Selecting the right technologies and solutions for your business requires a thorough understanding of what you do—and don’t—need. While leaning toward more general purpose, traditional solutions is seemingly more comfortable for many enterprises, purpose-built tech may hold the key for true efficiency.
Anais Dotis-Georgiou, product manager, InfluxData, joined DBTA’s webinar, Why a Time Series Database is the Best Solution for Building Real-Time, Intelligent Systems and Applications, to discuss the scenarios in which time series databases rival traditional relational databases, offering a variety of benefits relating to performance, operational overhead, and more.
To begin, Dotis-Georgiou defined time series data as any data that has a timestamp associated with it. As a sequence of data points, time series data typically consists of successive measurements made from the same source over a time interval.
“Everything happens in the context of time, and time series data preserves this context,” said Dotis-Georgiou.
Why should you care about time series data? “We are in the age of instrumentation,” said Dotis-Georgiou. “We are getting more and more time series data from both our physical and digital worlds.”
From devices to sensors, autonomous vehicles, and smart grids in the physical world to networks, containers, financial systems, and VMs in software in the digital world, time series data plays a significant role, particularly in regards to monitoring.
“Whether or not you’re looking at data in the physical or virtual world, the commonality here is that users want to be able to understand trends over time so they can not only detect when their environment that they are monitoring deviates from the norm and they have anomalies but also to forecast and to, in general, have more control over the physical and/or virtual world,” Dotis-Georgiou explained.
Time series data is crucial in shifting from reactive to proactive business operations, where incorporation of time series data can help a business save millions in avoiding potential downtime. However, harnessing time series data is not without its challenges, as it requires:
- Support for massive scale, where ever-increasing data sources and frequencies create high throughput and storage requirements
- Real-time action where applications must analyze data within streams and act in real time
- Support for high data cardinality driven by a higher number of tags collected, impacting performance
- Simplified access and a widely used toolset to promote developer productivity
Why don’t general purpose databases fit the job? According to Dotis-Georgiou, while general purpose databases are ideal for structured data and when complex interrelationships exist between the data elements, they fall short in several areas:
- Slow insert performance (transactional overhead)
- High storage costs
- Does not operate at scale
- Complex query execution and manual data lifecycle management
InfluxData innovates in this space, offering InfluxDB, a time series database (TSDB) purpose-built and optimized for time-stamped data. InfluxDB excels where general purpose databases do not for time series data, including high scalability and performance, high write throughput, efficient queries over time ranges, and more.
InfluxDB utilizes Apache-backed technologies to efficiently ingest, store, and analyze time series data at any scale, according to InfluxData. Additionally, the database offers native SQL support, storage efficiency, and seamless integrations with an open data architecture while eliminating cardinality concerns.
This is only a snippet of the full Why a Time Series Database is the Best Solution for Building Real-Time, Intelligent Systems and Applications webinar. For the full webinar, featuring more detailed explanations, a demo, case studies, a Q&A, and more, you can view an archived version of the webinar here.