In this three-part series, we’re going to discuss how to utilize the Striim platform as a data integration platform as a service. We’ll dive into the history and a brief technical overview of the platform, the ease of use in building and scaling applications via our wizards-driven UI, and working with simple and complex data flows.
Let’s start with a bit of background on the Striim platform.
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.
The Striim platform has a wizard-driven, intuitive drag-and-drop UI for building data flows. You can also visualize the movement of data, as well as the data itself, through Striim’s real-time analytics and dashboard capabilities. Striim is a very broad platform, so although it is a data integration platform as a service, it has numerous other capabilities for real-time streaming data analytics.
As indicated in this video, there are a lot of sources and targets you can integrate with. On the source side, we can pull continuous real-time data from almost any enterprise data source. We can also continuously deliver to any target and, while the data’s moving, enable transformation and processing of that data.
You can deploy your data integration applications anywhere, since Striim runs anywhere that Java runs. Users can run Striim on-premises, on physical machines, or on VMs. It runs in Docker / Kubernetes so you can run it as containers, and it’s also available in the Azure, AWS, and Google Cloud marketplaces. If you deploy in the cloud, we have an agent that you can run on-prem that securely connects into our platform in the cloud, and allows you to to deliver data from on-prem sources into cloud sources if you are running as iPaaS.
To learn more about the Striim platform, read our Striim Platform Overview data sheet, set up a quick demo with a Striim technologist, or provision the Striim platform as a data integration platform as a service on Microsoft Azure, Google Cloud Platform, or Amazon Web Services.
