dbt
dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow.
Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.
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Verodat
Verodat is a SaaS platform that gathers, prepares, enriches and connects your business data to AI Analytics tools. For outcomes you can trust. Verodat automates data cleansing & consolidates data into a clean, trustworthy data layer to feed downstream reporting. Manages data requests to suppliers. Monitors the data workflow to identify bottlenecks & resolve issues. Generates an audit trail to evidence quality assurance for every data row. Customize validation & governance to suit your organization. Reduces data prep time by 60%, allowing data analysts to focus on insights. The central KPI Dashboard reports key metrics on your data pipeline, allowing you to identify bottlenecks, resolve issues and improve performance. The flexible rules engine allows users to easily create validation and testing to suit your organization's needs. With out of the box connections to Snowflake, Azure and other cloud systems, it's easy to integrate with your existing tools.
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evalship
evalship helps engineering teams test LLM-powered software with a review of their evals at setup and a report on every pull request. The review checks scorers, judges and prompts for flaws, such as a judge that scores 1.0 when its call fails, and gives each flaw a fix with code references. Setup is a prompt for your coding agent: it configures GitHub Actions and opens a setup pull request. On later pull requests, evalship compares results with the default branch, reports the evals a change really broke and the likely cause, sets flaky evals aside, and flags changed prompts or LLM calls that no eval exercises. Its check can block a merge that breaks an eval. It reads results from pytest, Vitest, Jest, promptfoo, Langfuse, Braintrust and LangSmith, with no SDK to add. Evals run in the customer's own GitHub Actions with their own model API keys; evalship only reads the results. Teams get a dashboard with each eval's history and a weekly report by email. Public repositories are free.
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DATPROF
DATPROF Test Data Platform is a complete test data management solution that helps software teams create, protect, provision, and automate high-quality test data. The platform combines data masking, synthetic test data generation, data subsetting, test data provisioning, and automation in one integrated solution.
DATPROF enables organizations to safely use realistic, production-like data for development, testing, QA, and CI/CD pipelines without exposing sensitive or privacy-related information. It helps companies comply with regulations such as GDPR, PCI, and HIPAA while improving software delivery speed and reducing manual test data work.
DATPROF is a software company specialized in test data management. Its mission is to help organizations make test data available faster, safer, and more efficiently, especially in complex enterprise and regulated environments.
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