In this video, Steve Wilkes, founder and CTO of Striim, discusses how to utilize the Striim platform as a data integration platform as a service.
Striim, at its heart, is a streaming integration platform that is in the business of continually collecting data from a number of disparate enterprise sources, moving them, and delivering them to a variety of different targets, while being able to process that data in-flight and in-memory. We also provide processing tools for filtering, transformation, correlation, aggregation and enrichment – enabling users to transform the data and add value – all in a scalable and reliable way.
Striim started off as an on-premises platform, running on physical machines or VMs in the customer’s data center, but over the last two years we’ve moved into cloud and now provide Striim as a data integration platform as a service.
Striim is used for many different use-cases, but typically, any occasion where you need data that is up-to-date (real-time data), and/or data needs to be moved between technologies or locations, such as on-premises to cloud. Cloud, hybrid-cloud, and multi-cloud scenarios are becoming more and more widespread, and Striim supports multiple database, data warehouse, messaging, and storage technologies across all three major cloud providers.
The second category of use case is real-time applications, such as pushing data out onto Kafka or a cloud message bus such as Google Pub/Sub, Amazon Kinesis, or Azure Event Hubs, or being able to visualize and analyze that data in real time.
The third use case is where Striim is used to continuously prepare and deliver data to Big Data platforms. Previously, Striim was used to deliver data to on-premises big data implementations, but that has changed a lot recently to the shift toward a cloud big data approach. We are getting customer requests to move real-time data being pushed from an on-premises Hadoop infrastructure into cloud storage, for example.
To learn more about Striim’s capabilities as a data iPaaS and further use-cases, read our blog post: