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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. AtScale vs Mprove

AtScale vs Mprove

OverviewComparisonAlternatives

Overview

AtScale
AtScale
Stacks25
Followers83
Votes0
Mprove
Mprove
Stacks3
Followers6
Votes0
GitHub Stars329
Forks27

Mprove vs AtScale: What are the differences?

Developers describe Mprove as "Open Source Business Intelligence for BigQuery". Next generation analytics workflow that helps everyone in your company to learn from data faster. On the other hand, AtScale is detailed as "The virtual data warehouse for the modern enterprise". Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

Mprove and AtScale belong to "Business Intelligence" category of the tech stack.

Some of the features offered by Mprove are:

  • Dashboards
  • Charts
  • Filters

On the other hand, AtScale provides the following key features:

  • Multiple SQL-on-Hadoop Engine Support
  • Access Data Where it Lays
  • Built-in Support for Complex Data Types

Mprove is an open source tool with 159 GitHub stars and 8 GitHub forks. Here's a link to Mprove's open source repository on GitHub.

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Detailed Comparison

AtScale
AtScale
Mprove
Mprove

Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

A better workflow for teams. Data Analysts create SQL models. Business users explore models using a simple user interface.

Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment
Dashboards; Charts; Filters; SQL; Git powered workflow; Split SQL into YAML chunks; Reusable SQL blocks; Time zones; Open source
Statistics
GitHub Stars
-
GitHub Stars
329
GitHub Forks
-
GitHub Forks
27
Stacks
25
Stacks
3
Followers
83
Followers
6
Votes
0
Votes
0
Integrations
Python
Python
Amazon S3
Amazon S3
Tableau
Tableau
Power BI
Power BI
Qlik Sense
Qlik Sense
Azure Database for PostgreSQL
Azure Database for PostgreSQL
Clickhouse
Clickhouse
PostgreSQL
PostgreSQL
Google BigQuery
Google BigQuery

What are some alternatives to AtScale, Mprove?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Cube

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

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