Trending Time Series Databases Software
Momentum scores for Time Series Databases are shown below. The Momentum Grid® highlights each product’s Momentum score on the vertical axis and the product’s Satisfaction score on the horizontal axis. These scores are based on G2’s Satisfaction and Momentum algorithms. Products with a top 25% Momentum Grid® score are shown within the shaded area below.
Time Series Databases Momentum Grid® Description
A product’s Momentum score is calculated by a proprietary algorithm that factors in social, web, employee, and review data that G2 has deemed influential in a company’s momentum. Software buyers can compare products in the Time Series Databases category according to their Momentum and Satisfaction scores to streamline the buying process and quickly identify trending products. For sellers, media, investors, and analysts, the Momentum Grid® provides benchmarks for product comparison and market trend analysis. Badges are awarded to products with the top Momentum Grid® scores.
Products included in the Momentum Grid® for Time Series Databases have received a minimum of 10 reviews. There must also be at least a year of G2 data for the product to be included. These ratings may change as the products are further developed, the sellers grow, and additional opinions are shared by users; a new Momentum Grid® report will be issued for this category as significant data is collected.
Time Series Databases Definition
Time series databases allow businesses to store time-stamped data. A company may adopt a time series database if they need to monitor data in real time or if they are running applications that continuously produce data. Some examples of applications that product time series data include network or application performance monitoring (APM) software tools, sensor data from IoT devices, financial market data, and a number of security applications, among many others. Time series databases are optimized for storing this data so that it can be easily pulled and analyzed. Time series data is often used when running predictive analytics or machine learning algorithms, enabling users to understand historical data to help predict future outcomes. Some big data processing and distribution software may provide time series storage functionality.
To qualify for inclusion in the Time Series Databases category, a product must:
- Store data based on timestamps
- Consume data in real time
- Allow users to easily pull the data for time series analysis
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