Software Alternatives & Reviews

Trustgrid Data Mesh Platform VS Azure Databricks

Compare Trustgrid Data Mesh Platform VS Azure Databricks and see what are their differences

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.

Azure Databricks logo Azure Databricks

Azure Databricks is a fast, easy, and collaborative Apache Spark-based big data analytics service designed for data science and data engineering.
  • Trustgrid Data Mesh Platform Landing page
    Landing page //
    2022-08-10
  • Azure Databricks Landing page
    Landing page //
    2023-04-02

Trustgrid Data Mesh Platform videos

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Azure Databricks videos

Azure Databricks is Easier Than You Think

More videos:

  • Review - Ingest, prepare & transform using Azure Databricks & Data Factory | Azure Friday
  • Review - Azure Databricks - What's new! | DB102

Category Popularity

0-100% (relative to Trustgrid Data Mesh Platform and Azure Databricks)
Data Dashboard
57 57%
43% 43
Technical Computing
0 0%
100% 100
Data Integration
100 100%
0% 0
Business & Commerce
0 0%
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 Trustgrid Data Mesh Platform and Azure Databricks

Trustgrid Data Mesh Platform Reviews

We have no reviews of Trustgrid Data Mesh Platform yet.
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Azure Databricks Reviews

10 Best Big Data Analytics Tools For Reporting In 2022
Azure Databricks is a data analytics tool optimized for Microsoft’s Azure cloud services solution. It provides three development environments for data-intensive apps, namely Databricks SQL, Databricks Machine Learning, and Databricks Data Science & Engineering.The platform supports languages including Python, Java, R, Scala, and SQL, plus data science frameworks and...
Source: theqalead.com

Social recommendations and mentions

Based on our record, Azure Databricks seems to be more popular. It has been mentiond 2 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.

Azure Databricks mentions (2)

  • Top 30 Microsoft Azure Services
    In the big data space, Azure offers Azure Databricks. This is an Apache Spark big data analytics and machine learning service over a Distributed File System. The distributed cluster of nodes running analytics and AI operations in parallel allow for fast processing of large volumes of data and integration with popular machine learning libraries such as PyTorch unleash endless possibilities for custom ML. - Source: dev.to / almost 3 years ago
  • ZooKeeper-free Kafka is out. First Demo
    https://azure.microsoft.com/en-us/services/databricks. - Source: Hacker News / about 3 years ago

What are some alternatives?

When comparing Trustgrid Data Mesh Platform and Azure Databricks, 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.

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.

MicroStrategy - MicroStrategy is a cloud-based platform providing business intelligence, mobile intelligence and network applications.

data.world - The social network for data people

Arcadia Enterprise - Arcadia Enterprise is the ultimate native BI for data lakes with real-time streaming visualizations, all without adding hardware or moving data.

Cinchy - Developed for real-time data collaboration, Cinchy Dataware Platform addresses the root cause of data fragmentation and data silos, eliminates the cost and need for time-consuming data integration, and mitigates risks of data duplication.