Local-first SQL workbench for DynamoDB and OpenSearch developers
Run relational SQL queries, JOINs, and aggregations directly against live DynamoDB and OpenSearch data, with an integrated visual canvas and staged write review. DynoTable, created by DynoTable, connects from your Windows desktop to AWS using your own credentials, and presents features such as an AI agent tied to your Bedrock keys, Smart Tables relationship browsing, and autocomplete driven by your table attributes. It targets developers and DBAs who need advanced querying and safer edit workflows.
What can it do with DynamoDB and OpenSearch?
DynoTable exposes relational queries on non-relational stores. The SQL Workbench runs SELECT queries with JOIN, GROUP BY, HAVING, and aggregations (SUM, AVG, COUNT DISTINCT) against DynamoDB, and it also supports OpenSearch SQL with Lucene features. Smart Tables displays related rows visually, and schema export to TypeScript, Zod, or JSON Schema formats maps table layouts to code artifacts.
Does it affect system performance during queries?
Queries execute from the desktop to AWS, keeping data traffic local to your machine. Because the app connects directly to AWS SSO, aws-vault, or IAM, network activity goes between your PC and the cloud endpoint. Staged writes present a reviewable diff before commit, which shifts write validation to the client side and avoids unreviewed bulk edits on the server.
Is it safe to use on a production environment?
The app adopts a local-first privacy model and credential handling tied to your account. The AI Agent runs on your own Amazon Bedrock keys rather than external servers, and the developer states table data does not pass through third-party servers. DynamoDB Local is supported for offline testing, which lets you validate workflows without using production credentials.
Good fit for developers who need SQL on NoSQL, with one notable trade-off
DynoTable suits developers and DBAs who require JOINs or aggregations on DynamoDB and want a local-first workflow with staged write review. It simplifies complex single-table designs via visual Smart Tables and AI tied to Bedrock keys. The trade-off is that advanced SQL operations add conceptual complexity, so teams should plan mappings between relational queries and their underlying NoSQL access patterns.
