Amazon ECR
by Amazon Web Services
Amazon ECR is where many AWS teams keep their container images, and its SigV4-signed APIs describe repositories, image tags, sizes, push and last-pull times, scan findings and lifecycle policies. A Python connector built with Connect Anything pulls that inventory into your Lake, where platform and security teams can see what is stored, what is actually used and what carries known vulnerabilities.
- Category
- Developer Tools
- Type
- Package registries
- Interface
- REST API
- Built with
- Connect Anything
- Credentials
- AWS access keys
02Data
What you can sync.
- Repositories
- Images
- Image scan findings
- Lifecycle policies
Report on Amazon ECR repositories in DataPrism BI and let an AI Mind answer delivery questions in plain words.
Need more? Name any other Amazon ECR data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- AWS access keys
Amazon ECR uses AWS access keys. You add the credentials once, when the AI asks for them, and it tells you where to create them in Amazon Web Services if you need help.
04Questions
Questions you can answer.
Once Amazon ECR data is in your Lake, ask an AI Mind in plain words or build the answer in DataPrism BI.
- Which repositories hold large amounts of image storage that has not been pulled in 90 days?
- How many images that are still being pulled carry critical or high scan findings, and in which repositories?
- Which repositories have no lifecycle policy, or allow mutable tags?
- How often does each repository receive new image pushes, and which ones have gone quiet?
- Which CVEs appear in more than ten repositories at once?
05How it works
How it works in DataMind.
-
Pick Amazon ECR.
Open Lake, pick Amazon ECR in the source picker and name the connection. Connect Anything opens with a ready prompt that points the AI at the Amazon ECR API docs.
-
Answer a few questions.
The AI reads the Amazon ECR documentation, then asks which credentials you will use, which data to sync (for example repositories and images), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
-
Your data lands in Lake.
The connector agent writes a Python connector for Amazon ECR, tests it against the live API and shows you sample rows. Then the data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
06Pairs
Works well with Amazon ECR.
Sources teams join with Amazon ECR in the Warehouse, each one connected the same way.
