Data Engineering

Actionable alerts in one place

Data Engineering from BigPanda transforms millions of events into a small number of actionable alerts, no matter where they originate, on a first pane of glass.

Challenge

Teams are swimming in event noise.

The widespread adoption of monitoring and observability tools causes ITOps, DevOps and SRE teams to drown from event noise. Events distributed across a hybrid or multi-cloud environment pile up and overwhelm teams’ human capacity to focus just on important alerts, causing IT incidents to pile up and painful, costly outages to occur.

Solution

High-quality alerts in one place.

By engineering the raw event data into a high-quality stream of alerts, BigPanda becomes the first pane of glass that ITOps teams can consult in order to quickly understand what is happening in their IT environments and which action to take.

90% noise reductionReduce IT event noise by automatically filtering out false positives and benign events—and add context to the remaining events to transform incidents into actionable alerts. Enable your existing team to focus quickly on the real issues related to incidents and outages.
Increased value for monitoringEasily assess the impact of alerts by normalizing and enriching them with context (like location, host, or affected service) to increase their quality. Visualize the value provided by all your monitoring tools and adopt a data-driven approach to tool rationalization that leads to a more mature observability strategy.
Eliminate silosConsolidate critical alerts, regardless of their monitoring source, and provide ITOps teams with a first pane of glass inside BigPanda. Investigating high-quality, enriched alerts in one place eliminates the need for teams to manually switch between separate tool consoles when working on incidents and outages.

Modules

The power of
Data Engineering

Monitoring integrations

Ingest events from various monitoring and observability sources through a REST API, email alert, or SNMP trap. Most integrations can be set up by users themselves.

Event normalization

Normalize heterogeneous data from different monitoring tools into a single, consistent format using general-purpose key-value pairs called tags.

Event deduplication

Intelligently reduce noise by parsing incoming events to recognize duplicates or updates to existing alerts, which are then discarded or merged.

Event filtering

Automatically suppress non-actionable events, such as maintenance windows or non-production environments.

Event enrichment

Annotate events with context tags extracted from payload data (like hostnames or clusters) or topology (like a CMDB or service map).

Event aggregation

Every time a monitored resource changes state, a new event is generated (sometimes called a flapping event). BigPanda rolls up these events and presents them to users in a single timeline view.

Explore

Relevant links

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Strengthen your observability strategy.

Do you require a solid monitoring and observability foundation before optimizing further along your ITOps chain? Not if you’re building a boat.

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CASE STUDY

Zayo unifies technology stack to filter out 99.9% of events.

Zayo’s NOC successfully manages a contextualized stream of actionable alerts by automatically suppressing low-quality noise.

CASE STUDY

FreeWheel reduces alert noise by 90%.

FreeWheel made it easy for response teams to quickly detect problems from a first pane of glass and take action on priority alerts which reduced their MTTR by 78%