Static, Streaming, and Historical Data

From Traditional Analytics to Next-Gen Analytical Applications

With the rise of the IoT, streaming data is growing. Relational databases and data warehouses are hitting their limits. Next-gen technologies are required to meet today and tomorrow’s data challenges. Organizations are turning to analytical processing on streaming workloads, to explore and analyze data that generates business-critical insights.

Relational Databases & Data Warehouses Aren’t Built for Streaming Data

Building custom analytical apps is a challenge

Slow Performance

Disk-based architectures that rely only on CPUs to process data are too slow for datasets growing in volume and complexity

Streaming Struggle

Traditional solutions can’t cope with the speed and complexity of streaming data analysis, particularly from the IoT

Time Series Difficulties

Modeling time series data is outside of the traditional relational database calculus

Isolated AI

Analytical tools, like AI & ML models, are isolated from technology that natively manages data, limiting their value

Bring Data Intelligence to Analytical Apps at Breakthrough Speeds

Kinetica combines the capabilities you need in one unified engine, so it’s easy to run advanced query processing or build a custom analytical app

High Performance

Interactive: uses both a CPU and GPU for processing, and uses a memory-first approach for servicing

Static & Streaming

Agnostic consumption: Can accept static and/or streaming data

Time Series Analysis

Calculated: Queries can ask about time windows and perform calculations very effectively


Built-in: AI & ML aren’t separate initiatives; they are built into analytic workloads or custom apps to deliver intelligent insight

Kinetica Demo - Measuring Risk

Take a look at Kinetica’s financial instrument analysis demo for measuring risk

Analytical Processing Capabilities


SQL support. No need to learn new skills to operate


Connect to any data management, BI, ETL, data quality, or data wrangling tool


Kinetica supports authentication and authorization features such as LDAP, role-based access control, and encryption

High availability

Across clusters, the HAProxy component per cluster manages requests and quickly detects whether or not a cluster is available

High speed ingest

Consume high velocity streaming data


Bring your own algorithm or model to your data, and run it in database or in engine

Time series functionality

Kinetica natively manages time-series data and window functions for time series analysis

Full Text Search

Geospatial and geofencing analysis with full text search, to quickly drill down from billions of data points to a very small matching dataset without knowing what you’re looking for