It’s About Time: Timeengine Simplifies Large-Scale Data Processing & Analytics, a Scalable Tool That Reliably Handles Massive Volumes of Time Series and Spatial Data
QOMPLX™ is proud to announce TimeEngine, a multidimensional database and analytics engine for ingesting, storing, and modeling time series data. TimeEngine makes it easy for customers to better understand their data even when collecting and operating at billions of data events per day.
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TimeEngine supports elastic autoscaling to petabyte ranges while maintaining low millisecond-level query performance available as a Software-as-a-Service offering. This capability enables the acceleration of decision-making, the automation of detection and triage routines on sensor data as well as the creation of tremendous business value by ingesting, processing, storing, and alerting on analytic routines and queries with confidence and without interruption.
Data is core to our modern digital economy, but the eye-popping volume of dataflows organizations must examine has grown from a steady stream to a thunderous waterfall. To gain a comprehensive understanding of a variety of operational risks, sometimes insight only comes from understanding how related data changes over time, space, or other logical progression. That can include applications like:
- Tracking mobile assets like vehicles and shipping containers and how their attributes are changing
- Examining how corporate premises may be impacted by wind or flood exposures
- How operational technology assets perform over time and can achieve better performance using predictive maintenance and operations practices
- Understanding climate change by tracking changing animal migrations or behaviors
- Evaluating and alerting on activities and geo-tagged information within a specified proximity to potential terrorism targets and political violence events
“TimeEngine is unapologetically built for tremendous speed and scale,” said Jason Crabtree, CEO of QOMPLX. “It provides a flexible and highly configurable solution to optimize and reduce data analysis and retention costs. With powerful querying tools and specialized modeling capabilities for a wide spectrum of applications, we’re confident that TimeEngine can outcompete existing offerings with its advanced analytic features, robust scalability, and attractive storage costs.”
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A Holistic View of Your Risk
QOMPLX’s TimeEngine is multidimensional, and addresses the common need to store, index, and serve metrics from arbitrary event and time series data sources at substantial scale. It enables sophisticated and intuitive analysis of metrics and log data, financial ticker symbols, economic indicators, and scientific records in a highly-available and highly efficient distributed data store, ensuring that data is recorded and available even in the event of hardware or network failures. The distributed system supports seamless horizontal scaling to add and remove capacity to optimize performance for even the most challenging use cases. Intensive streaming analytics capabilities with optional real-time triggers, a powerful domain-specific time series modeling and query language, and configurable persistence with data summarization, downsampling, and granular retirement controls.
Multiple Languages and Applications Across Industries
TimeEngine already comes with client libraries for Java, Scala, Python, Erlang and Elixir and integrates seamlessly with the larger QOMPLX ecosystem of risk analysis tools. Users can trigger any number of highly advanced, recursive algorithms for anomaly detection, classification, event correlation, and forecasting future outcomes. TimeEngine can thus be used to solve a number of complex problems in real-time, including discerning l******* ROI of customers, funnel analysis, behavior/trend isolation, and multivariate statistical process control.
“Our team has made significant investments into making TimeEngine easy to use and integrate into core customer use-cases,” says Randy Clayton, Chief Delivery Officer of QOMPLX. “These investments pay dividends for our customers in terms of speed and agility to maximize value from their data at scale.”
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