Big Data Architect Certification Self-Study Kit Bundle

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1 Big Data Architect Certification Bundle This certification bundle provides you with the self-study materials you need to prepare for the exams required to complete the Big Data Architect Certification. The bundle includes the following self-study kits: BDSCP Module 1 : Fundamental Big Data (Exam B90.01) Foundational course that establishes a basic understanding of Big Data from business and technology perspectives, including common benefits, challenges and adoption issues. BDSCP Module 2 : Big Data Analysis & Technology Concepts (Exam B90.02) Explores contemporary analysis practices, technologies and tools for Big Data environments at a conceptual level, focusing on common analysis functions and features of Big Data solutions. BDSCP Module 10 : Fundamental Big Data Architecture (Exam B90.10) Coverage of the Hadoop stack, data pipelines and other technology architecture layers, mechanisms and components, and associated design patterns. BDSCP Module 11 : Advanced Big Data Architecture (Exam B90.11) Drill-down of Big Data solution environments, additional advanced design patterns and coverage of cloud implementations and various enterprise integration considerations. BDSCP Module 12 : Big Data Architecture Lab (Exam B90.12) A hands-on lab during in which a set of real-world exercises challenge participants to build and integrate Big Data solutions within IT enterprise and cloud-based environments. By purchasing these kits as part of this bundle you receive a 20% discount. Please note that this bundle requires the following textbooks: Business Case for Big Data, Big Data Analytics, Doing Data Science. These text books can be purchased separately or together with individual self-study kits or with the bundle when placing an order.

2 BDSCP Module 1: Fundamental Big Data This foundational course provides a high-level overview of essential Big Data topic areas. A basic understanding of Big Data from business and technology perspectives is provided, along with an overview of common benefits, challenges, and adoption issues. The course content is divided into a series of modular sections, each of which is accompanied by one or more hands-on exercises. Fundamental Terminology and Concepts A Brief History of Big Data Business Drivers that Have Led to Big Data Innovations Characteristics of Big Data Benefits of Adopting Big Data Challenges and Limitations of Big Data Basic Big Data Analytics Big Data and Traditional Business Intelligence and Data Warehouses Big Data Visualization Common Adoption Issues Planning for Big Data Initiatives New Roles Introduced by Big Data Projects Emerging Trends. Text Books This BDSCP course module covers a range of in-depth topics that are described in the course booklet(s) and further elaborated by detailed technical coverage and case study examples in the accompanying The Business Case for Big Data (Too Big to Ignore) text books. separately as part of the Module 1, which includes designed to prepare you for Exam B90.01 and are also suitable

3 BDSCP Module 2: Big Data Analysis & Technology Concepts This course explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The course content does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions, as well as a high-level understanding of the back-end components that enable these functions. The Big Data Analysis Lifecycle (from dataset identification to integration, analysis and visualization) Common Analysis and Analytics Techniques A/B testing, Regression, Correlation, Text Analytics Sentiment Analysis, Time Series Analysis Network Analysis, Spatial Analysis Automated Recommendation, Classification, Clustering Machine Language, Natural Language, Semantics Data Visualization and Visual Analysis Assessing Hierarchies, Part-to-Whole Relationships Plotting Connections and Relationships, Mapping Geo-Spatial Data Foundational Big Data Technology Mechanisms Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE) Big Data Processing (Parallel Data Processing, Distributed Data Processing, Shared- Everything/Nothing Architecture, SCV) Big Data & Cloud Computing. Text Books This BDSCP course module covers a range of in-depth topics that are described in the course booklet(s) and further elaborated by detailed technical coverage and case study examples in the accompanying The Business Case for Big Data (Too Big to Ignore) text books. separately as part of the Module 2, which includes designed to prepare you for Exam B90.02 and are also suitable

4 BDSCP Module 10: Fundamental Big Data Architecture This course provides an overview of the fundamental and essential topic areas pertaining to Big Data solution platform architecture, introducing several new Big Data mechanisms and covering a range of architectural models, approaches and considerations. Specifically, it covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. It further introduces the enterprise data warehouse and discusses various options for its integration with the Big Data solution platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used. Finally, the use of cloud environment for the development of the Big Data solution platform is explored in the context of cloud computing delivery and deployment models. New Big Data Mechanisms, including Security Engine, Cluster Manager, Data Governance Manager, Visualization Engine and Productivity Portal Data Processing Architectural Models, including Shared-Everything and Shared- Nothing Architectures Enterprise Data Warehouse and Big Data Integration Approaches, including Series, Parallel, Big Data Appliance and Data Virtualization Architectural Big Data Environments, including ETL, Analytics Engine and Application Enrichment Cloud Computing & Big Data Architectural Considerations, including how Cloud Delivery and Deployment Models can be used to host and process Big Data Solutions (and resulting issues and risks). separately as part of the Module 10, which includes designed to prepare you for Exam B90.10 and are also suitable

5 BDSCP Module 11: Advanced Big Data Architecture This course builds upon Module 10 by exploring advanced topics pertaining to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security. A number of design patterns and compound patterns are further discussed that are generally employed when developing a Big Data solution. These patterns range from data ingress to egress and data storage to data processing. Big Data Solution Architectural Layers including Data Sources, Data Ingress and Storage, Event Stream Processing and Complex Event Processing, Egress, Visualization and Utilization, Big Data Architecture and Security, Maintenance and Governance Big Data Solution Design Patterns, including patterns pertaining to Data Ingress, Data Wrangling, Data Storage, Data Processing, Data Analysis, Data Egress, Data Visualization and more Big Data Architectural Compound Patterns. separately as part of the Module 11, which includes designed to prepare you for Exam B90.11 and are also suitable

6 BDSCP Module 12: Big Data Architecture Lab This course module covers a series of exercises and problems designed to test the participant s ability to apply knowledge of topics covered previously in course modules 10 and 11. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data architecture practices as they are applied and combined to solve real-world problems. As a hands-on lab, this course incorporates a set of detailed exercises that require participants to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data architecture technologies, mechanisms and techniques can be applied to solve problems in Big Data environments. For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion. For individual completion of this course as part of the Module 12 Self-Study Kit, a number of supplements are provided to help participants carry out exercises with guidance and numerous resource references.. separately as part of the Module 12, which includes designed to prepare you for Exam B90.12 and are also suitable