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Choose the Right Tools for Big Data Development

Leverage Hadoop as your pilot project to gain organizational buy-in and build institutional learning.

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The lack of a defined and comprehensive approach to big data leads to:

  • Inability to address the skills gaps of database experts and data scientists
  • Inappropriately handling of single points of failure
  • Compliance and security risks due to the lack of security in many big data products

A well-defined big data tool stack provides the ability to:

  • Quickly handle large volumes of data with multiple schemas
  • Leverage the scalability of the database cluster to accommodate load spikes
  • Implement redundancies to ensure high availability and fault tolerance

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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: Assess fit for big data

The Purpose

  • Understand the current big data landscape.
  • Identify the project team.
  • Assess the current data analytics stack.

Key Benefits Achieved

  • Understand the organization’s readiness for big data development.

Activities: Outputs:
1.1 Document and assess the development process
  • Data analytics stack
1.2 Assess the data analytics stack
  • Development process flow
1.3 Address the gaps in the stack
  • Big data project team

Module 2: Draw the big data flow

The Purpose

  • Map the requirements to big data.
  • Draw the big data flow.

Key Benefits Achieved

  • Ensure business requirements are mapped to each component of the big data flow.

Activities: Outputs:
2.1 Document the business requirements and use cases
  • List business requirements
2.2 Draw the top-down and bottom-up big data flows
  • Big data flow target state

Module 3: Build the Hadoop stack

The Purpose

  • Choose the appropriate installation approach.
  • Import data into Hadoop.
  • Develop the MapReduce program.
  • Select big data analytics tools.
  • Conduct end-to-end testing.

Key Benefits Achieved

  • Create a baseline Hadoop stack that fits the organization’s needs.
  • Understand the challenges of installing and managing the Hadoop stack.

Activities: Outputs:
3.1 Select the installation approach
  • Complete pilot Hadoop stack fitted for the organization
3.2 Classify the imported data
  • Test and validate points in the Hadoop stack
3.3 Select the data collection tools
3.4 Design the relational schema
3.5 Test and validate the dataflow
3.6 Choose analytics tools
3.7 Perform end-to-end testing

Module 4: Roll out Hadoop in the organization

The Purpose

  • Prepare the Hadoop stack for deployment.
  • Gain institutional learning.
  • Create an organizational rollout plan

Key Benefits Achieved

Activities: Outputs:
4.1 Establish instrumentation points
  • Big data instrumentation points to measure value
4.2 Optimize the Hadoop stack
  • List of tools to improve performance of Hadoop
4.3 Develop an organization rollout plan
4.4 Establish a stakeholder communication plan
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