Enterprise Big Data Engineer

The course objectives of the Enterprise Big Data Engineer course include an advanced understanding of the technical environment and components necessary to construct and maintain a Big Data environment. Moreover, an Enterprise Big Data Engineer is able to correctly structure data pipelines.

A certified Enterprise Big Data Engineer has proficiency in key technologies required to store and processes massive data sets. (S)he understands the technologies with which Big Data environments are structure and has a deep understanding of how data is structured.

The Learning objectives of this course include:

• Explain the role of big data technologies like Hadoop and Spark in extending foundational data engineering concepts to handle volume, velocity, and variety.
• Design, implement, and maintain scalable data infrastructure and platforms that support both structured and unstructured data processing needs.
• Evaluate the architectural principles, trade-offs, and applications of data warehouses, data lakes, and hybrid solutions to meet specific business requirements.
• Demonstrate proficiency in data processing and analysis using appropriate tools and paradigms for batch and real-time workloads.
• Compare and contrast distributed storage systems (e.g., HDFS, S3) and their implications for data durability, availability, and performance within a pipeline.
• Implement security measures, data governance policies, and compliance checks within a big data infrastructure to protect sensitive information.

• Create effective visualizations and reports to communicate insights and support data-driven decision-making for stakeholders.
• Apply machine learning techniques to solve practical problems and enhance data engineering workflows within big data contexts.
• Design and deploy data solutions on cloud platforms (e.g., AWS, Azure, GCP), leveraging managed services for optimal scalability and cost-effectiveness.
• Collaborate effectively on data initiatives by understanding the functions and competencies of various data-related roles within an enterprise organization.

The Enterprise Big Data Engineer (EBDE®) course is a structured, hands-on program designed to equip participants with the essential knowledge and practical skills needed to design, implement, and manage enterprise-grade data engineering solutions. Delivered through 30 hours of instructor-led training, the program is built around distinct modules that progressively build your expertise from the ground up.

Module 1: Foundations of Data Engineering

• Establish a solid understanding of core data engineering concepts and key technologies.
• Differentiate between structured and unstructured data and their use cases.
• Explore specialized storage and processing solutions designed for various data types.

Module 2: Relational and NoSQL Databases

• Master the principles of relational databases and achieve proficiency in SQL.
• Dive into NoSQL databases and learn techniques for handling unstructured data.
• Compare database paradigms to select the optimal solution for specific business requirements.

Module 3: Data Integration and Processing

• Develop hands-on expertise in building ETL (Extract, Transform, Load) workflows.
• Understand the principles of batch processing and real-time stream processing.
• Apply these techniques to enable seamless data integration and movement across systems.

Module 4: Designing Scalable Data Pipelines

• Learn to architect, build, and optimize efficient data pipelines.
• Explore proven strategies for ensuring high performance and scalability.
• Tackle complex, real-world data pipeline challenges through practical scenarios.

Module 5: Data Architectures and Use Cases

• Examine the components of modern data architectures.
• Evaluate the suitability of different architectures for specific business scenarios.
• Design tailored data solutions that directly support organizational objectives.

Module 6: Machine Learning for Data Engineers

• Discover how to integrate machine learning into data engineering workflows.
• Utilize ML algorithms to automate and enhance data processing and analytics.
• Solve practical, data-related problems through applied case studies.

Module 7: Data Security and Compliance

• Implement robust security measures to protect data throughout its lifecycle.
• Ensure compliance with critical data privacy regulations and industry standards.
• Adopt best practices for governing and safeguarding sensitive information.

Module 8: Roles and Collaboration in Data Engineering

• Identify the key functions, roles, and competencies required for success.
• Learn how to collaborate effectively with data scientists, analysts, and business stakeholders.
• Develop strategies to maximize the long-term value of enterprise data initiatives.

This structured approach ensures participants gain a deep, practical understanding of data engineering concepts and best practices, empowering them to solve real-world challenges and excel as Big Data Engineers.

This qualification is aimed at individuals who are new to the field of Big Data, as well as those who have some experience but want to gain a deeper understanding of the various technologies and tools used in Big Data engineering.

The target audience of the Enterprise Big Data Engineer qualification therefore includes the following roles:

• Data Engineers
• Database Administrators
• Data Architects
• ETL Developers

What you’ll get:

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

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 Engineer examination.

The Enterprise Big Data Engineer (EBDE®) exam is a comprehensive assessment designed to evaluate candidates’ knowledge and practical skills in data engineering. Key details of the exam include:

• Exam Format: Closed-book exam with 80 multiple-choice questions.
• Passing Criteria:
  • Standard pass mark: 65% (52 correct answers).
  • Trainer pass mark: 75% (60 correct answers).
• Duration: 120 minutes; candidates taking the exam in a non-native language receive an additional 25% time (total 150 minutes).
• Question Types: Includes classic questions, negatively worded questions, and select-evaluate tasks requiring candidates to choose the correct options from provided statements.
• Bloom’s Levels: Questions test understanding (Level 2) and application of concepts and skills (Level 3).
• No Negative Marking: Incorrect or unanswered questions receive no penalty.

Candidates should prepare using the Enterprise Big Data Engineer Guide to ensure they grasp key concepts and are ready to apply their knowledge in real-world scenarios.