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Zenaton VS Apache Beam

Compare Zenaton VS Apache Beam and see what are their differences

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Zenaton logo Zenaton

Powerful & Easy Automation for Developers

Apache Beam logo Apache Beam

Apache Beam provides an advanced unified programming model to implement batch and streaming data processing jobs.
  • Zenaton Landing page
    Landing page //
    2021-09-15
  • Apache Beam Landing page
    Landing page //
    2022-03-31

Zenaton videos

+ Add

The Fabulous App is using Zenaton to increase user engagement & build their data pipeline.

More videos:

  • Review - Zenaton lets you build and run workflows with ease

Apache Beam videos

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How to Write Batch or Streaming Data Pipelines with Apache Beam in 15 mins with James Malone

More videos:

  • Review - Best practices towards a production-ready pipeline with Apache Beam
  • Review - Streaming data into Apache Beam with Kafka

Category Popularity

0-100% (relative to Zenaton and Apache Beam)

Zenaton

Apache Beam

Web Service Automation
100 100%
Web Service Automation
0% 0
Big Data
0 0%
Big Data
100% 100
Automation
100 100%
Automation
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

Share your experience with using Zenaton and Apache Beam. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Apache Beam seems to be more popular. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Zenaton mentions (0)

We have not tracked any mentions of Zenaton yet. Tracking of Zenaton recommendations started around Mar 2021.

Apache Beam mentions (14)

  • Ask HN: Does (or why does) anyone use MapReduce anymore?
    The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/9781491983867/. It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.). As for the framework called MapReduce, it isn't used much, but its descendant... - Source: Hacker News / 3 months ago
  • How do Streaming Aggregation Pipelines work?
    Apache Beam is one of many tools that you can use. Source: 5 months ago
  • Real Time Data Infra Stack
    Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow. - Source: dev.to / over 1 year ago
  • Google Cloud Reference
    Apache Beam: Batch/streaming data processing 🔗Link. - Source: dev.to / over 1 year ago
  • Composer out of resources - "INFO Task exited with return code Negsignal.SIGKILL"
    What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all. Source: over 1 year ago
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What are some alternatives?

When comparing Zenaton and Apache Beam, you can also consider the following products

Zapier - Connect the apps you use everyday to automate your work and be more productive. 1000+ apps and easy integrations - get started in minutes.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

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