Turning data into actionable insights.

KX Streaming Analytics provides the ability to ingest, store, process, and analyze historic and time series data to make analytics, insights, and visualizations instantly available.

To help ensure your applications and users are productive quickly, the platform provides the full lifecycle of data services, including query processing, tiering, migration, archiving, data protection, and scaling.

Our advanced analytics and visualization tools, used widely across finance and industry, enable you to define and perform queries, calculations, aggregations, machine learning and AI on any streaming and historical data.

Deployable across multiple hardware environments, data can come from real-time business events and high-volume sources including sensors, clickstreams, Radio-Frequency Identification, GPS Systems, social networking sites, and mobile devices.

A uniquely integrated platform that combines ingestion, analytics, and visualization.

KX Streaming Analytics provides full life-cycle data ingestion, processing, analytics, and data management. It also includes tools for data exploration, visualization, integration and querying – all in one software stack that reduces TCO by making deployment easier, reducing hardware requirements simplifying maintenance.

KX Analyst allows you to manipulate massive datasets, without programming, using an intuitive point-and-click interface.

Incorporate flexible spreadsheets to perform simple or complex analytical computations in real time, with full scripting support. Data analysts and scientists can effortlessly load, transform, query, and visualize massive datasets in near real-time in a development environment that offers tools for creating, editing, debugging, testing, and versioning kdb+ Big Data applications.

Data doesn’t stop, so the decisions and automation that depend on it cannot stop either. 

That’s why we support high-availability cluster configurations with node-level quorum capabilities that provide resilience in the face of network failures.

Advanced monitoring of processes, applications and nodes along with a range of hot/warm/cold configurations and data replication options cater for different cost/risk profiles. Fault tolerance is not fault elimination, however, so to cater for the most demanding, mission-critical environments we have extended capabilities around continuous data ingestion, storage and advanced query routing to ensure no downtime, no data loss or compromise in data integrity in the case of disruption.

Monitoring capabilities help clients ensure their day-to-day operations run smoothly and on schedule.

KX Streaming Analytics provides a set of customizable dashboards that display high-level information on the current status of primary indicators as well as real-time alerts and notifications of issues. Historical data is retained for follow-up investigations and root cause analysis.

KX Streaming Analytics’ small footprint and single-stack architecture enable it to be deployed seamlessly and identically across multiple environments.

Running the same software to solve complex business problems, whether on-premises, in the cloud, at the edge, or in any hybrid configuration of the three, makes it quicker to deploy, easier to maintain and cheaper to operate. It supports multiple cloud architectures including serverless deployments that enable developers and organizations to focus on delivering business results rather than worrying about hardware and infrastructure requirements.

KX is designed for critical real-time performance with extreme scalability to support continuous actionable intelligence.

KX Streaming Analytics is able to solve complex problems faster – and on a smaller infrastructure footprint – than any of its competitors — whether on-premise, in the cloud or at the edge. Built-in machine learning and complex event processing allow quick decision-making and proactive automated actions to be executed in fractions of a second.

KX Streaming Analytics supports various cloud architectures including serverless deployments where developers can build out flexible services and deliver business results rather than worry about hardware infrastructure.

For more information on these and other features in the latest release of KX Streaming Analytics, please click here

Leveraging the kdb+ time series database, the KX streaming analytics platform achieves extremely high performance with minimal infrastructure requirements using industry standard hardware.

Kdb+ is unique from other popular databases because it operates directly on the data in the database, removing the need to ship data to other applications for analysis.

Kdb+ makes full use of the intrinsic power of modern multi-core hardware architectures. Highly interoperable with other programming languages, kdb+ supports an extensive range of interfaces with a very simple API, for easy connectivity to external graphical, reporting and legacy systems. 

KX AutoML helps users to automate the process of applying machine learning techniques to real-world problems and opens up machine learning capabilities to a wider audience.

The AutoML framework provides a complete machine learning workflow, including pre-processing, feature engineering, model selection/optimization and report generation. Follow this link to see how it can be used to solve regression problems using the FRESH algorithm.

KX Dashboards for streaming analytics enable you to query, transform, share and present live data insights in real time. 

With 3rd party integration and SDK for custom visualization, hardware-accelerated rendering for faster display time and an extensive range of native display options, KX Dashboards enable solutions for surveillance, financial charting, sensor monitoring and more.

Simplifying Integration 

While KX offers a single-stack solution to support the complete data  analytics life-cycle from ingestion to processing and visualization, it also provides interfaces to other technologies. Examples range from programming languages like Python, R, C/C++ and Java to middleware like Solace and Kafka and to Machine language platforms like Keras and TensorFLow
This approach enables organizations to preserve the value of existing investments and technical skills in other areas and accelerate solution development through code reuse and industry-standard interconnectivity.