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Products that do what VALISOLUTION Data Validator does

Automate data validation across databases in minutes

  1. 1AB

    I created a web page to compare different analytical databases (both self-managed and services, open-source and proprietary) on a realistic dataset. It contains 20+ databases, each with installation and data loading scripts. And they can be compared to each other on a set of 43 queries, by data load time or by storage size. There are switches to select different types of databases for comparison - for example, only MySQL compatible or PostgreSQL compatible. If you play with the switches, many interesting details will be uncovered. Full description:…

    2022 · benchmark.clickhouse.com

  2. 2WO

    Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent…

    2024 · github.com

  3. 3SD
  4. 4DD

    Gleb, Alex, Erez and Simon here – we are building an open-source tool for comparing data within and across databases at any scale. The repo is at https://github.com/datafold/data-diff, and our home page is https://datafold.com/. As a company, Datafold builds tools for data engineers to automate the most tedious and error-prone tasks falling through the cracks of the modern data stack, such as data testing and lineage. We launched two years ago with a tool for regression-testing changes to ETL code…

    2022

  5. 5IB

    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

  6. 6DO
  7. 7
    Evidence113

    Beautiful reports with just SQL and markdown

    2021

  8. 8PD

    2017 · pydantic-docs.helpmanual.io

  9. 9

    The fastest way to build your data warehouse

    2023

  10. 10
    soarSQL98

    The analytical SQL editor - powered by duckDB

    2025

  11. 11

    Connect DecisionBox to your Databricks to validate findings

    May 2026 · decisionbox.io

  12. 12

    Bulletproof your GraphQL API

    2019

  13. 13AU
  14. 14VA
  15. 15DT

    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

  16. 16DS

    I played around with GPT-3 to build this demo. Select a public BigQuery dataset and describe your query in natural English, then edit the generated SQL as needed and execute it. https://app.tabbydata.com/sql-assistant-demo

    2021

  17. 17ST

    Hi HN — we've built a testing framework for Supabase that spins up fast, isolated Postgres databases for each test case. It’s designed to make RLS policies easy to validate with real database state, without global test fixtures or mock auth. Features: - Instant isolated Postgres DBs per test - Automatic rollback after each test - RLS-native testing with `.setContext()` for auth simulation - Flexible seeding (SQL, CSV, JSON, JS) - Works with Jest, Mocha, and any async test runner - CI-friendly (runs cleanly in GitHub Actions) We also published example projects and a free set of tutorials:…

    Nov 2025 · npmjs.com

  18. 18VJ
  19. 19DT
  20. 20AE

    This C library is part of a main project aimed at providing a reactive key-value (KV) database. The data is typed (numbers, strings, dates, or booleans) and can include formulas with references to other entries. Clients connected to this database receive a real-time data stream with updates to the subscribed keys, allowing them to react to changes and their dependencies. Essentially, it’s like building a distributed Excel, where data and formulas dynamically update across the system. I couldn’t find any libraries that offered the full set of features I needed for evaluating expressions, so I…

    2024 · github.com

  21. 21

    Your AI is only as good as your data. Prove it.

    Mar 2026 · provero.org

  22. 22PA
  23. 23VA

    Wrote this to learn more about the `chumsky` parser combinator library, rustyline, and the `ariadne` error reporting crate. Such a nice DX combo for writing new languages. Still a work in progress, but I thought I'd share :)

    2025 · github.com

  24. 24

    CLI-first data validation. YAML config. 27+ built-in rules.

    Feb 2026

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