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GigaOm Sonar for Time-Series Databasesv1.0

An Exploration of Cutting-Edge Solutions and Technologies

Table of Contents

  1. Executive Summary
  2. Overview
  3. Considerations for Adoption
  4. GigaOm Sonar
  5. Solution Insights
  6. Near-Term Roadmap
  7. Analyst’s Outlook
  8. Report Methodology
  9. About Andrew Brust

1. Executive Summary

A time-series database maximizes the storage, retrieval, and analysis of time-stamped data. Time-series data reflects data-driven events or measurements that reveal how real-world occurrences, like the temperature reading from a sensor, have changed over time.

Time-series databases perform a number of functions to provide this utility. They’re able to ingest a vast amount of data points with incredibly low latency (including sub-second time frames) at tremendous scale. Each data point is marked with a timestamp. In addition to tracking and monitoring the progress of that data in real time, these databases downsample, compress, and aggregate time-series data to make room for analysis of more recent data and reduce storage consumption. They contain several capabilities for managing time-series data, including summarization, data lifecycle management, and record scanning. They also employ numerous means of optimizing queries pertaining to indexing and compression techniques while supporting time-series relevant functions like windowing, time bucketing, moving averages, and certain forms of segmentation.

Time-series database technology is important because it supports any number of real-time data analysis use cases. Batch processing, conventional relational database management systems, and NoSQL databases often lack the performance and scalability for use cases requiring low latency for the industrial internet, internet of things, and digital twins. With a time-series database, organizations can optimize those applications (including real-time stock trading, adtech, and insurtech), along with other common requirements for time-series analysis. These pertain to horizontal considerations like network monitoring, application program monitoring, observability and data observability, sensor data, and logistics. These applications, as well as the growing trend of including sensors in home, personal, and professional devices, make time-series databases increasingly necessary.

Time-series databases appeal to a broad range of enterprise users. Developers and IT professionals require them for observability and application monitoring. Information security teams employ them for cybersecurity and enterprise security. Data analysts involved in any internet of things (IoT) or sensor data use case work with this technology to support business end users with the freshest, most relevant data to support real-time recommendations and personalized interactions with customers across industry verticals.

The primary benefit provided by time-series databases is insight into how real-time information systems are changing over time. With the rise of IoT and sensor data use cases going back to the beginning of the last decade, the space is fairly mature. The speed with which organizations can capitalize on real-time data analytics provided by these engines makes them urgent for those pursuing real-time applications, though options with specialized programming languages require considerable effort to use. The impact of these databases on applications requiring low latency is quite significant.

The vendors in this space range from open source providers to those offering databases that are included as part of time-series data platforms. Understandably, some of these specialize in streaming data applications in particular, but others target a broader scope of data dynamics, focusing more on the times series nature of the data rather than its velocity. The increasing prevalence of application monitoring and DevOps tooling is contributing to the adoption of solutions that include time-series analysis.

This report evaluates solutions that don’t focus on a single class of use cases but were designed with time-series analysis in all of its manifestations in mind.

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


This GigaOm report focuses on emerging technologies and market segments. It helps organizations of all sizes to understand a new technology, its strengths and its weaknesses, and how it can fit into the overall IT strategy. The report is organized into five sections:

  • Overview: An overview of the technology, its major benefits, and possible use cases, as well as an exploration of product implementations already available in the market.
  • Considerations for Adoption: An analysis of the potential risks and benefits of introducing products based on this technology in an enterprise IT scenario. We look at table stakes and key differentiating features, as well as considerations for how to integrate the new product into the existing environment.
  • GigaOm Sonar Chart: A graphical representation of the market and its most important players, focused on their value proposition and their roadmap for the future.
  • Vendor Insights: A breakdown of each vendor’s offering in the sector, scored across key characteristics for enterprise adoption.
  • Near-Term Roadmap: 12- to 18-month forecast of the future development of the technology, its ecosystem, and major players in this market segment.