Real-time analytics challenges often fall into three buckets: speed, volume, and data freshness. These challenges, however, are often solved the same way: incurring significant expenses. This article will explore the cost drivers in real-time analytics and how innovative solutions can make it more affordable and efficient.
The Impact of Denormalization on Pipelines and Costs
Besides the obvious costs of moving to real-time analytics such as training your employees or incorporating new technologies, the hidden costs that might shock you are in the design of your system, specifically around data pipelines and denormalization.
An integral part of data analytics, multi-table JOINs, are nevertheless resource-hungry operations, primarily due to their computational complexity. This is especially true in real-time OLAP databases, where most traditional systems either do not support complex JOINs or perform poorly when executing them. To address these challenges Data practitioners often resort to denormalization, a process where tables are pre-joined into a larger unified table during the preprocessing phase.
The Hidden Cost of Denormalization
While this workaround can boost query speed, it introduces additional operational overhead in constructing and maintaining these denormalized data structures, leading to inflated costs and added complexity in data analytics.
Moreover, once a denormalization pipeline is established, its inherent complexity and rigidity resist changes. Any modification, often necessary in a dynamic business environment, could trigger extensive and costly reengineering efforts and data backfill. This inflexibility hinders the data pipeline's responsiveness to evolving needs, delaying actionable insights and slowing down decision making, all while escalating operational costs.
Pioneering On-the-Fly JOIN: Lessons From Airbnb
While the problems above seem significant, innovative solutions are already making an impact in the industry. A notable example is Airbnb's advanced metrics management platform, Minerva. Previously, Minverva was bound by the performance constraints of existing tools: with a intricate denormalization pipeline, schema changes were extremely time-consuming and costly, adding new metrics took hours or even days depending on the amount of data that needed to be backfilled.
Minerva's engineers, however, found a way to maintain tables in a snowflake schema and perform JOIN operations on-the-fly. This liberated the team from time-consuming and complex denormalization, leading to substantial resource savings and increasing the system's agility. Read the full story from Airbnb here.
A Cost-Efficient Approach to Real-Time Analytics
Addressing these challenges head-on, the open-source Linux project StarRocks provides a path towards more affordable and efficient real-time analytics. It allows for on-the-fly JOIN operations, effectively eliminating the need for separate stream preprocessing tools, and significantly reduces the infrastructure costs associated with real-time analytics.
Simplifying the data pipeline without compromising the quality of analysis, StarRocks enhances the flexibility of real-time analytics. The capability to derive immediate insights and make data-informed decisions swiftly, coupled with substantial operational cost savings, is redefining the economics of real-time analytics for many.
Try Real-Time Analytics for Free
Real-time analytics doesn't need to be synonymous with high costs. With solutions like StarRocks (and its commerical counterpart CelerData), it's possible to find an optimal balance between efficiency, affordability, and thorough analysis. As real-time analytics becomes more of a business priority, it's crucial to explore and embrace such innovations, ensuring we can deliver valuable insights without breaking the bank.
And there has never been a better time to explore new solutions. CelerData Cloud takes advantage of the powerful real-time innovations introduced through the StarRocks project, and delivers it on the cloud. Try it for yourself free for 30 days here.
Real-Time Analytics for the Modern Enterprise
In today's rapidly evolving business landscape, data has become the lifeblood of modern enterprises. Real-time analytics has emerged as...
How To Simplify Your Real-Time Analytics Pipeline
The ultimate goal of real-time analytics is to minimize the time for actionable insights. To achieve this, two factors need to be consi...