streamify
ApacheKafka
| streamify | ApacheKafka | |
|---|---|---|
| 4 | 104 | |
| 474 | 28 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | 5 months ago | |
| Python | ||
| - | - |
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streamify
- Where can I find online projects end-to-end?
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Completed my first Data Engineering project with Kafka, Spark, GCP, Airflow, dbt, Terraform, Docker and more!
Here is link number 1 - Previous text "Git"
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?
eventsim - Event data simulator. Generates a stream of pseudo-random events from a set of users, designed to simulate web traffic.
dramatiq - A fast and reliable background task processing library for Python 3.
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
outbox-inbox-patterns - Repository to support the article "Building a Knowledge Base Service With Neo4j, Kafka, and the Outbox Pattern"
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Jenkins - Jenkins automation server
eventsim - Event data simulator. Generates a stream of pseudo-random events from a set of users, designed to simulate web traffic.
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
finnhub-streaming-data-pipeline - Stream processing pipeline from Finnhub websocket using Spark, Kafka, Kubernetes and more
istio - Connect, secure, control, and observe services.
tfl-bikes-data-pipeline - Processing TFL data for bike usage with Google Cloud Platform.
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.