Google Cloud Dataflow VS Apache Kafka

Google Cloud Dataflow Reviews

Apache Kafka Reviews

Google Cloud Dataflow mentions (14)

• How do you implement CDC in your organization

  Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 1 year ago

• Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!

  This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 1 year ago

• How are view/listen counts rolled up on something like Spotify/YouTube?

  I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 1 year ago

• Best way to export several GCP datasets to AWS?

  You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 1 year ago

• Why we don’t use Spark

  It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / almost 2 years 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 / about 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 / 3 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 / 3 months ago

• 🦿🛴Smarcity garbage reporting automation w/ ollama

  *Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...). - Source: dev.to / 3 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 / 3 months ago