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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.

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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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