Modernize infrastructure monitoring by combining time series databases with machine learning for real-time predictions and actions
Up until now, most enterprises have settled for a reactive approach using a traditional time series database to visualize current trends and run batch analysis after the fact. However, modern businesses need to be proactive with sophisticated predictions and real-time actions which maximize the value of data.
Iguazio extends Prometheus with horizontal scaling, high speed ingestion and streaming, support for Python Pandas AI on the same data without copies, accelerated queries through pre-aggregation, automatic rollups and parallelism as well as high-availability, consistency, security and management.
While traditional TSDBs are limited to a single data type, the Iguazio database supports multiple data models (time series, SQL/NoSQL table, document, stream, object, file). This allows real-time enrichment with other data models taken from historical context, operational databases, social or environmental sources.
Iguazio is fully integrated with serverless functions, a multi-model real-time database and microservices over Kubernetes including Promethues, Nuclio, Python Pandas, Jupyter, restful APIs and open source AI tools. It delivers a seamless development experience throughout different stages in the processing pipeline.
Iguazio processes high volumes of incoming time series data in different formats or protocols, enriches it in real-time, feeds it into AI algorithms and serves the results back to users, dashboards or control systems. This complete workflow is achieved without tedious integrations.
