New capabilities to enrich and unify event data from observability and monitoring tools that SRE, DevOps, and ITOps teams can easily detect problems and take action

BigPanda announced new Data Engineering capabilities that empowers enterprise DevOps and ITOps teams to unlock the full value of their monitoring and observability tools and get started on their AIOps journey.

A newer and simpler way to ingest data from multiple disparate sources, BigPanda’s Data Engineering capabilities transform event data output into high-quality, actionable alerts.

“Despite investing in several best-of-breed observability and monitoring tools, most organizations struggle to extract actionable intelligence from their event data to help their incident responders prevent and resolve outages,” said Fred Koopmans, Chief Product Officer at BigPanda. “By converting millions of events from monitoring and observability tools into actionable alerts, our new Data Engineering capabilities help organizations unlock the full value of their monitoring and observability investments and lay the foundation for successful AIOps adoption.”

“The amount of email alerts we received prior to working with BigPanda was unsustainable for us to properly escalate and triage incidents,” said Michael Lorenzo, senior director of operations for the global NOC at FreeWheel. “BigPanda’s data engineering capabilities helped us quickly cut out excess noise to better detect incidents and uncover probable root cause in real time. Ultimately, we were able to reduce email alerts by more than 90%.”

BigPanda’s Data Engineering capabilities include:

■ Easy integrations: More than 50 native low-code integrations, including integrations with leading observability, monitoring, IT service management, and IT operations management tools. There is no need for complex custom code to make it easy to ingest event data from any monitoring or observability tool.

■ Normalization: Standardization of disparate monitoring event formats into a common format that makes it easy to perform cross-source event correlation.

■ Seamless noise filtering: Out-of-the-box deduplication and noise filtering eliminate false positives and benign events. This helps teams focus on relevant events and reduce noise by up to 98%. In addition, a preview capability makes it easy to build and test new filter patterns based on alert metadata and enrichment tags.

■ Enrichment at scale: Enriched events include contextual data such as location, host, or affected services that increase the quality of alerts. Organizations can better realize the value provided by their monitoring and observability tools.

■ Unified visibility: Presenting higher-quality alerts from various monitoring and observability sources as part of a first pane of glass inside in BigPanda eliminates the need for teams to switch between different tool consoles to identify problems.

■ Interactive dashboards: Dashboards showing how events are processed, make it easy for ITOps and DevOps teams to see the daily trend of number of events, the source of different events, and actioned incidents in a single place.