In the era of big data and cloud computing, online analytical processing (OLAP) has fallen out of favor. As modern data warehouses evolved to accommodate larger volumes of data and claims faster processing capabilities, OLAP fell out of fashion as a notion emerged that it would struggle to manage the combinatorial explosion. The focus shifted toward runtime querying engines. Instead of using predefined or pre-aggregated OLAP cubes, many business intelligence (BI) and analytics tools used their own data modeling applications that worked directly with raw data.
However, in recent times, there has been a realization that OLAP, when implemented intelligently, can be highly beneficial even in the context of a massive scale of data and cloud computing. Modern OLAP leverages advanced technologies and optimizations that overcome any previous limitations. Besides being cost-effective, especially for cloud implementations, it delivers real performance advantages.
Modern OLAP systems are designed to handle large datasets efficiently. One of the key strengths of OLAP lies in its ability to provide real-time or near-real-time insights, enabling businesses to make data-driven decisions with greater agility. The multidimensional data model of OLAP cubes facilitates instant query response, empowering users to derive valuable insights from complex data sets without compromising on query performance.
Consider These Advantages of Using OLAP in Modern Data Stacks
Performance: As data volumes continue to grow, OLAP remains a powerful technology to extract valuable insights and unleash the true potential of modern data stacks and cloud environments. OLAP’s multidimensional data model with pre-aggregation cubes enables efficient data processing and analysis, delivering faster insights and sub-second query responses. Modern algorithms further enhance performance, allowing organizations to handle large-scale data workloads without worrying about data explosion problems.
Unique OLAP Capabilities: Some domains, like finance, have an inherent need to preserve crucial OLAP capabilities even with the growing data storage and compute requirements for which they make a move to modern data stacks and cloud environments. Moving to the cloud enables data scalability, addressing the challenges of handling vast data volumes. However, they cannot afford to compromise on essential OLAP functionalities such as advanced hierarchies, like parent-child relationships, and alternate hierarchies with weighted rollups.
These capabilities are indispensable, especially in the world of financial analytics, regardless of how modern and advanced the new data stack may be. By leveraging OLAP in the cloud, organizations can continue to analyze and explore data with complex hierarchies efficiently, ensuring accurate and in-depth financial insights without sacrificing the flexibility and analytical power that OLAP provides.
Cloud Architecture and Cost Effectiveness: OLAP in cloud architecture presents a powerful and forward-looking solution that combines the benefits of OLAP technology with the scalability and cost-effectiveness of cloud computing. By harnessing the cloud’s resources, OLAP can efficiently process and analyze large volumes of data, thus meeting the growing demands of modern businesses.
Once OLAP cubes are constructed and populated, they provide a highly efficient and aggregated representation of the data. These pre-aggregated structures accelerate query performance, enabling teams to access insights in real-time or near-real-time. BI teams can seamlessly integrate existing tools with the OLAP model, fostering a cohesive and familiar analytics environment. It empowers decision-makers to explore data interactively, drill down into details, and gain valuable insights.
One of the primary advantages of OLAP in the cloud is its cost-effectiveness. OLAP technology, being primarily based on pre-aggregation, drastically reduces runtime querying compute cost, as answers to business queries are already pre-computed and stored in an aggregated structure. At querying time, OLAP tools have to look for right answers from the aggregated structure—not the cloud data storage—and return the query response, which results in much less compute cost as compared to runtime aggregation compute cost. It truly enables build once, query many times culture. Apart from this, businesses can leverage the cloud’s elasticity to scale computing and storage resources up or down as their data requirements fluctuate. This adaptability ensures companies can handle peak workloads without overprovisioning and reducing unnecessary expenses.
Universal Semantic Layer: A USL approach ensures consistent data definitions and analytics across the organization, reducing data discrepancies and improving data governance. A common understanding of the data enables better communication among teams and results in a smooth implementation of a data-driven decision-making process.
At the same time, using a USL means that multiple business intelligence tools and reporting platforms can connect to and leverage the same semantic model. Hence, various parts of the organization can continue to work with their familiar BI interfaces while accessing common underlying data.
OLAP lends itself naturally to delivering on a powerful universal semantic layer. The concept of dimensions, measures, hierarchies and calculations forms the foundation of an OLAP semantic model. OLAP semantic models also accommodate complex calculations, custom expressions or formulas required for complex business-specific logic. These components provide a powerful way to structure and organize data, making it more accessible and intuitive for business users who need to derive insights from the data.
If we consider typical sales data, product categories, geographic regions and time periods could be dimensioned. Measures, on the other hand, would be sales revenue, profit margins, quantities sold or any other KPIs. Hierarchies allow data to be analyzed at different levels of granularity. Users may roll up or drill down to view data at different time intervals such as year, quarter, month, day, etc.
USL and OLAP models are designed to shield business users from the underlying complex data modeling and schemas. They can work with filters, dimensions and measures that are business-focused representations of the data using familiar business terms while abstracting technical intricacies. They can view and explore data through intuitive hierarchies, filters and measures aligned with their business processes and KPIs.
Conclusion
OLAP in cloud architecture represents a forward-looking, fast and cost-effective solution. It capitalizes on the cloud’s scalability and pay-as-you-go model to process vast amounts of data efficiently, while its pre-aggregated structures and real-time capabilities empower business intelligence teams to gain valuable insights seamlessly. With its agility, OLAP in the cloud is well-positioned to meet the challenges of the future, providing organizations with a competitive edge with data-driven decision-making.
