From live feeds and multi-topic views to message sending, data samples, and playbooks, Kafka Streamyard is built for real investigation and manual test workflows.
KS philosophy: Kafka Streamyard is built around a simple idea: give you the easy-to-use features you wish were already in your current Kafka UI. Whether you are a developer, tester, DevOps engineer, or an authorized business user, if something is missing, tell us — we want KS to keep getting better and become the bee's knees.
Build Streamyard Language (SL) filters with autocomplete for JSON messages, keys, and headers. Reuse recent and saved filters across topics and environments, and optionally use OpenAI to help generate queries.
Run many playbacks asynchronously for larger topics while you do other work. Playback results stay persisted so you can come back later, and use the offset function to hone in further on the scope of the data you want to retrieve.
Build the result view you want, choose the columns that matter, and export the selected view as a CSV report using the same columns you see in the grid.
Import and export result data as JSON arrays, JSON objects, and NDJSON, using the same envelope shape across workflows. When valid partition metadata is present, Kafka Streamyard can preserve it on the target topic.
In large systems with thousands of topics, create and save your own filters so the topics you need appear immediately while the rest are ignored with patterns. You can also right-click any topic to reveal a rich set of time-saving tools.
Turn a playback into a quick live feed when you want to watch messages arrive in real time, or let it run and come back later. Use the same SL filtering so the live view stays focused on the messages that matter.
Follow up to 20 live topics at once in one chronological stream, with the same per-topic filtering options, so cross-topic flows interleave naturally in a single investigation view.
Copy topic data (like file manager) between environments, with schema-aware handling, and run repartition tasks when you need a different partition layout on the destination.
Generate line-based playbooks from selected topics, preview them with dry run, and automate create, copy, clear, delete, config, and import-data workflows safely.
Use the Message Details panel to inspect message history, edit key, value, and headers, copy the full KS message format or individual parts, and send messages back to a topic for manual testing.
Save useful messages directly into reusable samples, organise them for the order you want to send them in, and keep manual test workflows ready for replay after a fix.
Use session variables with parent samples, inheritance, and recalc controls so IDs can stay stable across a workflow or regenerate on every send when needed.
Use @@ generators and references across headers, key, and message to create realistic people, orders, invoices, shipments, devices, and more without maintaining hand-written payload files.
Load batches of sample messages with configurable quantity, pacing, bursts, and partitioning strategies so repeatable playback matches the test you are running.
Use the task system to run long operations in the background, including copy topic data (like file manager), export jobs, playbooks, randomized sample loaders, and repartitioning, while keeping progress and output visible in one place.
Audit topic information at a glance, inspect consumer offset controls, ISR data, configuration, and additional Kafka Streamyard metadata or overrides, and keep operational context close while you investigate data and configuration together.
Use comprehensive offset functions to inspect yesterday’s data, absolute time ranges, offsets, head/tail windows, or a specific message count per partition when you need precise investigations over large topics.
Inspect cluster health and disk usage, review all consumers or per-topic lag, reset offsets when needed, troubleshoot Kafka ACLs, and manage Kafka Connect from the same desktop workflow.
Browse schemas, understand payload shape before producing or consuming, and use schema-aware context to inspect data more confidently and seed smarter workflows.
Use SL filters with autocomplete, history, and saving, then move from playback into live feeds and multi-topic live views to focus on exactly the events you care about.
Work with persisted playback results, auto-detect formats, inspect schemas, build result columns, and export JSON, NDJSON, or CSV reports without leaving the investigation flow.
Capture samples, organize favorites, run async playback, create playbooks, copy topic data (like file manager), and track long-running jobs in the background while you work on something else.
Use enhanced topic organisation, manage topics, repartition when needed, inspect consumers and ACLs, and keep investigations open across windows and persisted result sets.
Install the desktop app and connect to your clusters.
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