- Bill & Melinda Gates Foundation
- Bosch Automotive Aftermarket Diagnostics
- Cardinal Health
- Centre for Computational Research, University of Buffalo
- Milne Library, SUNY College
- US Bank
- 1 additional organization contributed information to assist with the development of this solution set. Due to the sensitivity of the information, this contributor requested confidentiality.
data architecture is different from traditional data for several key reasons,
- Big data architecture starts with the data itself, taking a bottom-up approach. Decisions about data influence decisions about components that use data.
- Big data introduces new data sources such as social media content and streaming data.
- The enterprise data warehouse (EDW) becomes a source for big data.
- Master data management (MDM) is used as an index to content in big data about the people, places, and things the organization cares about.
- The variety of big data and unstructured data requires a new type of persistence.
- Many data architects have no experience with big data and feel overwhelmed by the number of options available to them (including vendor options, storage options, etc.). They often have little to no comfort with new big data management technologies.
organizations do not architect for big data, there are a couple of main risks:
- The existing data architecture is unable to handle big data, which will eventually result in a failure that could compromise the entire data environment.
- Solutions will be selected in an ad hoc manner, which can cause incompatibility issues down the road.
- Before beginning to make technology decisions regarding the big data architecture, make sure a strategy is in place to document architecture principles and guidelines, the organization’s big data business pattern, and high-level functional and quality of service requirements.
- The big data business pattern can be used to determine what data sources should be used in your architecture, which will then dictate the data integration capabilities required. By documenting current technologies, and determining what technologies are required, you can uncover gaps to be addressed in an implementation plan.
- Once you have identified and filled technology gaps, perform an architectural walkthrough to pull decisions and gaps together and provide a fuller picture. After the architectural walkthrough, fill in any uncovered gaps. A proof-of-technology project can be started as soon as you have evaluation copies (or OSS) products and at least one person who understands the technology.
Impact and Result
- Save time and energy trying to fix incompatibilities between technology and data.
- Allow the Data Architect to respond to big data requests from the business more quickly.
- Provide the organization with valuable insights through the analytics and visualization technologies that are integrated with the other building blocks.
1. Recognize the importance of big data architecture
Big data is centered on the volume, variety, velocity, veracity, and value of data. Achieve a data architecture that can support big data.
2. Define architectural principles and guidelines while taking into consideration maturity
Understand the importance of a big data architecture strategy. Assess big data maturity to assist with creation of your architectural principles.
3. Build the big data architecture
Come to accurate big data architecture decisions.
4. Determine common services needs
What are common services?
This guided implementation is a four call advisory process.
Call #1 - Design a big data architecture strategy
Discuss with an Analyst the implications of your maturity on your principles and guidelines. Validate your pattern selection tool results and ensure there are no requirement gaps.
Call #2 - Build the big data architecture
Validate your Big Data Architecture Decision Making Tool results and receive guidance on next steps to understand the implications on your common services.
Call #3 - Determine common service needs
Receive guidance on your common services needs and advice on beginning your implementation plan.
Call #4 - Plan the big data architecture implementation
Grasp how initiatives can be grouped together and the dependencies between initiatives. Recognize metrics for determining the success of your architecture.
Book Your Workshop
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 roadmap in place to complete your project successfully.
Module 1: Recognize the Importance of Big Data Architecture
- 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.
Define the corporate strategy.
- Defined Corporate Strategy
Define big data and what it means to the organization.
- Defined Big Data
Understand why doing big data architecture is necessary.
Examine Info-Tech’s Big Data Reference Architecture.
- Reference Architecture
Module 2: Design a Big Data Architecture Strategy
- 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.
Discuss how maturity will influence architectural principles.
Determine which solution type is best suited to the organization.
- Architectural Principles & Guidelines
Define the business pattern driving big data.
- Big Data Business Pattern
Define high-level requirements.
- High-Level Functional and Quality of Service Requirements Exercise
Module 3: Build a Big Data Architecture
- 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
Establish required data sources.
- Data Sources Exercise
Determine data integration requirements.
- Data Integration Exercise
Learn which data persistence model is best suited.
- Data Persistence Decision Making Tool
Discuss analytics requirements.
Module 4: Plan a Big Data Architecture Implementation
- 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.
Identify common service needs.
Conduct an architectural walkthrough.
- Architectural Walkthrough
Group gaps together into initiatives.
Document initiatives on an initiative roadmap.
- Initiative Roadmap