How do you determine the right scale for your data warehouse needs?

Assess Your Current Data Landscape: Data Volume: Analyze the volume of data you're currently storing and ingesting. Consider both historical data and projected growth for the foreseeable future. This will give you a baseline for storage requirements. Data Velocity: Evaluate the rate at which your data is generated and needs to be processed. Do you have real-time data streams, or is your data more static with periodic updates? Understanding data velocity helps determine processing power needs. Data Variety: Consider the different types of data you're storing, including structured, semi-structured, and unstructured data. The variety of data can influence the complexity of your data warehouse schema and processing requirements.

There are multi dimensional aspects that you need to consider. - What is the domain of your business? - What is expected benefit from your data warehouse? - What is the data volume today? - What is the company's business vision and plans? - What is the projection for next 3 to 5 years? - What are the regulatory requirements? - Who are your target users?

Scaling a data warehouse involves assessing data volume, understanding business needs, analyzing query performance, and planning for retention. It's crucial to estimate user load, consider data types, evaluate ETL processes, and account for future expansion. Budget, compliance, technology selection, and continuous monitoring are also key. Engage stakeholders, forecast growth, optimize data models, and ensure modular design for flexibility. Monitor costs and ROI, and choose scalable technologies and supportive vendors. Always adapt based on feedback.

Below points need to be considered 1. Analyze data volume, variety, and velocity. 2. Define user interactions and query complexity. 3. Set performance expectations for response times and throughput. 4. Estimate future data growth based on business plans. 5. Consider budget constraints and cost-effective scaling options. 6. Choose between vertical or horizontal scaling based on needs. 7. Validate assumptions through pilot testing with sample data. 8. Align technical requirements with business objectives and budget. 9. Account for regulatory compliance and security considerations. 10. Continuously monitor and optimize scaling strategies as needed.

Determining the right scale for your data warehouse involves several steps. First, identify your business requirements. Understand the volume of data you'll be dealing with and the speed at which it will grow. Second, estimate the workload. This includes the number of queries, their complexity, and the expected response times. Third, consider data retention policies. How long does the data need to be stored? Finally, plan for future growth. It's important to choose a solution that can scale as your business grows. This approach ensures your data warehouse meets your needs effectively.

Design data models that can accommodate new data sources and evolving business requirements without significant rework. Conduct regular reviews of the data warehouse architecture and performance to identify areas for optimization and enhancement.

Cloud providers offer pay-as-you-go pricing models, allowing you to scale resources up or down based on demand and only pay for what you use.

Consider Cost Optimization: Cloud-based: Cloud providers offer pay-as-you-go pricing models. This allows you to scale resources up or down based on your needs, potentially leading to cost savings if your data volume fluctuates. However, be mindful of egress fees (data transfer fees out of the cloud) if you frequently move large datasets. On-premises: On-premises data warehouses require upfront investments in hardware and software licenses. While there are no ongoing pay-as-you-go fees, the total cost of ownership can be high due to maintenance needs and potential hardware upgrades.

Choose hardware or cloud instances with sufficient CPU capacity to meet these demands. Partition data based on key attributes such as time, geography, or customer segments to facilitate parallel processing and data retrieval.

Determine the number of users who will need access to the data warehouse and what level of access they require (read-only, read/write, etc.) Implement robust security measures to control access to sensitive data and ensure data privacy. This might involve role-based access controls, data encryption, and activity auditing.

Implement multi-factor authentication, single sign-on (SSO), and integration with identity providers to enhance security and streamline user authentication processes.

For access, first we need to establish some shared understanding regarding data ownership, data stewardship, etc. So, access can be segregated to reader and/or writer, this is importance to keep data validity, authority intact. enabling granularity of access. access can be on the level of projects, datasets, tables, columns or records level. If data access granularity not supported by the system, it’s better to have data redundancy with push approach, so the data security concerns verified by the owner of data. Letting the users having their own set of data redundancy may inevitable, but shouldn’t be a problem in modern big data systems.

Schedule routine database maintenance tasks to optimize performance and ensure data integrity. Strengthen security measures to protect sensitive data and prevent unauthorized access or breaches.

Maintenance plan e.g data housekeeping may consist data depreciation/retention policy. Some data may subject to be discarded automatically if not being accessed for some time(e.g 3 monthly) or there’s legal guidance that allows it. E.g financial data may need to be kept for at least 3 years. But as the data deletion can be automated, confirmation process from data owner still requires to keep system integrity. There’s case data owner have unaccounted system by rely on data in deprecating list. Maintenance also includes access maintenance, removing users thar actually not using their access, or rotating credentials for host to host automation systems.

With access maintenance it is a good practice to use OS groups. Provide groups the least access required, then move or remove users from the groups as their access requirements change. Setting roles and granting rights to groups makes access maintenance much easier.

Continuously Monitor and Adapt: Performance Monitoring: Regularly monitor your data warehouse performance metrics, including query execution times, resource utilization, and storage consumption. Identify bottlenecks and adjust your scaling strategy as needed. Data Profiling: Periodically profile your data to identify changes in data volume, velocity, or variety. This helps ensure your data warehouse architecture remains aligned with your evolving needs.

8. Source systems current and future You have to consider different source systems. In Telecom as example, the call details files are the most space consuming. You may need to integrate with other financial or technical systems. Get the forecast from commercial team and build on it . 9. Key performance indicator support is a success factor. Keep in mind that there are four main KPIs to consider. Human Resources performance Technical performance Financial performance And commercial performance .

Data ingestion and data processing performance In modern era, data ingestion in data warehousing doesn’t only happen as end of the day batch processing. In case of intraday day data processing, it’s more important to consider data ingestion performance for scalability of data warehouse. Data warehouse architecture should be capable enough to enable parallelism in data loading.