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Google Cloud Dataproc VS Panoply

Compare Google Cloud Dataproc VS Panoply 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

Panoply logo Panoply

Panoply is a smart cloud data warehouse
  • Google Cloud Dataproc Landing page
    Landing page //
    2023-10-09
  • Panoply Landing page
    Landing page //
    2023-09-27

Panoply is a smart data warehouse that automates all three key aspects of the data analytics stack: data collection & transformation (ETL), database storage management, and query performance optimization. Panoply empowers anyone working with data analytics to quickly gain actionable insights on their own - without the need of IT and Engineering.

Google Cloud Dataproc videos

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Dataproc

Panoply videos

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Panoply demo: Get faster data analytics in minutes!

Category Popularity

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

Google Cloud Dataproc

Panoply

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Management
0 0%
Data Management
100% 100
Big Data
100 100%
Big Data
0% 0
Data Integration
0 0%
Data Integration
100% 100

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 Panoply

Google Cloud Dataproc Reviews

We have no reviews of Google Cloud Dataproc yet.
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Panoply Reviews

Top 14 ETL Tools for 2023
Panoply is an automated, self-service cloud data warehouse that aims to simplify the data integration process. Any data connector with a standard ODBC/JDBC connection, Postgres connection, or AWS Redshift connection is compatible with Panoply. In addition, users can connect Panoply with other ETL tools, such as Stitch and Fivetran, to further augment their data integration...
Source: www.integrate.io
Top 5 BigQuery Alternatives: A Challenge of Complexity
Although Panoply was developed for data analysts, you don't have to be one to use it. Anyone with a good understanding of SQL can get a data pipeline up and running within a matter of minutes. This frees up your time to focus on analysis, whether you’re running queries directly in Panoply or in your favorite BI tool.
Source: blog.panoply.io
Top ETL Tools For 2021...And The Case For Saying "No" To ETL
Under the hood, Panoply uses a flexible ELT approach (rather than traditional ETL), which makes data ingestion much faster and more dynamic, since you don’t have to wait for transformation to complete before loading your data. And since Panoply builds managed cloud data warehouses for every user, you won’t need to set up a separate destination to store all the data you pull...
Source: blog.panoply.io
Top 7 ETL Tools for 2021
Panoply is an automated, self-service cloud data warehouse that aims to simplify the data integration process. Any data connector with a standard ODBC/JDBC connection, Postgres connection, or AWS Redshift connection is compatible with Panoply. In addition, users can connect Panoply with other ETL tools such as Stitch and Fivetran to further augment their data integration...
Source: www.xplenty.com

Social recommendations and mentions

Panoply 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 / 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

Panoply mentions (3)

What are some alternatives?

When comparing Google Cloud Dataproc and Panoply, 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.

Supermetrics - Supermetrics condenses all the major vectors of data relevant to a user's marketing campaigns and helps them make sense of it all.

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

QuickBI - Export data from over 300 sources to a data warehouse and analyze it with a reporting tool of your choice. Quick and easy setup.

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

Fivetran - Fivetran offers companies a data connector for extracting data from many different cloud and database sources.

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