GigaOm Sonar for Real-Time Analytical Databasesv1.0

1. Executive Summary

The key focus of real-time analytics is to power analysis that can deliver results and insights as events happen in the real world. To help achieve this instant analysis, a new category of databases—real-time analytical databases—has emerged. Real-time analytical databases work to ensure the data used in analysis is as up to date as possible. These databases have their roots in traditional online analytical processing (OLAP) databases; however, they surpass these predecessors by providing the ability to connect to and ingest extremely large (up to petabyte-scale) volumes of data, often from streaming data sources and batch or change data capture (CDC) sources. In doing so, they offer functionality found in both specialized business intelligence (BI) platforms and streaming data platforms and combine it with the core capabilities found in operational databases and data warehouses.

To facilitate analytics over large volumes of data with minimal latency, the databases in this category make use of structural and architectural optimizations. Examples include columnar orientation, various types of indexing, partitioning, and segmentation, precomputations of aggregations to accelerate queries, and vector processing. Scalability—the resilience of the system under the demands of increasing workloads—and high availability are also important in this category because of the time-critical nature of the analysis.

Real-time analytical databases allow organizations to see an up-to-the-minute view of the state of their data. This enables decisions to be made as events occur or as conditions change in the real world. This technology benefits any organization or user that needs data to be current and accurate. Use cases can take many practical forms, spanning different industries and target audiences. These can include healthcare, emergency response, cybersecurity, fraud detection, shipment and inventory tracking, personalized advertising, financial trading, and apps for food delivery or ridesharing. The time-sensitive criticality of some of these use cases is a primary factor driving the impact and urgency of adoption for these databases. The simple decreasing tolerance for latency among the modern everyday end users of an app, regardless of its intended function, drives urgency as well.

In terms of maturity, real-time analytical database technology is uniquely divergent. The online analytical processing (OLAP) methodology on which a number of these platforms are based (even if they use relational, rather than multidimensional, storage) represents a longstanding one. On the other hand, the workloads and use cases powered by real-time analytical databases are fresh and cutting-edge. The vendor landscape consists largely of newer, up-and-coming commercial offerings, often built upon open source databases.

This is the first year that GigaOm has reported on the real-time analytical database space in the context of our Sonar reports. This GigaOm Sonar provides an overview of vendors of real-time analytical databases and their available offerings, outlines the key characteristics that prospective buyers should consider when evaluating the solutions, and equips IT decision-makers with the information they need to select the best one for their business and use case requirements.