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Microservices and other distributed software architectures need trace analytics to ingest and visualise OpenTelemetry data within a tool such as hosted OpenSearch. Ingesting and centralising traces allows users to identify performance bottlenecks and events with distributed architectures. By using you can easily manage trace ingestion, retention, and costs with full visibility of your billing.

Identify problems and correlate events with the full context of data by monitoring service dependencies and health metrics throughout your systems.

Experience real-time monitoring, analysis, and reporting of high-granularity data and custom metrics, displaying throughput, availability, reliability, and error rates by using our all-in-one observability platform.


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What Is Trace Analytics?

When a single user action can easily trigger a complicated series of knock-on events across front-end and back-end services, trace analytics allows you to discover any issues that may have happened within this sequence of events.

This series of events can be referred to as traces. A set of traces is called a distributed transaction. As one of the main pillars of observability, tracing is vital for monitoring transaction level tracking. Because of the limitations of manual debugging for analysing traces, it is beneficial to use a robust analytics platform for analysing what can often be massive amounts of data. aims to help you discover patterns versus outliers, establish correlations, and derive insights that would otherwise be buried within individual traces.

Common analytics use-cases include service dependency graphs, critical path analysis, error analysis, latency analysis and anomaly detection. In order to troubleshoot high latency or error requests, trace analytics can be used by engineers. Additionally, trace analytics can determine the root cause of code issues by breaking down time spent on CPU, garbage collection, lock contention, and improving CPU utilization. While logs and metrics remain significant, ultimately it's going to be tracing that will solve the transaction processing issues that your system is encountering.

Companies Feel The Difference When They Use

"Internally, has made it easier for us to provide better support for our customers, since finding individual messages based on various data in the payload has become easier.

At Youredi, pretty much everyone from our technical support teams through to our professional services teams uses"

Mats von Weissenberg, CTO @ Youredi

Youredi testimonial

"One thing is certain when operating in the startup space, a strict budget. Before we make any purchase decision to use a SaaS Log Management platform, we need to validate the benefits of the platform and more importantly the team behind the platform.'s customer support team stood out. Right from the beginning, the team was there to answer questions and walk us through the process."

Ioannis Sintos, Co-Founder & CIO - Uizard

Uizard testimonial offers our company an excellent solution of ingesting our logs, we recently had to do quite a few updates on the platform since the previous service owner in our company left and didn't want to do them, was of great assistance throughout the whole process.

Thierry Gysin, Co-Founder & CIO - Ringier

Ringier testimonial

Trace analytics With

Using Trace analytics alongside's hosted OpenSearch solution, you can automatically generate a service map that shows how your systems are connected. The purpose of trace groups is to enable users to group similar traces, monitor performance, and identify issues sooner.

With's deployment tracking features, releasing code is safer and more efficient. Improve code-level visibility across web, mobile, and cloud-native applications, including microservices, serverless, and other modern cloud-native technologies.

Anomalies and outliers can also be flagged in our platform using daily, weekly, and as they happen alerts. trace analytics
pinpoint and troubleshoot

Pinpoint issues with ease

A trace tells a complete story about a request or transaction, whereas metrics provide a high-level view of whether the system works as expected without explaining why. In contrast, logs provide fine-grained details about events such as timestamps and ordering of events.

Using traces over logs is often said to be more sophisticated because you are often able to work with more added context against your events. Trace data can be filtered and visualised based on application, subsystem, service, and action. By using you can analyse tracing data grouped by service to pinpoint exactly where a problem originated by filtering for traces exceeding a given latency.

Data flow and dependency visualisations

As long as there are changes being made to the application, changes being deployed at a fast pace, and changes in user behaviour, there will always be production issues. can help you resolve these issues. Regardless of your role, even if you don't know much about the application, can provide insight into what's causing performance issues.

visualisations for for OTel For OpenTelemetry

OpenTelemetry (OTel) combined with makes end-to-end observability easy. Using OpenTelemetry, engineers can essentially standardize any data coming from any source.

A secure, compliant, and production-ready distribution of the OpenTelemetry project, for OpenTelemetry provides unified analysis and complete centralization of any kind of telemetry data.

Since all major observability vendors are required to support the OpenTelemetry protocol, choosing an analysis service that is already OpenTelemetry-compliant is crucial to future-proofing your operations.

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