Real-time analytics of big data streams
ITRS Insights performs an impressive range of analytics across both real-time and historical data at a scale required by today’s big data world. This makes it suitable for running analytics on your business activities, your underlying technology or a combination of both
Perform sophisticated temporal or text based queries via an intuitive interface; helping you to interpret the masses of data coming from multiple sources in real-time.
Both the data and processing are spread across a cluster of nodes, with no data or processing reliant on a single node only. Hence, if a node goes down, the cluster can continue without issue, ensuring constant availability of both data access and analysis. In addition, the cluster is near linear scaling allowing fast computation of huge numbers of queries on the streaming data.
ITRS Insights RESTful interface allows you to easily deploy and seamlessly integrate with a wide variety of data collection or visualisation tools, or your own proprietary systems. It also includes a seamless integration to your ITRS Geneos deployments.
- Anomaly detection: advanced algorithms enable you to interpret periodic patterns in your data, so you can identify anomalies and future trends, and automate processes to protect against the occurrence of issues. Insights ability to analyse and store multiple streams of data from different sources means more information is readily available to query and compare, helping you find your answers in real-time.
Machine learning: the machine learning algorithm identifies patterns by learning what a “normal” pattern/trading day looks like. By segmenting the patterns against time, for example by trading days, you can use the patterns to detect anomalies. As more and more data is fed into ITRS Insights over time, it increases its ability to recognise patterns seen before and to pre-empt scenarios or flag issues before they occur, increasing your confidence in foreseeing and protecting against potential issues.
ITRS Insights ability to analyse and store data in both time series and semi structured databases means more information is readily available to query and compare.
- Time series: data that has a defined schema and time context can be processed using sophisticated algorithms that analyse the data to derive patterns, anomalies, trending and correlation between different streams, and a wide range of statistical and temporal analysis.
- Semi structured: ideal for data with widely varying content, either with or without a schema. This data can be accessed and analysed in conjunction with the time series data to provide a full picture of the underlying enterprise.
Now you can achieve a complete view of your data’s behaviour using one application. For instance, by comparing historical trades to current messages you can track trade flows that go through multiple hops, in different formats, from one place.
Your queries, research and investigations are pulled together into notebooks that can be easily shared with different audiences and stakeholders, including those who might not necessarily have direct access to the ITRS Insights application. If the query is dynamic, the visualisation will be too.
The easy to use interface is designed to enable fast and simple visibility into your complex environments. ITRS Insights uses “domains” to understand the subset of data you want to work with. “Contributors and streams” let you select the sources of data that are important to you.
Whatever data you choose to stream into ITRS Insights is stored in a read-only format, making it secure and immutable.