Deploying effective Artificial Intelligence for IT Operations (AIOps) requires accurate data from from all your infrastructure formatted for your AIOps platforms; an observability pipeline makes that possible.
AIOps applies machine learning over large amounts of operational data to automate IT operations, including event correlation, anomaly detection, and causality determination. IT teams are exploring AIOps for several reasons, like reducing alert fatigue, proactively detecting performance problems, and avoiding outages. Many of those teams are searching for an all-in-one AIOps solution that does it all. The challenge these teams face isn’t with the predictive algorithms and models. Instead, their challenges are more practical concerns around collecting, normalizing, and routing data to the right places.
AIOps tools need the flexibility to ingest and index data from many sources. These include infrastructure, networks, applications, a range of monitoring tools, and deployed software agents. All data from these diverse sources must be normalized before it can be used for either real-time analytics over data in flight or for historical analysis over larger datasets at rest. Successfully deploying AIOps into the enterprise means managing three core constraints: volume, accuracy, and precision.
Stream can help you reduce as much as 50% of ingested operational data, delivering higher accuracy data to AIOps platforms. Easily eliminate duplicate fields, null values, and any elements of machine or security that provide little value to downstream AIOps models – and control infrastructure costs along the way.
AIOps isn’t the only use case for your operational data. Log analytics, application performance management, and security operations platforms still need rich troves of data. Use Stream to route data to multiple platforms without deploying new agents or sidecars – so you can use your current monitoring infrastructure to drive new use cases.
Because Cribl Stream is built for operational and observability data, it simplifies normalizing the hundreds of different input formats emitted from applications, system infrastructure, and networking equipment. You can also enrich data in flight, creating a higher value data product for your AIOps and observability needs.
Cribl Stream helped them move to the cloud by making it easy to stream, transform, filter, and manage all their on-prem data