Online analytical processing (OLAP) provides organizations with the capability to perform multidimensional analysis quickly, exploring all their data from diverse perspectives. This dynamic, interactive approach to analytics leads to better insights and decision-making.
Running OLAP on-prem, however, can be expensive, requiring a big-ticket initial capital expenditure. Everyday operational expenses, maintenance and support staffing needs coupled with growing data volumes can be overwhelming. Often, these scaling-up costs prove to be an investment burden that becomes difficult to justify. Under or over-provisioning of storage and computing resources can make it highly inefficient to run.
Cloud-based OLAP solutions offer a cost-effective alternative. The pay-as-you-go model eliminates large upfront investments, providing a system that is flexible to scale on demand with a highly optimized resource utilization. Additionally, cloud providers take care of system maintenance, security and upgrades — freeing up in-house IT resources for other strategic tasks.
Let’s look at multiple ways that OLAP on the cloud impacts cost efficiency:
Pre-Aggregation
Pre-aggregation is a powerful technique used with OLAP, wherein frequently used dimensions are pre-calculated, viz. commonly used summaries such as sum, average, count, etc. For example, in a sales table, monthly sales by product category may be aggregated in advance. Readily available for immediate use, this pre-aggregated data significantly cuts down processing time. It reduces the need for running similar queries, over multiple times, consuming lesser computing resources.
In a cloud environment, pre-aggregation has a sizable impact on cost as cloud charges are based on the compute resources utilized. Elimination of multiple, real-time aggregation on raw data cuts down on the processing power needed, thus lowering costs. Moreover, aggregated tables are smaller than raw data, thus less data needs to be stored and moved, which again reduces cloud service bills.
Cloud Model Efficiencies
An on-prem OLAP solution typically requires a high initial capital investment in hardware, software licenses and IT staff. On the other hand, deployments on a cloud platform are billed only for the actual resources utilized, eliminating the upfront expenditure. It is also more efficient and eliminates scenarios with underutilized infrastructure.
In addition, cloud-based solutions ensure scalability which makes the data architecture highly elastic and adept at handling fluctuating analytical workloads. Whether facing a sudden surge in data processing demands or the need for additional storage, these systems can scale seamlessly to meet these requirements with ease. They ensure consistent performance, offering an economically attractive and operationally efficient choice.
Optimized Storage
OLAP architecture finds great synergy with a cloud provider’s tiered approach to storage that caters to different needs and results in reduced costs of running analytics.
Cloud storage options provide far more than just a place to park your data; they offer a tactical advantage in reducing the cost of OLAP analysis. By leveraging the appropriate storage class, organizations can significantly lower their expenses while maintaining efficient data access and analysis capabilities.
Frequently accessed data may be stored in high-performance storage tiers that offer quick retrieval for real-time analysis. Lower cost, and slower access tiers can be used for historical and archival data. Moreover, data can be automatically transitioned between storage tiers based on the predefined data life-cycle management policies.
A tiered approach to data management optimizes storage costs, without compromising data access speed. This makes cloud-based OLAP an optimal architecture from a total cost of ownership (TCO) perspective.
Universal Semantic Layer (USL)
Using the transformative advantages of a universal semantic layer (USL), OLAP on the cloud helps redefine analytics. It creates coherencies by consolidating diverse sources of data and reducing complexities that come with intricate hierarchies. The layer offers a consistent, unified and standardized view of data across the organization, becoming a single source of truth. This centralized approach not only enhances the reliability of data insights but also simplifies the analytical workflow, making it easier for users to access and interpret data.
Organizations can create a common data dictionary and framework for their analytical processes with a central semantic layer on the cloud. This results in a shared understanding and reduces disparate interpretations. Enhanced user experiences ensue, democratizing data access and encouraging wider adoption of analytical insights.
A cloud-based OLAP architecture is highly scalable, more cost effective and much easier to deploy. Moreover, it is bundled with robust data security, governance and compliance features — integrally available with cloud providers.
OLAP Deployment on the Cloud is a Good Choice
Cloud-based OLAP solutions offer a transformative approach to data analytics, significantly reducing overall costs and enhancing capabilities. It is an economically attractive and operationally efficient alternative to traditional on-premises systems.
With benefits, such as reduced upfront costs, improved resource utilization, scalable infrastructure, pre-aggregation efficiencies and centralized, optimized storage with USL, OLAP deployment on the cloud is a much better architectural choice.
