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Mezmo Unveils Mezmo Flow for Guided Data Onboarding and One-Click Log Volume Optimization

Mezmo unveiled Mezmo Flow, an industry-first guided experience for building telemetry pipelines. With Mezmo Flow, users can quickly onboard new log sources, profile data, and implement recommended optimizations with a single click, to reduce log volumes by more than 40%. With this release, Mezmo enables next generation log management, a pipeline-first log analysis solution that helps companies control incoming data volumes, identify the most valuable data, and glean insights faster, without the need to index data in expensive observability tools.

The accelerated shift to digital and the adoption of distributed and microservices-based architectures have led to organizations generating petabytes of data daily. This has resulted in massive challenges with storage, latency, and analysis. Companies are dropping high-volume sources due to prohibitive costs, forcing observability to be inadequate.

Developers should not have to choose between how much they can log and how fast they can debug and troubleshoot issues, especially with custom applications. SREs need an easy way to understand logs, monitor any data spikes, solve any infrastructure issues, and easily provision data to downstream teams and systems. The new release from Mezmo streamlines both developer and SRE workflows.

Mezmo is leading the way for intelligent pipelines that automatically analyze telemetry data sources, identify noisy log patterns, and create a data-optimizing pipeline that routes to any observability platform. With Mezmo Flow, users can create their first log volume reduction pipeline in less than 15 minutes, retaining the most valuable data and preventing unnecessary charges, overages, and spikes. Next generation log management is a pipeline-first log analysis that improves the quality of critical application logs to improve signal-to-noise ratio for increased developer productivity. Alerts and notifications on data in motion can help users take timely actions for accidental application log volume spikes or changes in metrics.

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