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Description

Kafma serves as a desktop GUI client and integrated development environment for Kafka, compatible with macOS, Windows, and Linux. It consolidates the consume–inspect–produce workflow into a single interface, enabling developers to effortlessly browse topics, review records, modify payloads, and send messages without the need to change views. Additionally, Kafma automatically interprets Avro and Protobuf formats, allows for message replaying and forwarding, and provides tools for drafting, comparing, and validating schemas prior to publication. Users can also oversee topics and consumer groups, monitor lag, and reset offsets as needed. The Pro version of Kafma offers the ability to replicate remote topics—including their configurations, schemas, and a specified range of messages—into a local cluster for thorough testing, with an option for field masking. Furthermore, Kafma is compatible with self-hosted Kafka instances, Confluent Cloud, and Amazon MSK, with a Free plan also being accessible for users. This versatility makes Kafma a valuable tool for developers working with Kafka environments.

Description

Auto-complete queries with a built-in Mongo shell. It highlights syntax errors as your type and saves your query history. This is a great tool for beginners and professionals who use MongoDB. Drag-and-drop UI allows you to create complex filter array elements and find() queries. For easier querying and debugging, break down aggregation queries into manageable stages and build them stage-by-stage. Instant code generation in JavaScript (Node.js), Java (2.x. driver API), Python, C# and PHP. You can also generate SQL queries and SQL queries from MongoDB that you can copy into your application. You can save MongoDB imports and exports, data comparisons and migrations as tasks you can run whenever you need them. You can also skip the reminders and set them to run when you need them. You can make changes to your collection's Schema in just a few clicks. This is great for schema performance tuning, restructuring or cleaning up after data migrating.