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This page provides answers to frequently asked questions (FAQs) about Tenzir.

What is Tenzir?

Tenzir is a platform that pioneers open-source security data pipelines.

Tenzir is also the name of the Germany-based startup behind the product.

What part of Tenzir is open and what part is closed source?

The diagram below illustrates the high-level components, indicating what parts are open and what are closed source:

The majority of code is open source and available at under a permissive BSD 3-clause licence, available at GitHub. This code implements the tenzir and tenzir-node command line tools that execute pipelines and store data. A flexible plugin infrastructure allows for enhancing the open-source core with closed-source plugins.

The Tenzir stack also consists of a cloud platform and web app, accessible via Both of these components are not openly available. The platform is the control plane that manages the fleet of nodes and their pipelines, whereas the nodes are the data plane for compute and storage.

We offer the Community Edition as a binary package that consists of all open-source plugins, plus our own additional closed source plugins, such as a plugin that connects the nodes to our platform.

Can Tenzir see my data?

In theory yes, in practice no. Let us explain.

A Tenzir deployment consists of nodes that you manage, and a platform. The app runs in your browser to access the platform. All computation and storage takes place only at your nodes. The platform acts as rendezvous point that connects two TLS-encrypted channels, one from the node to the platform, and one from the browser to the platform:

We connect these two channels at the platform. Therefore, whoever operates the platform could interpose on data that travels from the nodes to the app. In the Professional Edition and Enterprise Edition, we run the platform. However, we emphasize that data privacy is of utmost importance to us and our customers. As a mission-driven company with strong ethics, our engineering follows state-of-the-art infrastructure-as-code practices and we are performing security audits to ensure that our code quality meets the highest standards.

We have plans to make this a single, end-to-end encrypted channel, so that we no longer have the theoretical ability to interpose on the data transfer between app and node.

If you have more stringent requirements, you can also run the platform yourself with the Sovereign Edition.

Does Tenzir run on-premise?

Yes, Tenzir can run on premise and support fully air-gapped environments. The Sovereign Edition allows you to deploy the entire platform in a dockerized environment, such as Docker Compose.

The Professional Edition and Enterprise Edition are backed by a Tenzir-hosted instance of the platform in the public cloud (AWS in Europe).

Does Tenzir offer cloud-native nodes?

Tenzir currently does not offer cloud-hosted nodes. You can only run nodes in your own environment, including your cloud environment.

However, we offer a cloud-native demo node that you can deploy as part of every account.

Why did you invent yet another query language? Why not SQL?

We opted for our own language for several reasons that we outline below.

At Tenzir, we have a clear target audience: security practitioners. They are rarely also data engineers and fluent in SQL and experienced with lower-level data tools. Rather, they identify as blue/purple teamers, incident responders, threat hunters, detection engineers, threat intelligence analysts, and other domain experts.

We opted for a dataflow language because it easier to comprehend: one step at a time. At least conceptually, because a smart system optimizes the execution under the hood. As long as the observable behavior remains the same, the underlying implementation can optimize the actual computation at will. This is especially noticeable with declarative languages, such as SQL, where the user describes the what instead of the how. A dataflow language is a bit more concrete in that it's closer to the how, but that's precisely the trade-off that simplifies the reasoning: the focus is on a single operation at a time as opposed to an entire large expression.

We spoke to countless security analysts that have decades of experience with SIEMs. Splunk pioneered this space with their Search Processing Language (SPL). Nowadays, Splunk gets a lot of flak for its pricing, but we admire how well it caters to a broad audience of users that are not data engineers. SPL hit the sweet spot of catering to its intended audience. Admittedly, SPL grew over time and perhaps we'd start off differently today. To quote Bjarne Stroustrup:

There are only two kinds of languages: the ones people complain about and the ones nobody uses.

In fact, Splunk released SPL2 as an upgrade to SPL to make it more pipeline-ish. And Elastic also doubled down as well with ES|QL. Nearly every SIEM and adjacent observability tool has its own pipeline language. When we designed our language, we had the fortune of starting with a clean slate and could draw inspiration from others to achieve:

  • the familiarity of splunk
  • the power of Kusto
  • the expressiveness of dplyr
  • the flexibility of jq
  • the ambition of Zed
  • the clarity of PRQL
  • the composability of Nu

These may sound esoteric to some, but they are remarkably well designed evolutions of pipeline languages that are not SQL. In fact, for a given dataflow pipeline there's often an equivalent SQL expression, because the underlying engines frequently map to the same execution model. This gives rise to transpiling dataflow languages to other execution platforms. Ultimately, our goal is that security practitioners do not have to think about any of this and stay in their happy place, which means avoiding context switches to lower-level data primitives.

Our long-term strategy is to support as many language frontends as possible, similar to Databricks building a SIEM-to-Spark transpiler. Emerging projects like substrait and ibis are first attempts. The Composable Data Management System Manifesto puts these efforts in a broader vision. As much as we support this vision, we cannot wait until these fledgling projects reach production-grade quality, forcing us to walk the fine line of rolling our and building on top existing technology, even if it is not always easy to integrate.

Central to our effort is building an open system that's extensible. This is why we leverage Apache Arrow, allowing anyone to hook into a standardized data stream to deploy custom analytics. None of the existing dataflow languages used by security people have this property. Neither did we encounter the capability to write operators that work across multiple schemas in a rich type system. We do not want users to think of tables, but rather domain entities. But SQL puts tables front and center. We'd rather put the concept of tables in the background because security use cases often revolve around high-level concepts. When desired, it's of course possible to restrict the analytics to a specific set of schemas, and this is why our engine offers multi-schema dataflows. This is a novel approach that fuses the ease of use from the world of document-oriented engines and combines it with the power of engines that operate on semi-structured data.

Finally, we wanted to give users the ability to express both streaming and batch processing workloads in the same language. In particular, we designed the language so that switching between a historical query and live streaming is a matter of logically exchanging the pipeline input.

To support all of these ideas elegantly with a compelling user experience, we needed a new language.

What database does Tenzir use?

Tenzir does not rely on a third-party database.

Tenzir nodes include a light-weight storage engine on top of partitioned Feather or Parquet files, accessible via the import and export operators. The engine comes with a catalog that tracks meta data and a thin layer of sketches to accelerate queries.

Our tuning guide has further details on the inner workings.

Does a Tenzir node run on platform X?

We currently support the platforms that we mention in our deployment instructions.

For any other platform, the answer is most likely no. While we would love to support a wide variety of platforms, we are still a small team with limited engineering bandwidth. Please talk to us to let us know what is missing and consider contributing support for additional platforms to our open source project.

Do you have an integration for X?

Our integrations page includes descriptions of use cases with third-party products and tools. If X is not in that list, it does not mean that X is not supported. The steps below help you understand whether there exists an integration:

  1. Check the available formats. Sometimes an integration is just a lower-level building block, such as the Syslog parser.
  2. Check the available connectors. An integration can also be generic communication primitive, such as the AMQP that acts as client to speak with a RabbitMQ server, or the HTTP connector to perform an API call.
  3. Check Fluent Bit inputs and outputs. Our fluent-bit operator makes it possible to use the entire ecosystem of Fluent Bit integrations.
  4. Call a command-line tool. It is always possible to integrate a command line tool using the shell operator, by hooking standard input and output of a forked child as a byte stream into a pipeline.
  5. Use Python. The python operator allows you to perform arbitrary event-to-event transformation using the full power of Python.

Please do not hesitate to reach out to us if you think something is missing, by opening a GitHub Discussion or swinging by our Discord server.