Software Alternatives & Reviews

Google Cloud Dataproc VS Data Studio

Compare Google Cloud Dataproc VS Data Studio 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

Data Studio logo Data Studio

Data Studio is a data transforming platform that allows businesses or users to convert their client’s data into useful reports through data visualization.
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • Data Studio Landing page
    Landing page //
    2022-12-05

Google Cloud Dataproc videos

Dataproc

Data Studio videos

Google Data Studio Explained in 100 seconds

More videos:

  • Review - The Best BI Tool For Beginners? - Google Data Studio Review
  • Review - Tableau vs Google Data Studio: Pros and Cons + My Recommendations

Category Popularity

0-100% (relative to Google Cloud Dataproc and Data Studio)
Data Dashboard
85 85%
15% 15
Business Intelligence
0 0%
100% 100
Big Data
100 100%
0% 0
Development
100 100%
0% 0

User comments

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Social recommendations and mentions

Data Studio might be a bit more popular than Google Cloud Dataproc. We know about 3 links to it since March 2021 and only 3 links to Google Cloud Dataproc. 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: over 2 years ago

Data Studio mentions (3)

  • Adding Series Lines to a Stacked Bar Chart
    The service formerly known as Google Data Studio might have a suitable option, or you can make your own if you have (or have access to) JavaScript and CSS expertise. Alternatively, you might be able to approximate the effect with the combo chart option, but getting the formatting right would be a nightmare. Source: 12 months ago
  • Google Cloud Reference
    Data Studio: Collaborative data exploration/dashboarding 🔗Link 🔗Link. - Source: dev.to / over 1 year ago
  • Should the subreddit allow posts about Data Studio?
    Data Studio is Google's business intelligence tool for building interactive reports and visualizations with a drag-and-drop interface. It can connect to Google Sheets as a data source, which makes it really easy to build fancy reports with filters and charts. Source: almost 2 years ago

What are some alternatives?

When comparing Google Cloud Dataproc and Data Studio, 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.

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

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

Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...

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

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.