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Goka VS Google Cloud Dataproc

Compare Goka VS Google Cloud Dataproc and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Goka logo Goka

Goka is a distributed stream processing library for Apache Kafka written in Go.

Google Cloud Dataproc logo Google Cloud Dataproc

Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost
  • Goka Landing page
    Landing page //
    2023-09-18
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09

Goka videos

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2008 BMS GOKA 800cc

More videos:

  • Review - GOKA CATTLE FEED
  • Review - Best medicine for healing cracks | goka jalwatawar malam | review | usage | indication | content

Google Cloud Dataproc videos

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Dataproc

Category Popularity

0-100% (relative to Goka and Google Cloud Dataproc)

Goka

Google Cloud Dataproc

Stream Processing
100 100%
Stream Processing
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Big Data
14 14%
Big Data
86% 86
Databases
100 100%
Databases
0% 0

User comments

Share your experience with using Goka and Google Cloud Dataproc. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Google Cloud Dataproc should be more popular than Goka. It has been mentiond 3 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.

Goka mentions (1)

  • Go and Kafka
    You might want to try: https://github.com/lovoo/goka -- it uses levelDB to keep state from a stream. The application we wrote in-house with goka can process (keeping state) approximately 800+ messages/sec per consumer in a consumer-group. Source: about 3 years ago

Google Cloud Dataproc mentions (3)

  • Connecting IPython notebook to spark master running in different machines
    I have also a spark cluster created with google cloud dataproc. Source: about 1 year ago
  • Why we don’t use Spark
    Specifically, we heavily rely on managed services from our cloud provider, Google Cloud Platform (GCP), for hosting our data in managed databases like BigTable and Spanner. For data transformations, we initially heavily relied on DataProc - a managed service from Google to manage a Spark cluster. - Source: dev.to / about 2 years ago
  • Data processing issue
    With that, the best way to maximize processing and minimize time is to use Dataflow or Dataproc depending on your needs. These systems are highly parallel and clustered, which allows for much larger processing pipelines that execute quickly. Source: over 2 years ago

What are some alternatives?

When comparing Goka and Google Cloud Dataproc, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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

Tigon - Tigon is an open source real-time stream processing framework built on top of Apache Hadoop and Apache HBase.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Lightbend Akka Platform - Lightbend Fast Data Platform is the easy on-ramp to successful streaming Fast Data applications.

HortonWorks Data Platform - The Hortonworks Data Platform is a 100% open source distribution of Apache Hadoop that is truly...

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