| description | cover | coverY |
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The source of Memphis.dev |
.gitbook/assets/Banner- Memphis.dev streaming .jpg |
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When your application requires a message broker or a queue,
Implementing one will require you to -
- Build a dead-letter queue, create observability, and a retry mechanism
- Build a scalable environment
- Create client wrappers
- Tag events to achieve multi-tenancy
- Enforce schemas and handle transformations
- Handle back pressure. Client or queue side
- Configure monitoring and real-time alerts
- Create a cloud-agnostic implementation
- Create config alignment between production to a dev environment
- Spent weeks and months learning the internals through archival documentation, ebooks, and courses
- Onboard your developers
And the list continues...
Memphis.dev is more than a broker. It's a new streaming stack.
It significantly accelerates the development of real-time applications that require a streaming platform with high throughput, low latency, easy troubleshooting, fast time-to-value,
minimal platform operations, and all the observability you can think of.
- Memphis Broker. A distributed engine, which also acts as the primary storage layer for produced events or data.
- Memphis Schemaverse. Schema management and enforcement tool built within Memphis to help users increase data quality and avoid upstream breaks.
- Memphis Functions. Developer-first serverless stream processing to transform and enrich ingested events on-the-fly.
- Memphis Connect. A modular framework to enable fast pull and push of data to and from different sources and destinations.
- Reliability - Queues and brokers are a mission-critical component in the modern application architecture and should be highly available and stable as possible.
- Performance and Efficiency - Provide great performance while maintaining efficient resource consumption.
- Developer Experience - Enable rapid development and ultra-short time-to-production.
- Observability - Increase observability, integrations with 3rd-party monitoring tools, real-time notifications, stream lineage, and therefore troubleshooting time reduction.
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- Async task management
- Real-time streaming pipelines
- Data ingestion
- Async communication between services on k8s
- Queuing
- Multiple destinations to a single message
- Ingest Grafana loki logs
- Stream video frames