Scale
Mar 20, 2019 Business Intelligence on the cloud works well for fringe workloads, but as the size of data increases, analytics slows down and fails to meet the requirements of business users. Organizations often fall into this classic lift-and-shift trap where they think that just by relocating their existing BI platforms to the cloud, they would be able to reap all the benefits of the cloud. However, to maximize the transformational benefits of the cloud, you need to build an elastic environment for cloud-scale data, that can optimize resource utilization as well as deliver the performance that your business users need.
Problems of Scale and Speed with Increasing Business Workloads
An analytics team at a leading global travel company was tasked with the work of reducing cloud usage costs for analyzing their heavy workloads on AWS. With more than a thousand concurrent queries, they wanted to achieve query performance that met their SLAs of 3 seconds or less on their existing BI tools, Excel and Tableau. Soon, they realized that it was almost impossible to deliver consistently high performance on the scale of data that they were handling without significant architectural changes.
Technical architects, data scientists, and IT managers across industries face similar challenges when they migrate their business workloads to the cloud. Besides the size of their data, speed and resource consumption also depend upon the way BI is done on big data on the cloud. If they are using a large number of computing resources or heavy-lifting massive volumes of data to answer every business query, the overall costs can get prohibitive.
So how does one go about it?
Building a Semantic Layer on the Cloud
Even though both – your data and the business intelligence tools – are on the cloud, the interaction between them still presents a performance bottleneck. Kyvos resolves this problem by building a high-performing semantic layer that enables your existing BI tools to analyze data at scale on the cloud with high performance and instant response times.
Once the data lands on the cloud, Kyvos uses its revolutionary AI-powered smart aggregation technology to build multi-dimensional data models and stores them on the cloud platform itself. These models provide instant responses to all queries and allow users to query their data without limitations, do slice and dice, and drill into whichever area they want to explore.
The semantic layer that Kyvos builds takes care of identity management, security, and provides a consistent semantic model for business users. This makes it easier for them to navigate through massive volumes of data using a business intelligence tool of their choice. They can drag and drop dimensions and measures into their visualizations in a very intuitive way and explore their data limitlessly.
Elastic Architecture
Built natively for the cloud, Kyvos embeds cloud technologies in its architecture instead of following a patchy approach. It leverages the cloud for elasticity to optimize resource utilization, deal with peak loads, and deliver cost-effective BI, helping you take advantage of the very reason you moved to the cloud.
Kyvos is architected for the cloud and can quickly scale up and down without disruption. As you scale out for massive amounts of data, Kyvos also scales out transparently to build models on that data. As it builds data models, it spins up the number of machines that are required, and once models are built, those machines are released so that you do not have to pay for them anymore.
Similarly, while querying, you can scale your querying capability up or down depending upon the kind of load that you expect. This is achieved by increasing or decreasing the number of query engines that are used. Depending upon usage patterns, you can add querying power to handle peak loads and reduce the number of query engines when not in use. This optimizes resource utilization and enables cost savings as you pay only for the resources that you need.
The Performance Factor
Providing optimal performance on data at a massive scale is one of the biggest challenges of data analytics at a massive scale . It requires real optimizations in multiple ways so that you can get consistent performance regardless of the number of dimensions, attributes, or the size of data.
Kyvos helps you analyze large-scale data on the cloud with sub-second response times. The core capability of the platform is to enable massively scalable data modeling on the cloud. The multi-dimensional models that Kyvos builds can handle any size data.
As it does all heavy-lifting in advance, queries are light-weight and served immediately. Since the models are fully materialized, they deliver consistent performance even for the most complex queries. Besides this, in case you need to reduce response times even further, all you need to do is increase your querying capacity by adding query engines. Kyvos also offers the segmentation feature that helps you to dedicate resources for mission-critical functions and users.
Conclusion
An inflexible BI architecture that works the same way as on-premise slows down when the size of your data on the cloud increases, besides consuming too many resources. If you want to leverage the cloud for massive data, you need a modern architecture with built-in elasticity that required on the cloud. Kyvos helps you build an elastic, high-performing environment for enterprise-scale data.
If you want to learn more about Kyvos, request a demo now.
