At DataRock Labs, we believe that the journey of information from raw data to business value should be transparent. By combining cutting-edge data platform technologies with AI solutions, we ensure that you get the most out of your data.

Data rocks. But it's often dirty, fragmented, and difficult to analyze. We can help you get the most out of your data by cleaning it up, organizing it, and making it easy to understand.

AI rocks. And the first steps are easy, but valuable AI services in production is a whole different ballgame, especially at enterprise scale. Our AI solutions are designed to provide reliable long-term support for your business processes.

BI rocks. But it's completely useless if it isn't in the right place, at the right time, in the right format. We excel in extracting valuable insights from data, enabling informed decision-making and enhancing your business's efficiency.

Great Expectations is a Python library that helps you write tests to validate data in your pipelines, ensuring that it meets your expectations. It allows you to define expectations for various data sources and formats, such as databases, CSV files, or JSON files, and then run those expectations as tests. This can be particularly useful in data engineering and data science workflows to ensure data quality and consistency throughout the pipeline. Great Expectations provides a flexible and expressive way to specify these expectations and integrate them seamlessly into your existing codebase.

We have 50 merged Pull Requests in the Great Expectations Project 🚀 View our merged Pull Requests

Run your dbt Core projects as Apache Airflow DAGs and Task Groups with a few lines of code. Benefits include:

• Run dbt projects against Airflow connections instead of dbt profiles
• Native support for installing and running dbt in a virtual environment to avoid dependency conflicts with Airflow
• Run tests immediately after a model is done to catch issues early
• Utilize Airflow’s data-aware scheduling to run models immediately after upstream ingestion
• Turn each dbt model into a task/task group complete with retries, alerting, etc.

We have 3 merged Pull Requests in the Astronomer Cosmos Project View our merged Pull Requests

CodiumAI is leveraging its know-how in order to provide developers with an AI agent tool aiming to help developers review PRs faster and more efficiently. It automatically analyzes the commits and the PR and can provide several types of feedback:

Auto-Description: Automatically generating PR description - name, type, summary, and code walkthrough.
PR Review: Feedback about the PR main theme, type, relevant tests, security issues, focused PR, and various suggestions for the PR content.
Question Answering: Answering free-text questions about the PR.
Code Suggestion: Committable code suggestions for improving the PR.

We have 2 closed Pull Requests in the Codium PR-Agent Project View our merged Pull Requests