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Complete the Big Data Architecture course and, optionally, get accredited as a Certified Big Data Architect by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Because this course encompasses both the Big Data Professional and Big Data Architect certifications, upon passing the exam you will also receive official Big Data Professional and Big Data Architect digital accreditation certificates and certification badges from Acclaim/Credly, along with an account that can be used to verify your certification status.

If you already completed the Big Data Professional course modules, you can purchase a partial course (or a partial bundle) with only the modules specific to the Big Data Architect track here.

The Big Data Architecture course is comprised of the following 5 course modules, each of which has an estimated completion time of 10 hours:

  • Module 1: Fundamental Big Data Science & Analytics
  • Module 2: Big Data Analysis & Technology Concepts
  • Module 14: Fundamental Big Data Architecture
  • Module 15: Advanced Big Data Architecture
  • Module 16: Big Data Architecture Lab

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.
  1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
  2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
  3. For your reference: A set of printable PDF documents that you can keep (for all course workbooks and posters).
All three forms of access are subject to Arcitura’s *. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

Shown below are the digital contents and the topic outline for each course module:

Module 1: Fundamental Big Data Science & Analytics

This foundational course module 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 module content is divided into a series of modular sections, each of which is accompanied by one or more hands-on exercises.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster
  • Symbol Legend Poster

  • Patterns and Mechanisms Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Understanding Big Data
  • Fundamental Big Data Terminology and Concepts
  • Big Data Business Drivers and Technology Drivers
  • Traditional Enterprise Technologies Related to Big Data
  • OLTP, OLAP, ETL and Data Warehouses in relation to Big Data
  • Characteristics of Data in Big Data Environments
  • Dataset Types in Big Data Environments
  • Structured, Unstructured and Semi-Structured Data

  • Metadata and Data Veracity
  • Fundamental Analysis and Analytics
  • Quantitative and Qualitative Analysis
  • Machine Learning Types
  • Descriptive and Diagnostic Analytics
  • Predictive and Prescriptive Analytics
  • Business Intelligence and Big Data
  • Data Visualization and Big Data
  • Big Data Adoption and Planning Considerations

Module 2: Big Data Analysis & Technology Concepts

This course module explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The module content intentionally 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.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Big Data Analysis Lifecycle (from Business Case Evaluation to Data Analysis and Visualization)
  • A/B Testing and Correlation
  • Regression and Heat Maps
  • Time Series Analysis
  • Network Analysis and Spatial Data Analysis
  • Classification and Clustering
  • Filtering, including Collaborative Filtering and Content-based Filtering
  • Sentiment Analysis and Text Analytics

  • Clusters and Processing Batch and Transactional Workloads
  • How Cloud Computing relates to Big Data
  • Foundational Big Data Technology Mechanisms
  • Big Data Storage Devices and Processing Engines
  • Resource Managers, Data Transfer Engines and Query Engines
  • Analytics Engines, Workflow Engines and Coordinate Engines

Module 14: Fundamental Big Data Architecture

This course module 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.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster
  • Patterns and Mechanisms Poster

  • Design Patterns Poster
  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics 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 Filebased Sources
  • Poly Storage, including Automatic Data Replication and Data Size Reduction

  • Random Access Storage, including High Volume Binary, Tabular, Linked, Hierarchical and Data Sharding
  • Streaming Access Storage, including Streaming Storage and Dataset Decomposition
  • 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

Module 15: Advanced Big Data Architecture

This course module builds upon Module 14 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.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster
  • Design Patterns Poster

  • Patterns and Mechanisms Poster
  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics 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

Module 16: Big Data Architecture Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous modules. Completing this lab will help highlight areas that require further attention and will help prove proficiency in big data architecture practices as they are applied and combined to solve real world problems.

Course Module Contents

  • Lab Exercise Booklet
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Exercise Booklet and Poster (printable)

Topics Covered

  • Reading Exercise 16.1: SFI Case Study Background
  • Lab Exercise 16.2: Design Big Data Pipeline for SLA Compliance
  • Lab Exercise 16.3: Alleviate Customer Dissatisfaction
  • Lab Exercise 16.4: Reduce Data Storage Cost
  • Reading Exercise 16.5: LOC Case Study Background
  • Lab Exercise 16.6: Solution for Intelligent Oil Exploration

  • Lab Exercise 16.7: Enhance Oil Well Production
  • Lab Exercise 16.8: Reduce Maintenance Costs and Achieve Regulatory Compliance
  • Reading Exercise 16.9: TXC Case Study Background
  • Lab Exercise 16.10: Identify Fraud and Eliminate Waste
  • Lab Exercise 16.11: Prioritize Resource Allocation and Enable Open Data Access

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About Arcitura

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About Arcitura Certifications

What’s in an Arcitura Course


Each course provides a comprehensive curriculum with 2-8 modules and 20-80 hours of training.

More Than Just
Video Lessons

In addition to standard video lessons, courses include full-color workbooks and reference posters for all lessons.

Interactive & Graded

Courses also include interactive and graded exercises, interactive and graded self-tests and other supplements.

The Arcitura Difference


  • is authored by a dedicated courseware development team
  • has a self-test, accreditation exam and professional certification
  • is available via two different eLearning platforms


  • undergo a common development process
  • are authored to be consistent in quality, structure and style
  • share a common vocabulary and symbol notation
  • are authored in collaboration with subject matter experts

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Regardless of whether you are an individual looking to boost your career or an organization looking to up-skill a team, Arcitura courses and certifications provide a sound investment.

Because both courses and accreditations are vendor-neutral, they empower you with skills and credentials that you can take to wherever you need to go.

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