Deploying a data warehouse is a significant undertaking for any organisation. It involves strategic planning, architectural decisions, operationalisation, and most importantly, continuous management to ensure the data warehouse effectively meets business needs.
What is a Data Warehouse?
Customer Transactions
Sales Records
Marketing Data
7 Considerations for Data Warehouse Deployment
1. Cloud Migration and Strategy
One of the first considerations for deploying a data warehouse is deciding on the cloud migration strategy.
Organisations must determine if a hybrid or multi-cloud approach suits their needs.
Successful data and analytics (D&A) cloud migrations tend to be iterative, starting small to deliver a core set of cloud-based services and expanding over time toward a broader set of use cases. This iterative approach, which emphasises adaptability and flexibility, helps manage risks and allows for continuous learning and improvement.
Organisations should minimise basic lift-and-shift migrations that take only part advantage of cloud attributes.
Instead, they should assess individual D&A components to determine the impact on the deployment approach and build a cost versus effort rationalisation matrix to find the best strategy for replacing, rebuilding, or rehosting individual capabilities. This strategic planning ensures that the migration achieves the best performance and cost-efficiency.
2. Architecture and Operationalisation
- From the beginning, operationalising data workloads with DevOps and DataOps practices can increase productivity and minimise operational effort
- Minimising that effort involves incorporating a configuration-driven process using containerisation for various environments, which helps
- automate single-click deployments and
- minimises discrepancies across component versions within the technology stack
Additionally, organisations should consider:
- Modern D&A Architecture
- Cloud Technologies, and
- Operational best practices to build and run scalable analytics solutions
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Data Integration
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Knowledge Graphs
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Semantics
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Machine Learning to augment data integration design and delivery
3. Data Governance and Management
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ensuring data quality,
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addressing data privacy and security concerns and
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managing data effectively
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supporting integration,
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interoperability and
- automation within and between
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applications,
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data catalogues,
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quality and
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privacy
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4. Cross-Functional Teams and Skills
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Application Leaders
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D&A Leaders
- Security Leaders
5. Technological Considerations
Choosing the right mix of technologies is vital for data warehouse deployments. Modern D&A architecture, cloud technologies, and operational best practices can empower organisations to build and run scalable analytics solutions in new ways. Organisations should leverage data fabrics, simplifying data integration infrastructure and creating scalable architectures.
Organisations should embrace new cloud capabilities and styles of operation that position infrastructure and operations (I&O) as catalysts for continuous improvement. Creating a cloud centre of excellence, a team or function that provides:
- leadership,
- best practices,
- research,
- support, and
- training
for cloud adoption, can help refine cloud management and performance by formulating best practices across workload selection, governance, operations, and organisational skills.
6. Scalability and Flexibility
Managing vast amounts of data and ensuring the data warehouse can scale with the organisation's needs is a significant challenge. Flexible infrastructure is necessary for smooth data management. Organisations should explore innovative cloud-based analytics capabilities and build composable architectures to simplify decision-making.
Expanding advanced analytics to production in the cloud through modular addition rather than a lift-and-shift approach can help achieve faster, more cohesive delivery of advanced analytics capabilities. This approach reduces the operational effort in the cloud to compose analytics and data science capabilities together in a complementary fashion.
7. Cost Management
Balancing the cost of deploying and operating a data warehouse with the benefits it provides is crucial. Organisations need to evaluate the cost versus performance of different solutions. Building a cost versus effort rationalisation matrix to find the best approach for replacing, rebuilding, or rehosting individual capabilities can help manage costs effectively.
Organisations should also consider price over performance when evaluating vendor IaaS and PaaS solutions with D&A. It is essential to select a strategic primary cloud IaaS provider that meets the requirements of most D&A workloads and ensures it fits well with the organisation's skill set and use cases.
