Cloud Streaming Analytics Use Cases

Crosser recently launched the Crosser Node on the Azure Marketplace. This article introduces some use cases where Crosser can simplify and/or enhance your cloud-based streaming analytics.

To get started all you need to do is to register an account in the Crosser Cloud service and install a Crosser Node from the Azure Marketplace in your Azure account.

With Crosser Streaming analytics you easily build your analytics pipelines using the FlowStudio drag’n drop editor where you combine pre-built modules into processing flows. Interactively test your flows with live data and when verified, publish your flows with a single click onto your cloud node. Instead of connecting multiple services by configuring them independently you get a graphical view of your whole pipeline, from input to output.

The Crosser for Azure solution is an all-in-one solution for collecting, transforming, analyzing, processing and acting on streaming or batch data. By leveraging the over 100 existing modules and connectors non-developers can innovate and deploy faster than ever.

The capabilities of the Crosser solution opens up for a large number of use cases. Here are the top four:

#1 Centralized Aggregation and Analytics of IoT data

- Alternative to Azure IoT Hub and Streaming Analytics

If you have distributed data sources that can deliver data to the cloud using either MQTT or HTTP you can use the Crosser Node as a central aggregation point. Combined with the standard analytics features you can easily transform/harmonize your data before sending it either to storage or directly to Power BI for visualization. If you want to go even further it’s easy to extend your data pipeline with custom code (C# or Python) or even machine learning models, all within the same processing flow running.

In this example the Crosser Node is a direct replacement for the Azure IoT Hub and Streaming Analytics.

#2 Intelligent Process Automation

The Crosser Streaming Analytics solution can be used for much more than just IoT data. If you want to automate a workflow between two SaaS services, Crosser can be used here as well. Let’s say that you want to update an account in SalesForce each time a ticket is created in Zendesk, so that your sales team can react and track what is happening at their customers. You can then build a flow that is triggered whenever a ticket is created or changed in Zendesk and update the corresponding SalesForce account with the new information.

Use pre-built modules from the Crosser library to make the data formats used by these two systems compatible with each other. Make your workflow more intelligent by adding logic to decide what and when to transfer data, or extend the workflow by adding text message outputs directly to the account owner when something really critical has happened.

#3 Streaming Analytics

As a final example we will look at a fully in-cloud example. Let’s say your streaming data source is somewhere within your Azure account and it’s made available through the Azure Event Hub. You want to apply an algorithm (or ML) on this data and store the result in an Azure SQL database, so that you can later analyze and/or visualize the result.

That’s easy, just build a flow that uses the EventHub as input and Azure SQL as output. In between these you have access to all the data analytics tools available in Crosser to process your data. You can interactively see what is happening and it is easy to try new algorithms.

#4 On Premise Data Collection

In some use cases the data sources are only available on-premise, making a cloud-only solution impossible. With the Crosser Node, being a Docker container, you can solve this by installing a local node next to your data sources and then let this node publish the data over MQTT to the cloud node where you run your streaming analytics.

This works the other way around as well, i.e. when you want the results of your cloud-based streaming analytics to reach an on-premise destination. A local node can then receive the data from the cloud and deliver it to local destinations behind the firewall, without having to open any ports for inbound traffic from the Internet. The Crosser Cloud management system handles both local and cloud nodes in the same user-friendly way.

If there are use cases that don't involve any cloud data source or destination, a flow can be built and deployed to a local node - often called Edge Analytics.

Summary

Crosser provides the perfect tools to help you make your data useful. Get insights to optimize your operations and take appropriate actions immediately based on your data. Contact us to discuss how Crosser can be relevant and how to get going in no-time with the self-service capabilities of the platform.

Read more:

The Crosser Platform →

Cloud Streaming Analytics →

Edge Analytics →