Traditional warehousing has long been the backbone of enterprise data management assisting businesses make timely and informed decisions to improve their revenue streams, achieve cost savings and support their strategic objectives.
However, as data volumes continue to grow and the need for real-time and advanced analytics becomes more urgent, the limitations of traditional data warehousing become evident. Despite well-designed processes, ETL operations and data refreshes can become time-consuming and resource-intensive.
Scaling up server power and capacity may temporarily resolve data volume issues, but it often requires repeated upgrades in the future. Additionally, managing backups becomes increasingly complex. As the number of current and future data consumers grows, these issues can lead to high costs, frustrated users and reduced revenue.
Many organisations are also looking to uncover deeper value and insights from their vast amounts of data, beyond what standard reports can offer.
Some of the reasons why cloud databases outperform traditional data warehousing
- Columnar storage: Unlike traditional row-based storage, cloud databases often utilise columnar storage, which is designed for analytical workloads. This approach significantly reduces the amount of data read during queries, leading to faster query performance and reduced storage costs.
- Separation of storage and processing: Modern cloud databases decouple storage from compute, allowing organisations to scale resources independently based on demand. This flexibility ensures optimal performance without overprovisioning and minimises costs by only paying for what is used.
- Parallelism and elasticity: Cloud databases leverage parallelism, distributing data processing tasks across multiple nodes. This parallel processing accelerates query performance, especially for large datasets, and supports elastic scaling to handle variable workloads efficiently.
- Automatic tuning: Cloud platforms often include automated performance tuning features that adjust resources and optimise queries to maintain high performance. For example, Snowflake’s micro-partition pruning is a specific technique used to enhance query efficiency by automatically managing data partitions. This automated tuning ensures that performance remains high without manual intervention.
- Massively Parallel Processing (MPP): Cloud databases often employ Massively Parallel Processing (MPP) technologies. MPP systems distribute data processing tasks across many processors or nodes simultaneously, which enhances performance and scalability. This approach allows for efficient handling of large datasets and complex queries, reducing query times and increasing throughput. By using a distributed architecture and columnar storage, MPP technologies enable high-speed data processing and analysis, further boosting the performance benefits of cloud databases.
At Keyrus, we have accumulated vast practical experience with traditional and cloud data warehousing solutions, implemented at customers across industries. These use cases allow me to summarise the following tips for the cloud data warehouse cost management:
1. Use reserved instances and saving plans
For predictable workloads, consider using reserved instances or savings plans offered by cloud providers to lock in lower rates for a specific term. This can significantly reduce the cost of compute resources compared to on-demand prices.
2. Leverage auto-scaling and auto-pause features:
Use cloud provider auto-scaling and auto-pause capabilities to dynamically adjust resources based on demand. Microsoft Fabric and Snowflake, for example, offer options to scale compute resources automatically and pause idle resources to save costs.
3. Monitor and manage cloud spending
Utilise cost management tools provided by cloud providers (eg, Azure Cost Management, AWS Cost Explorer, Google Cloud Billing) to track, analyse and manage cloud expenses. These tools help you set budgets, monitor usage and receive alerts for cost anomalies or unexpected spending spikes.
Additionally, implement resource monitors (eg, Snowflake Resource Monitors) to control and manage compute costs, set spending thresholds and trigger alerts when limits are approached.
4. Monitor usage patterns and optimise
Regularly analyse usage patterns to identify optimisation opportunities and right-size compute resources based on workload requirements to avoid over-provisioning.
Leverage features such as caching, materialised views and proper data modelling techniques to reduce query times and storage needs.
Adjust the size of compute resources or schedule them to run only during specific periods when needed, reducing unnecessary costs.
5. Implement role-based access control
Implement role-based access control to enhance security, governance and cost management by enforcing access policies that control who can access specific resources, what actions they can perform and how they use these resources.
By effectively managing access, RBAC helps optimise resource usage and minimise unnecessary costs by preventing unauthorised or inefficient use of compute resources.
At Keyrus, we firmly believe that a data warehouse stands as the bedrock of proficient data management. It provides consolidated storage, seamless access and insights that can drive action. Whether you're looking to house your data in the cloud or on-premises, our team is well-equipped to assist you to:
- Transform the complexities of your data into tangible opportunities.
- Build an optimised architecture of your data warehouse.
- Help you harness the immense potential of your data.
- Enable data-driven decision-making and sustained business expansion.
Whether cloud or on-premises, we're your partner to unlocking the full potential of your data.
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