Fundamental Big Data Architecture
Duration
2 Days
Exam Details
- Exam Code: B90.10: Fundamental Big Data Architecture
- Duration: 60 minutes
- A passing grade on Exam B90.10 is a requirement for the following certification(s):
– Certified Big Data Architect - Availability: Pearson VUE Testing Centers Worldwide, Pearson VUE Online Proctoring, On-Site Proctoring
Price
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.
Key Outcomes
Students will be able to know:
- Big Data Pipeline Compund Pattern
- Big Data Mechanisms
- Machine Level Data Processing Architectures
- Big Data Architecture Types
- Big Data Analytics Logical Architecture
Objectives
- 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
Contact
- Nurman (+62 857-2375-3840)
- Irna (+62 822-1664-7749)
- Puji (+62 813-2424-2115)