Create a Customized Big Data Architecture and Implementation Plan
Create a Customized Big Data Architecture and Implementation Plan
Big data architecture is not your father’s data architecture.
Onsite Workshop
Without a big data architecture, organizations risk:
The existing data architecture being unable to handle big data, eventually resulting in a failure that could compromise the entire data environment.
Solutions being picked in an ad hoc manner, causing incompatibility issues down the road.
Having a big data architecture results in:
Building blocks that work together with ease, preventing incompatibility issues.
The ability to respond quicker to big data requests from the business.
Module 1: Recognize the Importance of Big Data Architecture
The Purpose
Set expectations for the workshop.
Recognize
the importance of doing big data architecture when dealing with big data.
Key Benefits Achieved
Big data defined.
Understanding of why big data architecture is
necessary.
Activities:
Outputs:
1.1
Define the corporate strategy.
Defined Corporate Strategy
1.2
Define big data and what it means to the organization.
Defined Big Data
1.3
Understand why doing big data architecture is necessary.
1.4
Examine Info-Tech’s Big Data Reference Architecture.
Reference Architecture
Module 2: Design a Big Data Architecture Strategy
The Purpose
Identification of architectural principles and guidelines to assist with decisions.
Identification of big data business pattern to
choose required data sources.
Definition
of high-level functional and quality of service requirements to adhere
architecture to.
Key Benefits Achieved
Key Architectural Principles and Guidelines
defined.
Big data business pattern determined.
High-level
requirements documented.
Activities:
Outputs:
2.1
Discuss how maturity will influence architectural principles.
2.2
Determine which solution type is best suited to the organization.
Architectural Principles & Guidelines
2.3
Define the business pattern driving big data.
Big Data Business Pattern
2.4
Define high-level requirements.
High-Level Functional and Quality of Service Requirements Exercise
Module 3: Build a Big Data Architecture
The Purpose
Establishment of existing and required data
sources to uncover any gaps.
Identification of necessary data integration
requirements to uncover gaps.
Determination
of the best suited data persistence model to the organization’s needs.
Key Benefits Achieved
Defined gaps for Data Sources
Defined gaps for Data Integration capabilities
Optimal Data Persistence technology determined
Activities:
Outputs:
3.1
Establish required data sources.
Data Sources Exercise
3.2
Determine data integration requirements.
Data Integration Exercise
3.3
Learn which data persistence model is best suited.
Data Persistence Decision Making Tool
3.4
Discuss analytics requirements.
Module 4: Plan a Big Data Architecture Implementation
The Purpose
Identification of common service needs and how
they differ for big data.
Performance of an architectural walkthrough to
test decisions made.
Group
gaps to form initiatives to develop an Initiative Roadmap.
Key Benefits Achieved
Common service needs identified.
Architectural walkthrough completed.
Initiative
Roadmap completed.
Activities:
Outputs:
4.1
Identify common service needs.
4.2
Conduct an architectural walkthrough.
Architectural Walkthrough
4.3
Group gaps together into initiatives.
4.4
Document initiatives on an initiative roadmap.
Initiative Roadmap
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Onsite Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn’t enough, we offer low-cost onsite delivery of our Project Workshops. We take you through every phase of your project and ensure that you have a road map in place to complete your project successfully.
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