Certified Big Data Architect

The Big Data Architect track is comprised of BDSCP Modules 1, 2, 10, 11 and 12. The final course module consists of a series of lab exercises that require participants to apply their knowledge of the preceding courses in order to fulfill project requirements and solve real world problems. Completion of these courses as part of a virtual or on-site workshop results in each participant receiving an official digital Certificate of Completion, as well as a digital Training Badge from Acclaim/Credly.

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Description

WHAT YOU WILL LEARN

A Certified Big Data Architect has demonstrated proficiency in the design, implementation and integration of Big Data solutions within the IT enterprise or cloud-based environments. Depending on the exam format chosen, attaining the Big Data Architect Certification can require passing a single exam or multiple exams. Those who achieve this certification receive an official digital Certificate of Excellence, as well as a digital Certification Badge from Acclaim/Credly with an account that supports the online verification of certification status.

MODULE OVERVIEW

The Big Data Architect certification track is associated with the following courses and the courses can be delivered via instructor-led training.

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.
The following primary topics are covered:
– Understanding Big Data
– Fundamental Terminology & Concepts
– Big Data Business & Technology Drivers
– Traditional Enterprise & Technologies Related to Big Data
– Characteristics of Data in Big Data Environments
– Dataset Types in Big Data Environments
– Fundamental Analysis and Analytics
– Machine Learning Types
– Business Intelligence & Big Data
– Data Visualization & Big Data
– Big Data Adoption & Planning Considerations
Duration: 1 Day

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 following primary topics are covered:
– Big Data Analysis Lifecycle (from business case evaluation to data analysis and visualization)
– A/B Testing, Correlation
– Regression, Heat Maps
– Time Series Analysis
– Traditional Enterprise
– Network Analysis
– Spatial Data Analysis
– Classification, Clustering
– Filtering (including collaborative filtering & content-based filtering)
– Sentiment Analysis, Text Analytics
– Processing Workloads, Clusters
– Cloud Computing & Big Data
– Foundational Big Data Technology Mechanisms
Duration: 1 Day

BDSCP Module 10: Fundamental Big Data Architecture

This course provides an overview of essential topic areas pertaining to Big Data solution platform architecture, covering a range of architectural models, approaches and considerations. Big Data mechanisms are explained for the creation of Big Data solutions, as well as architectural options for assembling data processing platforms.
The course further introduces the enterprise data warehouse and discusses various options for its integration with Big Data environments. Common scenarios are also presented to provide a basic understanding of how Big Data solutions are generally utilized. Finally, the use of cloud environments for the Big Data solutions is explored in the context of cloud computing delivery and deployment models.
The following primary topics are covered:
– Security Engines, Cluster Managers and Data Governance Managers
– Visualization Engines and Productivity Portals
– Machine-Level Data Processing Architectural Models
– Shared-Everything and Shared-Nothing Architectures
– Big Data Analytics Logical Architecture
– Data Sources and Data Acquisition Layers
– Storage, Processing and Batch Layers
– Realtime Processing, including Event Stream and Complex Event Processing
– Enterprise Data Warehouse and Big Data Integration Approaches (including Series and Parallel)
– Poly Source, including Relational, Streaming and File-based Sources
– Poly Storage, including Automatic Data Replication and Data Size Reduction
– Random Access Storage, including High Volume Binary, Tabular, Linked, Hierarchical and Data Sharding
– Large-Scale Batch Processing, Complex Decomposition and Processing Abstraction
– Poly Sink, including Relational Sink, File-based Sink and Automated Dataset Execution
– Big Data Appliance and Data Virtualization
– Architectural Environments, including ETL
– Analytics Engines and Application Enrichment
– Cloud Computing and Big Data Architectural Considerations
– Cloud Delivery and Deployment Models for Hosting Big Data Solutions
Duration: 1 Day

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 those pertaining to storage, processing and security. Also covered are a number of design patterns and compound patterns generally employed when building enterprise Big Data solutions.
The following primary topics are covered:
– Enterprise Data Warehouses and Big Data
– Operational Data Store, Data Marts and Analytical Databases
– Big Data Solution Architectural Layers
– Big Data Architecture, Maintenance and Governance
– Big Data Security Architecture
– Series, Parallel, Appliance and Virtualization Approaches
– Big Data and Cloud-based Storage and Data Processing
– Canonical Data and Large-Scale Graph Processing
– Realtime Access Storage and Direct Data Access
– Analytical Sandbox and Confidential Data Storage
– Batch Data Processing and Dataset Denormalization
– Online Data Repository and Big Data Warehouse Architecture
– Operational Data Store and Indirect Data Access
– Integrated Access and Centralized Dataset Governance
– Event Stream Processing and Complex Event Processing
– Fan-in Ingress, Fan-out Ingress and High Velocity Realtime Processing
– Data Egress, Data Visualization and Data Utilization
– Data Wrangling, Data Processing and Data Analysis Processing
– Big Data Solution Design Patterns and Architectural Compound Patterns
– Lambda Architecture, Layers, Characteristics and Benefits
Duration: 1 Day

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 Study Kit, a number of supplements are provided to help participants carry out exercises with guidance and numerous resource references.
Duration: 1 Day

PREREQUISITES
  • There are no formal prerequisites for the certification exam
EXAM & CERTIFICATION

You can take exams anywhere in the world via Pearson VUE testing centers, Pearson VUE online proctoring and Arcitura on-site exam proctoring at your location.
You are provided with three flexible exam format options:

  • Complete Exam B90.BDA, a single combined exam for the entire Big Data Architect certification track. Recommended for those who want to only take a single exam that encompasses all course modules within this track.
  • Complete the partial version of Exam B90.BDA. Recommended for those who have already obtained a BDSCP certification and would like to achieve the Big Data Architect Certification without having to be retested on BDSCP Modules 1 and 2.
  • Complete one module-specific exam for each course module in Big Data Architect Certification track. This is recommended for those who want to progress gradually through the track and who would like to be assessed after each course module before proceeding to the next.

    CLASSROOM / INSTRUCTOR LED
    • High-impact learning with case studies
    • Delivered by certified instructors
    • Targeted learning for real projects

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