Four months into this new gig at Cribl, I wish I could bottle up that “lightbulb” moment I get when walking people through how Cribl LogStream can help them gain better control of their observability data. So I hope the scenario walkthroughs below will capture some of that magic and shed some light on how LogStream can improve your organization’s data agility – helping you do more with your data, quickly, and with less engineering resources.

All Cloud providers such as AWS, Azure, Google Cloud Platform, and Oracle Cloud offer Object Storage solutions to economically store large volumes of data and retrieve it on demand. It’s far cheaper to store one petabyte of data in object storage than in block storage. As AWS S3 has become the standard, many on-premise storage appliance vendors have incorporated S3 APIs to store and retrieve data. Oracle wisely continued that trend to OCI (Oracle Cloud Infrastructure).

In the last few years, many organizations I worked with have significantly increased their cloud footprint. I’ve also seen a large percentage of newly launched companies go with cloud services almost exclusively, limiting their on-premises infrastructure to what cannot be done in the cloud — things like WiFi access points in offices or point of sale (POS) hardware for physical stores.

Health data is notoriously difficult to collect, route, and transform. I will demonstrate how to leverage the LogStream Observability Pipeline to solve these problems and help users search their Apple Health data.

Preventing data loss for data in motion is a challenge that LogStream Persistent Queues (PQ) can help prevent when the downstream Destination is unreachable. In this blog post, we’ll talk about how to configure and calculate PQ sizing to avoid disruption while the Destination is unreachable for few minutes or a few hours. The example follows a real-world architecture, in which we have.