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Google Cloud Dataproc VS SQream

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

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

Google Cloud Dataproc logo Google Cloud Dataproc

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

SQream logo SQream

SQream empowers organizations to analyze the full scope of their Massive Data, from terabytes to petabytes, to achieve critical insights which were previously unattainable.
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • SQream Landing page
    Landing page //
    2023-09-17

SQream is a data analytics acceleration platform built especially for massive data - from terabytes to petabytes. SQream takes queries down from days to hours and hours to minutes. The SQream platform provides the ability to analyze more data, faster, with multiple dimensions and cuts data preparation significantly by enabling ad-hoc querying on raw data. Leading global organizations in telecommunications, healthcare, ad-tech, retail and more rely on SQream to achieve critical business insights and potentially valuable BI across their massive data stores.

Google Cloud Dataproc videos

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Dataproc

SQream videos

+ Add

SQream DB v2020.1 - Product review and demo

More videos:

  • Review - Introducing SQream DB - The GPU-accelerated data warehouse for massive data
  • Review - SQream DB, GPU-accelerated data warehouse

Category Popularity

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

Google Cloud Dataproc

SQream

Data Dashboard
91 91%
Data Dashboard
9% 9
Big Data
87 87%
Big Data
13% 13
Development
100 100%
Development
0% 0
Big Data Infrastructure
0 0%
Big Data Infrastructure
100% 100

User comments

Share your experience with using Google Cloud Dataproc and SQream. 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 SQream. 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 / 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

SQream mentions (1)

What are some alternatives?

When comparing Google Cloud Dataproc and SQream, 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.

Panoply - Panoply is a smart cloud data warehouse

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

CAKE - CAKE provides a SaaS-based solution for advertisers, publishers and networks to track, attribute and optimize their spend in real-time.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Apache ORC - Apache ORC is a columnar storage for Hadoop workloads.

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