Artifactory Reviews

Apache Kafka Reviews

Artifactory mentions (20)

• Where to store executables shared by a team/project

  I kind of hate it, but Artifactory seems popular at companies: https://jfrog.com/artifactory/. Source: 11 months ago

• Adding Virtual Environments to Git Repo

  When not providing all dependencies yourself, you might suffer from people deleting the packages you depend on (IMHO a very rare scenario). If it is really that critical (hint: usually it isn't), create a local mirror of Pypi (full or only the packages you need). Devpi, Artifactory, etc. Can do that or you just dump the necessary files into Cloud storage, so you have a backup. Source: about 1 year ago

• Authenticated Docker Hub image pulls in Kubernetes

  Operate a pull-through cache registry, like Artifactory or the open source reference Docker registry. This will allow you to pull images from Docker Hub less frequently, improving your chances of staying under the anonymous usage limit. - Source: dev.to / about 1 year ago

• Concretely, how do you trust the experts? That is, how do you a) identify experts relevant to something and b) determine if they have a consensus (and if so, what it is) on a given topic?

  Like suppose for a second that . . . Idk . . . a product team wants our ci workflows to start using Artifactory. Okay great, I don't know Artifactory integration but I'm going to tell them "Sure, I'll get right on that.". Source: over 1 year ago

• What do I do with large "asset" files?

  If these "assets" have an independent release schedule I would treat them separately (especially if they are externally provided). If they are not built from source then treat them as artefacts, they don't belong in git. You can store the in an artefact repository (like Artifactory of Nexus) or (as u/nekokattt points out) in something like S3. Source: over 1 year ago

Apache Kafka mentions (120)

• Empowering Real-Time Data Pipelines: Leveraging Apache Kafka and Rudderstack

  In today’s fast-paced digital landscape, effective data management and analysis are essential for businesses aiming to stay ahead of the curve. Fortunately, modern tools like Apache Kafka and RudderStack have revolutionized the way we handle and derive insights from large datasets. In this blog post, we’ll explore our experience implementing the Kafka Sink Connector to facilitate seamless event data transfer to... - Source: dev.to / 2 months ago

• 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... - Source: dev.to / 4 months ago

• 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. - Source: dev.to / 4 months ago

• 🦿🛴Smarcity garbage reporting automation w/ ollama

  *Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...). - Source: dev.to / 4 months ago

• 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. - Source: dev.to / 4 months ago