Alternatives
Products that do what DataCheck does
CLI-first data validation. YAML config. 27+ built-in rules.
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Every data pipeline job I had to tackle required quite a few components to set up: - One tool to ingest data - Another one to transform it - If you wanted to run Python, set up an orchestrator - If you need to check the data, a data quality tool Let alone this being hard to set up and taking time, it is also pretty high-maintenance. I had to do a lot of infra work, and while this being billable hours for me I didn’t enjoy the work at all. For some parts of it, there were nice solutions like dbt, but in the end for an end-to-end workflow, it didn’t work. That’s why I decided to build an…
2024 · github.com
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2023 · github.com
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Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020
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Hi HN, I made a tiny tool called YAML Validator that checks your YAML files with one Docker command — no installs, no setup, no excuses. It does: - Syntax checks - yamllint linting - checkov security scanning - All in one lightweight, zero-config container --------------------------------------------------------- How to use: docker run -v "$(pwd):/data" pooyanazad/yaml-checker Optional alias (for .bashrc / .zshrc): alias ytest='docker run -v "$(pwd):/data" pooyanazad/yaml-checker' Then simply call: ytest sample.yaml YAML powers CI/CD, configs, infra ,and…
Nov 2025
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qq is jq inspired interoperable config format transcoder with interactive querying. It features an optional interactive editor with autocomplete for structured data. And supports inputs and outputs for json, xml, ini, toml, yaml, hcl, tf, and csv to varying degrees of capability.
2024 · github.com
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2022 · github.com
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2022 · validkube.com
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Hi HN! We’re Clemens and Felix from Cito - thrilled to show you what we’ve built to help data engineers stay on top of data quality issues. Think Datadog meets Incident.io. Tests in dbt are great when checking whether specific expectations are true, but don’t work well for use cases where data patterns may change over time. When relying on testing alone, data teams regularly face situations where business stakeholders identify data issues in dashboards first, eroding trust. In such situations, understanding the implications of an issue and debugging can be a very manual and time-consuming…
2022 · citodata.com
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2022 · github.com
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I built DDL to Data after repeatedly pushing back on "just use production data and mask it" requests. Teams needed populated databases for testing, but pulling prod meant security reviews, PII scrubbing, and DevOps tickets. Hand-written seed scripts were the alternative slow, fragile, and out of sync the moment schemas changed. Paste your CREATE TABLE statements, get realistic test data back. It parses your schema, preserves foreign key relationships, and generates data that looks real, emails look like emails, timestamps are reasonable, uniqueness constraints are honored. No setup, no…
Jan 2026
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2023 · github.com
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I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…
2024 · github.com
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Hey there! We use this currently for releasing the Agent [1] and thought it would be nice to open up broadly for others. [1]: https://docs.datadoghq.com/agent/
2023 · github.com
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Hey HN, I’m the author. I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where "Timesheets" must happen after "Project Start Date," and I wanted to define these rules via natural language. How it works: LLM Layer: Uses Groq/Llama-3.3 to parse a "story" into a JSON schema constraint config. Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity).…
Dec 2025 · github.com
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40+ free tools to convert, format and transform data.
Jun 2026 · datafrog.tools
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2021 · github.com
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Datasino is a command line tool that can come handy when load testing a service or database. Give it a data schema (many compound types supported, including recursive types), an encoding (JSON, CSV or ClickHouse) and a target (a file or a Kafka partition) and a data rate and it will generate a reasonably optimized (native code) random data source. See the top of the README for some examples. If you have any ideas on how to extend this tool so that it's useful to more people, please let me know.
2022 · github.com
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2022 · github.com
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2016 · github.com
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