Software Alternatives, Accelerators & Startups

Dremio VS Impala

Compare Dremio VS Impala and see what are their differences

Dremio logo Dremio

Get more value from your data, faster. Dremio makes your data engineers more productive, and your data consumers more self-sufficient.

Impala logo Impala

Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.
  • Dremio Landing page
    Landing page //
    2023-08-27
  • Impala Landing page
    Landing page //
    2023-04-02

Dremio

Website
dremio.com
$ Details
Release Date
2015 January
Startup details
Country
United States
State
California
Founder(s)
Jacques Nadeau
Employees
100 - 249

Dremio videos

Introduction to Self-Service Data with Dremio

More videos:

  • Review - Data Access for Data Science | Dremio
  • Demo - Dremio Demo | Deploying Dremio AWS Edition Step-by-Step

Impala videos

2016 Chevrolet Impala - Review and Road Test

More videos:

  • Review - 2020 Chevrolet Impala Review | The Final Year
  • Review - Is it the END of the road for the 2019 Chevy Impala?

Category Popularity

0-100% (relative to Dremio and Impala)
Office & Productivity
100 100%
0% 0
Big Data
0 0%
100% 100
Business & Commerce
100 100%
0% 0
Data Dashboard
32 32%
68% 68

User comments

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What are some alternatives?

When comparing Dremio and Impala, you can also consider the following products

Minitab Connect - Minitab Connect is a data management platform that comes with cloud-based data and integration workflows having data governance and integration tools.

Apache Kudu - Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.

Mozart Data - The easiest way for teams to build a Modern Data Stack

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Zaloni Data Platform - Get self-service data from a platform that accelerates business insights. Use data from any source, anywhere: the cloud, on-premises, multi-cloud or hybrid.

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.