threatbus VS orchest

Tenzir | C++, ReasonML, Rust, Python | Hamburg, Germany or Remote (EU timezones) | Open-source | Full-time | https://tenzir.com

Tenzir is an early-stage startup that builds a next generation data-plane for modern Security Operations Centers. It is our mission to help defenders pull ahead by integrating widely used open source tools and building solutions that reduce the time to detect attacks and help with post-mortem investigations. To that end, we develop the high-performance C++ database [VAST](https://github.com/tenzir/vast) with a ReasonML-based frontend that is served by a Rust API. We also develop [Threat Bus](https://github.com/tenzir/threatbus), a dissemination layer for threat intelligence, which orchestrates detection and response products in a publish/subscribe architecture.

We're currently hiring for

Tenzir | Hamburg, Germany| DevOps Platform Engineer | FULL-TIME | REMOTE | €70-80k | https://tenzir.com

Tenzir is seeking an experienced and passionate DevOps / Platform engineer who enjoys bringing open-core security technology into production deployment shape. We cultivate a UNIX-centric mindset: security operators use our high-performance C++ database VAST (https://github.com/tenzir/vast) to hunt in telemetry data, either via the CLI or our ReasonML-based frontend getting its data through a Rust API.

We also develop Threat Bus (https://github.com/tenzir/threatbus), a messaging layer for federating security content.

=== Role & Responsibilities ===

- Improve our CI/CD pipelines for continuous releases with GitHub Actions to build projects of different languages on various platforms and to automate unit and integration testing.

- Automate continuous deployment strategies in different environments, for our own staging and production clusters, but also on-prem (appliances) or with different cloud providers.

- Implement a reliable backend infrastructure for appliance and fleet management, configuration management and multi-layer VPNs.

- Write integrations with other tools from the (security) ecosystem to support a wider range of data formats.

- Be responsible for entire infrastructure segments, from whiteboard design to implementation and automation for production systems.

=== Interview Process ===

1. Fill out the application form at https://tenzir.com/career/devops-platform-engineer/

2. Phone call to get to know each other and identify potential roadblocks (30min)

3. Technical interview(s) (1-2h)

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If you are interested in cutting-edge C++ freelance work, or look for a local sysadmin position, please reach out directly to us at [email protected].

Tenzir | DevOps Platform Engineer | FULL-TIME | €70k | Hamburg, Germany | http://tenzir.com

Tenzir is seeking an experienced and passionate DevOps / Platform engineer who enjoys bringing open-core security technology into production deployment shape. We cultivate a UNIX-centric mindset: security operators use our high-performance C++ database VAST (https://github.com/tenzir/vast) to hunt in telemetry data, either via the CLI our our ReasonML-based frontend getting its data through a Rust API. We also develop Threat Bus (https://github.com/tenzir/threatbus), a dissemination layer for threat intelligence, which orchestrates detection and response.

=== Role & Responsibilities ===

As a key contributor to our infrastructure, you will improve and automate critical processes for building, packaging, and deploying our technology in test and production environments. Concretely:

Tenzir is seeking an experienced and passionate DevOps / Platform engineer who enjoys bringing open-core security technology into production deployment shape. We cultivate a UNIX-centric mindset: security operators use our high-performance C++ database VAST to hunt in telemetry data, either via the CLI our our ReasonML-based frontend getting its data through a Rust API. We also develop Threat Bus, a dissemination layer for threat intelligence, which orchestrates detection and response.

You can check out our OSS https://github.com/orchest/orchest

The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/

First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!

Disclaimer: we're building something very similar and I'm curious about a couple of things.

One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.

Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.

How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.

Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!

For those who are curious, I'm one of the authors of https://github.com/orchest/orchest

Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).

If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest

You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation

We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest

For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.

Disclaimer: author of Orchest https://github.com/orchest/orchest