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... - Source: dev.to / 18 days ago
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. - Source: dev.to / 24 days ago
*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...). - Source: dev.to / about 1 month ago
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. - Source: dev.to / about 1 month ago
Apache Kafka is a distributed streaming platform to share data between applications and services in real-time. - Source: dev.to / about 1 month ago
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. - Source: dev.to / 2 months ago
Our journey with FastStream started when we needed to integrate our machine learning models into a customer's Apache Kafka environment. To streamline this process, we created FastKafka using AIOKafka, AsyncAPI, and asyncio. It was our first step in making message queue management easier. - Source: dev.to / 5 months ago
Inter-Service Communication: Middleware provides communication channels and protocols that enable microservices to communicate with each other. This can include message brokers like RabbitMQ, Apache Kafka, RPC frameworks like gRPC, or RESTful APIs. - Source: dev.to / 5 months ago
This article explains how you can generate and process computer vision events in real-time using Pipeless and Kafka. Pipeless is an open-source computer vision framework to build and deploy apps in minutes. Kafka is a popular OSS distributed event streaming platform. - Source: dev.to / 6 months ago
Whenever a new row is added, a row is updated, or a row is deleted, Debezium notices it immediately. It then packages up these changes and sends them as a continuous stream of events by leveraging the power of Apache Kafka. - Source: dev.to / 6 months ago
As shown above, we are using Apache Kafka as the messaging queue. Each URL will be pushed into the queue as a single event from the URLInputLambda. Next, the ScraperLambdaget each event from the queue to be processed. - Source: dev.to / 7 months ago
Scalable data ingestion is a key aspect for a large-scale distributed search and analytics engine like OpenSearch. One of the ways to build a real-time data ingestion pipeline is to use Apache Kafka. It's an open-source event streaming platform used to handle high data volume (and velocity) and integrates with a variety of sources including relational and NoSQL databases. For example, one of the canonical use... - Source: dev.to / 8 months ago
In recent years, it has become apparent that almost no production system is complete without real-time data. This can also be observed through the rise of streaming platforms such as Apache Kafka, Apache Pulsar, Redpanda, and RabbitMQ. - Source: dev.to / 8 months ago
We know that real-time data is data that is generated and processed immediately, as it is collected from different data sources. Sources can be typical databases such as Postgres or MySQL, and message brokers like Kafka. A real-time data visualization consists of a few steps, first we ingest, then process, and finally show this data in a dashboard. - Source: dev.to / 10 months ago
Although, there are no limitations in using a pure event streaming technology like Kafka. - Source: dev.to / 9 months ago
In the first part of this series, we have seen how to use JR in simple use cases to stream random data from predefined templates to standard out and Apache Kafka on Confluent Cloud. - Source: dev.to / 9 months ago
The use of queues such as Amazon SQS, RabbitMQ or Apache Kafka has been a widely accepted solution for some time. - Source: dev.to / 9 months ago
The second approach is to use a message queue, as some others have suggested. The most powerful of these is probably Kafka, but it's almost certainly overkill. (Technically, Kafka is an event log, not a message queue, but that's semantics at this point). Source: 9 months ago
Apache Kafka is an open-source, distributed event streaming platform with message communication and storage capabilities. Although Kafka is not technically a message queue, it has the functionality of a message queue using a topic partition. - Source: dev.to / 10 months ago
When it comes to the limitations of AWS Step Functions, let us look at what it was doing. Step Functions handled communication between the different steps of their stream quality architecture and error handling. When it comes to communication between services, tools like Kafka exist and can be used to transfer data (or state) between services. Kafka uses a pub/sub (publish and subscribe) messaging model that... - Source: dev.to / 10 months ago
"The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds. This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type." https://kafka.apache.org/. - Source: Hacker News / 10 months ago
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