Time series data
User clicks in a website; asset monitoring; security systems; business metrics. What is the common ground among all of those data uses? To all of them, it’s very important to know when stuff happened. They generate what we call time series data. Time series data is a sequence of data points collected at regular intervals over a period of time, that usually save data coupled with a timestamp.
Though the concept and use of time series data are not new, the large growth in IoT has driven up the demand to process and analyze large volume of time series data. The main point about time series data is the order of time points matters. Time series data are analyzed to determine the long-term trend in order to predict the future or perform some other form of analysis.
Traditional databases were not built to handle the volume, speed, and variety of data being generated by devices and sensors. They can also be very expensive, since you pay for storage space and performance, and time series data usually demands a lot from both. This causes big challenges in ingesting, storing and analyzing this type of data.
SlicingDice's database easily crunches time series data
SlicingDice offers a petabyte-scale analytical data warehouse which is the ideal choice for storing time series data since it was built for that. Coupled with our innovative pay-per-column pricing model, where you can store unlimited rows per column for a fixed price, this means organizations can store as much data as they need, with low and predictable costs.
Since time series data can be used in a lot of different cases, we support both time series and non-time series data without losing performance or adding complexity, so analytics can be easily performed to meet any analytical use case. Since we’re All-in-One, organizations can save time and money by using a single solution to handle all their different data types and uses, freeing IT personnel to perform other, more business-critical tasks.
Check the Documentation
Schema and data type flexibility.
Store time series and non-time series in the same database. Store data with different structures. Query it all together for deeper insights.
Supports billions of insertions per minute.
Perform as many concurrent loading jobs you need, from as many data sources you want. Insertion throughput is nearly unlimited.
Inserted data is immediately available.
No matter the insertion method, data stored in SlicingDice can be analyzed, visualized and leveraged within a couple of seconds.
SQL & API flexibility.
Interact with your database using both SQL and our REST API. Perform any task the way it best fits your needs.