Create a Customized Big Data Architecture and Implementation Plan
Big data architecture is not your father’s data architecture.
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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.
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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.
Book NowModule 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. |
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| 1.2 | Define big data and what it means to the organization. |
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| 1.3 | Understand why doing big data architecture is necessary. |
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| 1.4 | Examine Info-Tech’s Big Data Reference Architecture. |
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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. |
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| 2.2 | Determine which solution type is best suited to the organization. |
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| 2.3 | Define the business pattern driving big data. |
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| 2.4 | Define high-level requirements. |
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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. |
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| 3.2 | Determine data integration requirements. |
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| 3.3 | Learn which data persistence model is best suited. |
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| 3.4 | Discuss analytics requirements. |
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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. |
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| 4.2 | Conduct an architectural walkthrough. |
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| 4.3 | Group gaps together into initiatives. |
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| 4.4 | Document initiatives on an initiative roadmap. |
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