incubation-engineering
orchest
incubation-engineering | orchest | |
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18 | 44 | |
- | 4,022 | |
- | 0.1% | |
- | 4.5 | |
- | 11 months ago | |
TypeScript | ||
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
incubation-engineering
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Why Postgres RDS didn't work for us
However if you really want to optimize data currently residing in Postgres for analytical workloads, as the original comment suggests - consider moving to a dedicated OLAP DB like ClickHouse.
See results from Gitlab benchmarking ClickHouse vs TimescaleDB: https://gitlab.com/gitlab-org/incubation-engineering/apm/apm...
Key findings:
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Automating Your Homelab with Proxmox, Cloud-init, Terraform, and Ansible
ansible: stage: configure image: alpine rules: - if: $ANSIBLE_SETUP_VM != "" && $ANSIBLE_SETUP_HOST != "" variables: ANSIBLE_HOST_KEY_CHECKING: "False" script: - apk add curl bash openssh python3 py3-pip - pip3 install ansible paramiko - ansible-galaxy collection install -r ansible/requirements.yml - curl --silent "https://gitlab.com/gitlab-org/incubation-engineering/mobile-devops/download-secure-files/-/raw/main/installer" | bash - mkdir /root/.ssh && cp .secure_files/ansible.priv /root/.ssh/id_rsa && chmod 600 /root/.ssh/id_rsa - ansible-playbook ansible/main.yml -i ansible/inventory --extra-vars vyos_host=$ANSIBLE_SETUP_VM --limit $ANSIBLE_SETUP_HOST,$ANSIBLE_SETUP_VM ```
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Float Compression 3: Filters
Interesting to match with the observations from the practice of using ClickHouse[1][2] for time series:
1. Reordering to SOA helps a lot - this is the whole point of column-oriented databases.
2. Specialized codecs like Gorilla[3], DoubleDelta[4], and FPC[5] lose to simply using ZSTD[6] compression in most cases, both in compression ratio and in performance.
3. Specialized time-series DBMS like InfluxDB or TimescaleDB lose to general-purpose relational OLAP DBMS like ClickHouse [7][8][9].
[1] https://clickhouse.com/blog/optimize-clickhouse-codecs-compr...
[2] https://github.com/ClickHouse/ClickHouse
[3] https://clickhouse.com/docs/en/sql-reference/statements/crea...
[4] https://clickhouse.com/docs/en/sql-reference/statements/crea...
[5] https://clickhouse.com/docs/en/sql-reference/statements/crea...
[6] https://github.com/facebook/zstd/
[7] https://arxiv.org/pdf/2204.09795.pdf "SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things" (2022)
[8] https://gitlab.com/gitlab-org/incubation-engineering/apm/apm... https://gitlab.com/gitlab-org/incubation-engineering/apm/apm...
[9] https://www.sciencedirect.com/science/article/pii/S187705091...
- ClickHouse Cloud is now in Public Beta
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Dokter 1.4.0 released
Documentation of rules is now available: https://gitlab.com/gitlab-org/incubation-engineering/ai-assist/dokter/-/blob/main/docs/overview.md
- Dokter: the doctor for your Dockerfiles
orchest
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Decent low code options for orchestration and building data flows?
You can check out our OSS https://github.com/orchest/orchest
- Build ML workflows with Jupyter notebooks
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Building container images in Kubernetes, how would you approach it?
The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/
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Launch HN: Patterns (YC S21) – A much faster way to build and deploy data apps
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
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Argo became a graduated CNCF project
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).
- Ideas for infrastructure and tooling to use for frequent model retraining?
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Looking for a mentor in MLOps. I am a lead developer.
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
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Is there a good way to trigger data pipelines by event instead of cron?
You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation
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How do you deal with parallelising parts of an ML pipeline especially on Python?
We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest
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Launch HN: Sematic (YC S22) – Open-source framework to build ML pipelines faster
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
What are some alternatives?
hadolint - Dockerfile linter, validate inline bash, written in Haskell
docker-airflow - Docker Apache Airflow
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
hookdeck-cli - Receive events (e.g. webhooks) in your development environment
v4
ClickBench - ClickBench: a Benchmark For Analytical Databases
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
databooks - A CLI tool to reduce the friction between data scientists by reducing git conflicts removing notebook metadata and gracefully resolving git conflicts.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
clickhouse-operator - Altinity Kubernetes Operator for ClickHouse creates, configures and manages ClickHouse clusters running on Kubernetes
Node RED - Low-code programming for event-driven applications