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Trustgrid Data Mesh Platform VS Amazon EMR

Compare Trustgrid Data Mesh Platform VS Amazon EMR and see what are their differences

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Trustgrid Data Mesh Platform logo Trustgrid Data Mesh Platform

A number of software providers have moved to Data Mesh connectivity solutions as they seek to lower the operating costs of their applications.

Amazon EMR logo Amazon EMR

Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
  • Trustgrid Data Mesh Platform Landing page
    Landing page //
    2022-08-10
  • Amazon EMR Landing page
    Landing page //
    2023-04-02

Trustgrid Data Mesh Platform videos

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Amazon EMR videos

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Amazon EMR Masterclass

More videos:

  • Review - Deep Dive into What’s New in Amazon EMR - AWS Online Tech Talks
  • Tutorial - How to use Apache Hive and DynamoDB using Amazon EMR

Category Popularity

0-100% (relative to Trustgrid Data Mesh Platform and Amazon EMR)

Trustgrid Data Mesh Platform

Amazon EMR

Data Dashboard
14 14%
Data Dashboard
86% 86
Data Integration
100 100%
Data Integration
0% 0
Big Data
0 0%
Big Data
100% 100
AI Platform
100 100%
AI Platform
0% 0

User comments

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Social recommendations and mentions

Based on our record, Amazon EMR seems to be more popular. It has been mentiond 10 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.

Trustgrid Data Mesh Platform mentions (0)

We have not tracked any mentions of Trustgrid Data Mesh Platform yet. Tracking of Trustgrid Data Mesh Platform recommendations started around Mar 2021.

Amazon EMR mentions (10)

  • 5 Best Practices For Data Integration To Boost ROI And Efficiency
    There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
  • What compute service i should use? Advice for a duck-tape kind of guy
    I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
  • Processing a large text file containing millions of records.
    This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
  • How to use Spark and Pandas to prepare big data
    Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
  • Beginner building a Hadoop cluster
    Check out https://aws.amazon.com/emr/. Source: about 2 years ago
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What are some alternatives?

When comparing Trustgrid Data Mesh Platform and Amazon EMR, you can also consider the following products

Denodo - Denodo delivers on-demand real-time data access to many sources as integrated data services with high performance using intelligent real-time query optimization, caching, in-memory and hybrid strategies.

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

IBM Cloud Pak for Data - Move to cloud faster with IBM Cloud Paks running on Red Hat OpenShift – fully integrated, open, containerized and secure solutions certified by IBM.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

data.world - The social network for data people

Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost

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