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Streamfold

Rotel is a high-performance OpenTelemetry collector built in Rust for efficient data handling. It supports metrics, logs, and traces, offering OTLP receivers and exporters alongside Kafka integration for stream processing. This lightweight solution minimizes resource overhead, making it ideal for deployment in performance-critical and resource-constrained environments.

Blacksburg, United StatesFounded 2022310+ followers
Updated 3 months ago

Funding

$3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Product

Problem

Managing telemetry data (logs, metrics, and traces) can be costly and complex for developers, especially when dealing with high volumes of data and multiple destinations. Developers often struggle to optimize costs associated with data ingestion, filtering, and routing, while maintaining full-fidelity archives for compliance and post-incident analysis.

Solution

Streamfold offers a telemetry pipeline platform designed to help developers manage and optimize their telemetry data. The platform allows real-time sampling of events and metrics through ingestion, filtering, and transformation. It enables users to manage costs by sending only relevant data to vendors and storing full-fidelity archives in cost-efficient cloud storage. Streamfold also provides flexible routing, allowing users to connect telemetry data to multiple destinations, empowering different teams with the data they need.

Target Audience

Streamfold is primarily targeted towards developers and engineering teams who need to manage and optimize their telemetry data pipelines for cost efficiency, compliance, and improved observability.

Features

  • Support for common telemetry tools such as OpenTelemetry, Datadog, Elastic, Splunk, and Amazon S3.
  • Flexible routing to connect telemetry data to multiple destinations for observability, product, data science, and customer success teams.
  • Data transformation capabilities to enforce taxonomies, rename fields, parse data, and extract fields using pre-built functions.
  • Real-time visibility into telemetry data with live sampling of events and metrics on ingestion, filtering, transformation, and deliverability.
  • Instant deployment of pipeline changes through API or UI with swift rollback capabilities.
  • Lightweight OpenTelemetry collector implemented in Rust for speed and efficiency (Rotel).
  • Client libraries for Python and Node.js, and an AWS Lambda extension for integration with various environments.
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