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.
Book NowModule 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 |
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| 1.2 | Assess the data analytics stack |
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| 1.3 | Address the gaps in the stack |
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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 |
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| 2.2 | Draw the top-down and bottom-up big data flows |
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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 |
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| 3.2 | Classify the imported data |
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| 3.3 | Select the data collection tools |
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| 3.4 | Design the relational schema |
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| 3.5 | Test and validate the dataflow |
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| 3.6 | Choose analytics tools |
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| 3.7 | Perform end-to-end testing |
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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 |
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| 4.2 | Optimize the Hadoop stack |
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| 4.3 | Develop an organization rollout plan |
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| 4.4 | Establish a stakeholder communication plan |
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