ApacheKafka
outbox-inbox-patterns
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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.
outbox-inbox-patterns
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Building a Knowledge Base Service With Neo4j, Kafka, and the Outbox Pattern
The following diagram describes the final architecture and that’s exactly what I’ve implemented in this small example project to support this article. KrakenD was used as the API Gateway and all the services were written in Kotlin using the Quarkus framework. people-api and movies-api have each a MongoDB database whereas knowledge-base has a Neo4j database.
What are some alternatives?
dramatiq - A fast and reliable background task processing library for Python 3.
MongoDB - The MongoDB Database
Jenkins - Jenkins automation server
kotlin - The Kotlin Programming Language.
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
spring-cloud-stream-multibinder - 🍀 Spring Cloud Stream Multibinder - Kafka & Kafka Streams
istio - Connect, secure, control, and observe services.
Quarkus - Quarkus: Supersonic Subatomic Java.
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.
Neo4j - Graphs for Everyone
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.
konig-kontext - A globally shared, request scoped, context for JVM based gRPC microservices