Time series databases are optimized for collecting, storing, retrieving, and processing time series data. It’s critical that businesses use a time series database for time series data and not one of the traditional data stores.
DBTA recently held a webinar with Daniella Pontes, product manager, InfluxData, who discussed how Time Series Databases are built with specific workloads and requirements in mind, including the ability to ingest millions of data points per second.
Time stamped data is crucial in a variety of instances including IoT, devOps, and real-time analytics, Pontes explained.
Events become regular time intervals, for example, when:
- Summarizing the average trade price of Apple stock every 10 minutes over the course of a day
- Summarizing the average response time for requests in an application over 1 minute intervals
The Influx Data platform can be the platform of choice for all metrics and event workloads, Pontes said. The platform allows users to:
- Quickly ingest data from everywhere
- Efficiently store (Compress) the data at scale
- Provide automation and control functions
- Evict and down-sample data
- Support real-time query, analysis and visualization of large data sets
- Provide time-based functions for “change over time” analysis and control
- Facilitate machine learning and anomaly detection algorithms
- Provide streaming analytics for data in motion
InfluxDB is easy to get started with, offers a familiar query syntax, has no external dependencies, allows for regular and irregular time series, is horizontally scalable, and is a member of a cohesive time series platform, Pontes said.
An archived on-demand replay of this webinar is available here.