jr
ApacheKafka
jr | ApacheKafka | |
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4 | 104 | |
114 | 28 | |
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9.4 | 0.0 | |
2 months ago | 5 months ago | |
Go | ||
MIT License | - |
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jr
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JR, quality Random Data from the Command line, part II
We have seen how to use JR in more advanced use cases, streaming quality random data with referential integrity. In the next part of this series, we will see how to use REST apis with JR. In the meanwhile, happy streaming!
- FLaNK Stack Weekly for 22 May 2023
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JR, quality Random Data from the Command line, part I
Datagen is the de-facto standard to generate random data for Kafka. But customising what's generated is not something you can do in 30 seconds, and enabling compression is currently not an option with the managed connectors. So I decided to write a tool which you could use to easily start streaming random data to kafka in seconds, and that's why JR was born. With the help of some friends and colleagues we packed JR with a lot of features (and many more coming!)
ApacheKafka
- PubNubとIFTTTによるSMS通知システム
- PubNub 및 IFTTT를 사용한 SMS 알림 시스템
- Système de notification par SMS avec PubNub et IFTTT
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Wie man Ereignisse von PubNub zu RabbitMQ streamt
Senden an Kafka (d. h. Senden der Daten an Apache Kafka)
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.
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How to Use Reductstore as a Data Sink for Kafka
Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.
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🦿🛴Smarcity garbage reporting automation w/ ollama
*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...)
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How to Build & Deploy Scalable Microservices with NodeJS, TypeScript and Docker || A Comprehesive Guide
RabbitMQ comes with administrative tools to manage user permissions and broker security and is perfect for low latency message delivery and complex routing. In comparison, Apache Kafka architecture provides secure event streams with Transport Layer Security(TLS) and is best suited for big data use cases requiring the best throughput.
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Easy Guide to Integrating Kafka: Practical Solutions for Managing Blob Data
Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
What are some alternatives?
TornadoVM - TornadoVM: A practical and efficient heterogeneous programming framework for managed languages
dramatiq - A fast and reliable background task processing library for Python 3.
kcat - Generic command line non-JVM Apache Kafka producer and consumer
outbox-inbox-patterns - Repository to support the article "Building a Knowledge Base Service With Neo4j, Kafka, and the Outbox Pattern"
docker-compose-viz - Docker compose graph visualization
Jenkins - Jenkins automation server
junodb - JunoDB is PayPal's home-grown secure, consistent and highly available key-value store providing low, single digit millisecond, latency at any scale.
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
flink-statefun - Apache Flink Stateful Functions
istio - Connect, secure, control, and observe services.
schema-registry-statistics - Schema Registry Statistics Tool
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.