Everything. Everywhere. All the time.
The world has changed. We’re used to everything being connected, but today, the amount of data produced constantly by all kinds of devices, services, and tools is almost impossible to comprehend.
There are cloud microservices, the IoT, temperature sensors and connected energy meters, market fluctuations and medical sensors. The list is endless, as is the amount of data produced. A single self-driving car can produce up to 4,000 GB of data each day. Multiply that by hundreds of thousands, or even millions, and you’ve soon got… a headache.
Here comes the flood
Tracking this second-by-second data can be critical – literally, in industries such as healthcare. And with 86,400 seconds a day, standard databases simply can’t handle the flood of data fast enough.
And that’s exactly where time series databases come to the rescue.
A Brief History of Time Series Data
From the dawn of time series data...
Time series databases grew out of the need to process financial data and track market fluctuations throughout the day. The ability of time series databases to pair data points with timestamps at a massive scale was of huge value. Developing the ability to identify trends or market anomalies could lead to big profits. Really big profits.
...To predicting the future
Time series databases' core benefit is in helping you quickly analyse and identify data patterns. Coupled with efficient data storage it becomes possible to concurrently view past, present, and potential future datasets.
Why do you need time series data?
A matter of time...
All businesses run on data, but some rely on it more than others. When a data stream produces large amounts of constantly changing data with the flow of time, that’s when TSDBs start to make sense.
It’s certainly possible to use other types of databases to track time-based data, but only time series databases are designed to scale up to the massive amounts of data produced by today’s numerous connected devices and tools.
The problem is not simply the amount of data produced, but also the storage needed, and the speed of data access. Time series databases are resource-efficient: they can eliminate unwanted data streams or select data points on the fly, and compress or aggregate data to optimise storage. Less waste. More useful information. And, more importantly, information that can be accessed much faster.
Lastly, there’s the issue of security. With the amount of potentially personal information recorded today, privacy and security are vital elements of any time series database.
All data needs to be protected, and Aiven’s time series solutions ensure that your data remains secure – and not owned or accessible by someone else.
Time series data in action
While time series databases began as a tool for tracking rapidly changing financial market fluctuations, their use has expanded with the exponential growth in the amount of data produced by today’s connected devices, apps, and services.
It seems that every day a new device or technology is created, ready to launch gigabytes of data into the cloud. There are as many uses for time series databases as there are apps on your phone. Here are just a few:
Internet of Things
The increasing amount of IoT deployments means only one thing: The amount of data produced by connected water, energy, temperature meters, health monitors, and wearable tech is growing exponentially. The need for highly scalable database architecture has never been greater.
Performance and health monitoring
App performance, button clicks, heatmaps, bounce rates… All these things can be measured and monitored by a TSDB so that developers can keep track of user habits and performance trends, identify bottlenecks, and streamline complex processes.
Financial trends and retail forecasting
TSDBs are still vital for tracking and analysing market fluctuations. Detecting causal relationships between related events and cross referencing with historical data can lead to profits in the stock market, and help retail stores predict future trends to anticipate stock demands.
Autonomous driving data
Self-driving cars seemed like a sci-fi dream until recently. The amount of data produced by potentially millions of cars is mind-boggling. TSDBs are an essential tool to process high volumes of real-time data, and contribute to improving safety, potentially saving lives.