From real-time alerts on cyber-attacks to IoT automation, event driven applications require modern architectures to rapidly analyze large amounts of data from different sources (such as sensors and videos). Triggering actions based on predefined rules enables companies to move towards more automated and intelligent decision making. With the iguazio Unified Data Platform, users deploy and run event driven applications with magnitudes faster time to insights, faster time to market and greater simplicity.


  • Faster response time to events
  • Data integration between interdependent systems
  • Rapid development and continuous integration


  • Informed decision making: Generating actions in real-time based on events
  • Volume: Processing and analyzing large volumes of data in real-time
  • Concurrent processing: Enriching and manipulating data in real-time
  • Combining real-time and historical data: Analyzing fresh and historical data simultaneously

Our Solution

Unlike traditional, complex data pipelines, iguazio’s Unified Data Platform enables customers to combine real-time processing and analytical capabilities in a single platform – ingesting, enriching, analyzing and serving streams, tables, objects and files. A variety of micro-services and processing frameworks access data concurrently, each making its own real-time adjustments. They add insights or query in parallel and results are always up to date. With this continuous approach, fresh and historical data drive real-time insights and actions for production use, as opposed to interactive queries which are just for data exploration and reports.

iguazio’s platform has built-in event-triggering and “serverless” functionalities, meaning microservices work via lightweight communications and are used only for event triggering, running relevant processing tasks and terminating tasks. Consequently, predefined rules invoke immediate alerts with minimum latency.

iguazio leverages stateless and containerized microservices that allow greater elasticity and simpler deployment. Modern platforms such as Mesos, Kubernetes and Docker can be used for orchestration while tools like Spark and TensorFlow handle data science tasks. Moreover, emerging stream processing tools are leveraged, creating new ML and AI libraries and implementing simple tasks using serverless functions.

Since processing is 100% stateless and updates are atomic, new applications are easily added and version updates are done on the fly in an agile cloud-native manner, allowing for rapid development and continuous integration.