Unified Analytics

Kinetica unifies multiple analytical techniques including relational, geospatial, graph, time series, and text search, so that users can detect and act on complex patterns in the moment. Simplify your architecture to reduce data movement and accelerate time to insight. Operationalize your results by using them as features for machine learning algorithms.

Combine Different Analytics Techniques

Interoperate with different data types and analytical techniques via SQL and REST API calls. Use relational, geospatial, graph, text, and time series operations in the same queries. Find new insights by mixing analytics and data sets. Handle high frequency streaming data and huge swaths of historical data in the same system. Get results in seconds instead of hours or days.

Tech Talk: How To Take Your SQL to a New Level

Model Relationships with Graph Analytics

Generate graph data from your relational and spatial data with a simple API call. Utilize pre-packaged graph algorithms to work with geospatial and relational functions for fast analysis without needing to deploy a separate graph database. Optimize routes, predict relationships, and forecast outcomes algorithmically on very large data sets in real-time.

Tech Talk: Data Driven Planning to Make Cities Smarter, an End-to-End IoT Example

Use the Power of Full Text Search

Kinetica provides powerful text search and filtering capabilities for string-based data. Generate views from comprehensive search terms including wildcards, fuzzy matching, word proximity, and relevance. Combine with streaming and geospatial s analysis and leverage for feature extraction to train models.

Tech Talk: How to Combine Text Search, Geospatial and Machine Learning Techniques for Streaming Data Analysis

Analyze Time Series Data at Scale

Kinetica lets you simultaneously ingest and analyze time series data at market-tick speed and IoT scale. Conduct powerful time series analysis such as aggregations, window functions, and inexact joins without needing to send data to a separate time series database. Incorporate all your historical data in continuous queries, and utilize other analyses such as geospatial aggregations.

Augment Analytics with Machine Learning

Your data scientists can combine different types of analyses to explore data sets and develop new feature calculations. Utilize Kinetica’s performance at scale to rapidly calculate complex features at high velocity . Utilize machine learning results to augment accuracy of your analytics. Easily deploy ML models to production, and get visibility on behavior.

Tech Talk: How To Run Machine Learning at Extreme Scale with Kinetica

Active Analytics for Smart Cities: Rio de Janeiro

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