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  5. AtScale vs Mode

AtScale vs Mode

OverviewDecisionsComparisonAlternatives

Overview

Mode
Mode
Stacks125
Followers227
Votes17
AtScale
AtScale
Stacks25
Followers83
Votes0

Mode vs AtScale: What are the differences?

What is Mode? SQL-based analytics tool that helps analysts query, visualize, and share data. Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

What is AtScale? 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.

Mode and AtScale can be categorized as "Business Intelligence" tools.

Some of the features offered by Mode are:

  • Write, save, and share SQL queries with other analysts in your company. Empower non-technical folks to update queries on their own. Run queries on a schedule, create lists of related reports, and explore a project's history as it changes over time.
  • Build reports using standard charting or create completely customer, interactive visuals with HTML, CSS, and Javascript
  • Database connectors for MySQL, Postgres, Redshift, Vertica, Hive, Heroku, Segment, BigQuery, Impala.

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

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Advice on Mode, AtScale

Wei
Wei

CTO at Flux Work

Jan 8, 2020

Decided

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

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Comments

Detailed Comparison

Mode
Mode
AtScale
AtScale

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

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

Write, save, and share SQL queries with other analysts in your company; Empower non-technical folks to update queries on their own; Run queries on a schedule, create lists of related reports, and explore a project's history as it changes over time; Build reports using standard charting or create completely customer, interactive visuals with HTML, CSS, and Javascript;Database connectors for MySQL, Postgres, Redshift, Vertica, Hive, Heroku, Segment, BigQuery, Impala; Mode also offers SQL School (sqlschool.modeanalytics.com), a free, interactive SQL tutorial and the Mode Playbook.
Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment
Statistics
Stacks
125
Stacks
25
Followers
227
Followers
83
Votes
17
Votes
0
Pros & Cons
Pros
  • 4
    Empowering for SQL-first analysts
  • 3
    Collaborative query building
  • 3
    Easy report building
  • 2
    Integrated IDE with SQL + Python for analysis
  • 2
    In-app customer chat support
No community feedback yet
Integrations
Apache Hive
Apache Hive
Microsoft Azure
Microsoft Azure
Google BigQuery
Google BigQuery
Apache Impala
Apache Impala
Amazon Redshift
Amazon Redshift
PostgreSQL
PostgreSQL
Segment
Segment
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server
Python
Python
Amazon S3
Amazon S3
Tableau
Tableau
Power BI
Power BI
Qlik Sense
Qlik Sense
Azure Database for PostgreSQL
Azure Database for PostgreSQL

What are some alternatives to Mode, AtScale?

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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