Companies who use Riak TS for time series data:
Companies choose Riak TS as their NoSQL database for its:
Riak TS Resources:
Five configurations are designed to satisfy your specific database requirements.
|RIAK TS OSS||RIAK TS DEVELOPER||RIAK TS PRO||RIAK TS ENTERPISE||RIAK TS ENTERPRISE PLUS|
|TIME SERIES DATA MODEL|
|MASTERLESS WITH BUILT-IN REPLICATION|
|HTTP API AND PROTOCOL BUFFERS|
|REDIS CACHING (KEY/VALUE BUCKETS)|
|RIAK ENGINEERING SUPPORT||Business Hours||Business Hours||24x7x365||24x7x365|
|ONSITE REVIEW AND SYSTEM ASSESSMENT|
|ONLINE TICKET TRACKING|
|SLA||24 Hour||4 Hour||1 Hour||30 Minutes|
|LICENSE TYPE||Apache 2||Commercial||Apache 2||Commercial||Commercial|
Time series data is any data that has a timestamp. This can be IoT device data, financial and economic data, or even scientific and health data. To provide optimal performance it is useful for the database to have some knowledge about the structure and format of the time series data
- Time series data often has a higher write load requirement than key value use cases and therefore requires high performance reads and writes even with a huge number of data sources.
- To more efficiently analyze time or location data, related time series data needs to be co-located on the same physical storage on the same vnode.
- The time series NoSQL data model requires flexibility to support both structured and semi-structured data as well as the ability to write range queries to analyze your time series data.
- Time series data is often collected at frequent intervals which may not be relevant as data ages. The data will often be rolled up, compressed and the granular details expired.
Riak TS read and write performance is optimized specifically for time series data. It groups and stores data together and automatically distributes replica data around the cluster to enable you to easily analyze temporal or geolocated data. This scale-out architecture lets you add capacity seamlessly using commodity hardware for near-linear performance improvement and faster queries.
Riak TS has a masterless architecture and automatically replicates data to ensure that your data is always available for both read and write operations. This is especially important when ingesting potentially millions of time series data points.
Riak TS is designed to store data and serve requests predictably and quickly, even during peak loads. This ensures data ingestion supports millions of data points being added from thousands of sources.
Riak TS’s optimized range queries make it easier to leverage your existing knowledge so that you can write SQL like queries to analyze your time series data.
Riak TS allows you to use composite keys (time, geohash, and data family) to define sort order on disk for faster read performance.
Riak TS seamlessly integrates with Apache Spark to ensure easier and faster operational analysis of time series data.
INTERNET OF THINGS (IoT) and CONNECTED DEVICE DATA
Connecting smart devices in our homes to provide better service and save money is helping drive the growth of the Internet of Things (IoT). Examples include: Utilities that have meters creating billions of data points a year and companies like The Weather Company managing 20 terabytes of new data per day.
Riak TS is a key/value store that supports rapid ingestion of time series data from connected devices. It provides extremely fast reads and writes on a scalable architecture. Riak TS enables application processing of this data to generate actionable information. Riak TS is designed to scale horizontally with commodity hardware, making it easy for administrators to add capacity without complex sharding.
TIME SERIES FINANCIAL DATA
Time series data not only comes from devices but is also generated in our financial systems in the form of stock market indices, commodity prices, unemployment numbers, and many other financial and economic indicators. Time series data can be used to see how a given asset, security, or economic variable changes over time or how it changes compared to other variables over the same time period.
Riak TS is uniquely architected to process the vast amount of time series data including financial and economic data. It enables related data to be stored, queried, and analyzed together to optimize the performance of reads and writes.
SCIENTIFIC TIME SERIES DATA
Scientific fields are also increasing their collection and analysis of large-scale time series data. For example, understanding the relationship between El Niño weather patterns and fish populations requires measuring and comparing air pressure changes in the Pacific Ocean against the number of new fish reported by fisheries and research expeditions.
To perform this type of analysis, you must be able to run queries of time series data at scale, as well as validate your data while it’s being collected to assist with data integrity and compliance. You also need a strong and familiar query language to quickly analyze your data without resorting to complex ETL tricks to do ‘off-box’ analysis with custom tools.
Riak TS provides all of this critical functionality and more so that you can focus on understanding and analyzing your data instead of being overwhelmed by the data tsunami.