Centrally managed data processing pipelines accelerated by auto-scaling cloud compute infrastructure deliver faster processing speeds and stability - without the complexity.
- Analyze data events asynchronously – as data arrive, in batch, or on a schedule – so that servers only do work when called on.
- Configure downstream actions like emails, SMS messages, application alerts, machine learning model predictions, or REST API calls.
- Visualize time-series data stream for real-time analysis and monitoring of high-frequency data loads of any scale.
- Dynamically provision the optimal quantity compute resources based on the volume and specific resource requirements of the batch jobs submitted.
- Focus on analyzing results and solving problems. There is no need to install and manage batch computing software or server clusters.
- Identify duplicate data points and determine how they should be handled.
- Schedule batch jobs to run on a periodic basis.
- Rapidly train and re-train AI / machine learning models by iterating over data in-memory rather than on disk.
- Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.