders to develop comprehensive, value-driven big data solutions.
• Emerging Trends: Stay informed on the latest advancements in big data, including edge computing, AI-driven architectures, and innovations in distributed systems.

This structured curriculum ensures participants gain a holistic understanding of the essential components of big data architecture, empowering them to design scalable, secure, and business-oriented solutions for enterprise environments. Each module is crafted to build actionable expertise, preparing participants to thrive as Enterprise Big Data Architects.

What you’ll get:

• E-Learning Video Covering Key Concepts and Best Practices
• Official Enterprise Big Data Architect E-Book
• Course Syllabus
• Sample Paper with Mock Questions
• Rationale with Detailed Explanations
• Official APMG International Enterprise Big Data Architect Exam Voucher (Redeemable for scheduling your certification exam)

The target audience for the Enterprise Big Data Architect (EBDAR®) qualification includes a wide range of professionals seeking to master the design, implementation, and optimization of big data architectures in enterprise environments. This includes:

Big Data Architects

Data Engineers

IT Architects

Data Governance and Security Professionals

Cloud Architects

Business Analysts and Decision-Makers:

The course will provide an overview of existing technologies but will not go into programming or implementation. There is no mandatory prerequisite to take the Enterprise Big Data Professional examination.

The Enterprise Big Data Architect (EBDAR®) exam is a comprehensive evaluation of candidates’ understanding of advanced data architecture concepts and their ability to apply these concepts in real-world scenarios. Key details of the exam include:

• Exam Format: Open-book exam with 80 multiple-choice questions, divided into 4 segments.
• Passing Criteria:
  • Standard pass mark: 65% (52 correct answers).
  • Trainer pass mark: 75% (60 correct answers).
• Duration: 150 minutes; candidates taking the exam in a non-native language receive an additional 30 minutes (total 180 minutes).
• Question Types: The exam includes classic questions, negatively worded questions, and select-evaluate tasks that require candidates to assess and select the best options from provided statements.
• Bloom’s Levels: The questions test higher levels of thinking, including analysis (Level 3), synthesis (Level 4), and evaluation (Level 5).
• No Negative Marking: Incorrect or unanswered questions do not incur penalties.

Candidates are encouraged to prepare using the Enterprise Big Data Architect Guide to ensure a solid understanding of the theoretical and practical aspects of data science. This preparation will help candidates effectively apply their knowledge to solve complex business problems and excel in the exam