Open-source Telemetry Pipelines: An Overview

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    Imagine a well-designed plumbing system with pipes carrying water from a well, a reservoir, and an underground storage tank to various rooms in your house. It will have valves, pumps, and filters to ensure the water is of good quality and is supplied with adequate pressure. It will also have pressure gauges installed at some key points to monitor whether the system is functioning efficiently. From time to time, you will check pressure, water purity, and if there are any issues across the system. 

    Telemetry pipelines work in a similar way. They collect telemetry data from various sources, including applications, servers, databases, devices, or industrial sensors, and take it to sinks such as databases or analytical tools. Along the way, they process the raw data into a usable format, transform and enrich it, while monitoring constantly to ensure streamlined operation. Telemetry pipelines can be deployed on-premise, as SaaS, or in a hybrid setup. They can also be part of monitoring solutions.

    Gartner Research notes that modern workloads generate hundreds of terabytes and even petabytes of telemetry data, and the cost and complexity associated with managing this data can be more than $10 million/year in large enterprises.. Telemetry pipelines help manage and convert large volumes of operational data into business insights.

    Diverse industries use telemetry pipelines to monitor system performance, manage operational data, track application use, and analyze user behavior. These pipelines are critical in modern businesses for optimizing efficiency, enhancing customer experience, and driving strategic decision-making.

    Commercial telemetry pipeline observability products deliver a streamlined implementation and operation. However, several open-source products have also established themselves in this space. This blog discusses the key players in the open-source telemetry pipelines.


    Datadog Vector is a Rust-based tool that is lightweight, fast, and efficient. A popular language today, Rust can be easily integrated with other languages, making it an ideal foundation for your open-source telemetry pipeline management. 

    Vector is easy to install and configure, and very flexible when it comes to integrating seamlessly with several vendors such as Splunk and Elasticsearch Cloud.  Its Vector Remap Language (VRL) is an expression-oriented language designed for secure and performant transformation of logs and metrics. It provides a large number of transforms, and if you are looking for performance, Vector emerges to be the best choice.

    One of the key benefits of Vector is that it is an end-to-end tool, which means that you don’t have to think of other building blocks to provide for pipeline management. You can also extend the functionality of Vector with Mezmo. See how Mezmo goes beyond Vector to support the entire telemetry data lifecycle

    Vector is now a key player in the open-source telemetry pipelines because of its focused functionality and robust security. Its widespread use can be attributed to its capacity to handle heavy workloads and complex use cases. Still, each instance of Vector requires custom deployment and dedicated maintenance. It can be a constraint if you are expecting to scale your operations. It is also difficult to automate, optimize, and monitor. 


    Calyptia Core boasts of fully pluggable architecture for diverse sources and destinations with different inflight processing options. A wide range of connectors and cross-platform working is what makes Calyptia a big player in the open-source telemetry pipelines space. You can process logs, metrics, security, events, and trace data with it securely and efficiently. 

    Calyptia is an ideal choice if you already have Fluent Bit, a lightweight log collector, or Fluentd, a log collector with extensive features. It can integrate with your SIEM toolset with a low-code approach, and you can also leverage its out-of-the-box monitoring and management.

    A major limitation of Calyptia is that it does not support the SaaS model and is available for on-premise deployment only. Also, its future plans are uncertain after the recent acquisition by Chronosphere. 


    Apache Kafka is a scalable, high-availability event streaming platform capable of handling high throughput. Its storage and compute layers offer simplified streaming to manage real-time data. 

    Kafka has a large active user community, and an impressive percentage of top companies in diverse sectors choose Kafka for performance. In addition to client libraries in several programming languages of your choice, you can leverage a vast array of community-driven tooling.

    Given the several manual setup options, Kafka can be overwhelming to deploy, manage, and optimize. Its scalability also comes at the cost of performance. Running Kafka at scale requires dedicated operational resources, and you may find these operational overhead high.

    How Mezmo Offers More

    The field of Telemetry Pipelines is rapidly evolving to harness the intrinsic value telemetry data offers. While the open-source telemetry pipelines benefit from cost advantage and large user communities, commercial solutions like Mezmo go beyond basics to add value and optimize. They also score on automation and better monitoring if you want to manage massive volumes of telemetry data. 

    Mezmo supports an open ecosystem, delivering data profiling to understand data and identify patterns. Working with the philosophy of Understand, Optimize, and Respond to telemetry data, Mezmo empowers you to have confidence in data, improve performance, reduce mental toil, and ensure teams have the right data, optimize your telemetry data, reduce costs, improve performance, and respond rapidly. Request a demo today to see for yourself.

    April Yep


    April has several years of experience in the observability space, leading back to the days when it was called APM, DevOps, or infrastructure monitoring. April is a Senior Product Marketing Manager at Mezmo and loves cats and tea.