Software Alternatives & Reviews

Google Cloud Dataproc VS Domino Data Lab

Compare Google Cloud Dataproc VS Domino Data Lab and see what are their differences

Google Cloud Dataproc logo Google Cloud Dataproc

Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost

Domino Data Lab logo Domino Data Lab

Domino is a data science platform that enables collaborative and reusable analysis of data.
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • Domino Data Lab Landing page
    Landing page //
    2023-09-13

Google Cloud Dataproc videos

Dataproc

Domino Data Lab videos

TRYING DOMINO'S NO PIZZA MENU! - Chicken Wings, Pasta, & MORE Restaurant Taste Test!

More videos:

  • Review - Domino (2005) Rant aka Movie Review
  • Review - Festool Domino Joiner DF 500 Q Review - 574432

Category Popularity

0-100% (relative to Google Cloud Dataproc and Domino Data Lab)
Data Dashboard
63 63%
37% 37
Business & Commerce
0 0%
100% 100
Big Data
100 100%
0% 0
Development
43 43%
57% 57

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Google Cloud Dataproc and Domino Data Lab

Google Cloud Dataproc Reviews

We have no reviews of Google Cloud Dataproc yet.
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Domino Data Lab Reviews

The 16 Best Data Science and Machine Learning Platforms for 2021
Description: Domino Data Lab offers an enterprise data science platform that allows data scientists to build and run predictive models. The product helps organizations with the development and delivery of these models via infrastructure automation and collaboration. Domino provides users access to a data science Workbench that provides open source and commercial tools for...

Social recommendations and mentions

Based on our record, Google Cloud Dataproc seems to be more popular. 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.

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 / almost 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: about 2 years ago

Domino Data Lab mentions (0)

We have not tracked any mentions of Domino Data Lab yet. Tracking of Domino Data Lab recommendations started around Mar 2021.

What are some alternatives?

When comparing Google Cloud Dataproc and Domino Data Lab, you can also consider the following products

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

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

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

IBM ILOG CPLEX Optimization Studio - IBM ILOG CPLEX Optimization Studio is an easy-to-use, affordable data analytics solution for businesses of all sizes who want to optimize their operations.

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

Composable Analytics - Composable Analytics is an enterprise-grade analytics ecosystem built for business users that want to architect data intelligence solutions that leverage disparate data sources and event data.