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  5. FnordMetric vs Superset

FnordMetric vs Superset

OverviewComparisonAlternatives

Overview

FnordMetric
FnordMetric
Stacks4
Followers15
Votes0
Superset
Superset
Stacks420
Followers1.0K
Votes45

FnordMetric vs Superset: What are the differences?

Developers describe FnordMetric as "Create charts and dashboards from SQL". FnordMetric allows you to write SQL queries that return SVG charts rather than tables. Turning a query result into a chart is literally one line of code. FnordMetric allows you collect and visualize timeseries data using only SQL. It enables you to build beautiful real-time analytics dashboards within minutes. On the other hand, Superset is detailed as "Data exploration and visualization platform, by Airbnb". 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.

FnordMetric and Superset belong to "Business Intelligence" category of the tech stack.

Some of the features offered by FnordMetric are:

  • Line Charts
  • Area Charts
  • Bar Charts

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

  • A rich set of visualizations to analyze your data, as well as a flexible way to extend the capabilities
  • An extensible, high granularity security model allowing intricate rules on who can access which features, and integration with major authentication providers (database, OpenID, LDAP, OAuth & REMOTE_USER through Flask AppBuiler)
  • A simple semantic layer, allowing to control how data sources are displayed in the UI, by defining which fields should show up in which dropdown and which aggregation and function (metrics) are made available to the user

FnordMetric and Superset are both open source tools. Superset with 25.1K GitHub stars and 4.83K forks on GitHub appears to be more popular than FnordMetric with 4.91K GitHub stars and 397 GitHub forks.

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Detailed Comparison

FnordMetric
FnordMetric
Superset
Superset

FnordMetric allows you to write SQL queries that return SVG charts rather than tables. Turning a query result into a chart is literally one line of code. FnordMetric allows you collect and visualize timeseries data using only SQL. It enables you to build beautiful real-time analytics dashboards within minutes.

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.

Line Charts;Area Charts;Bar Charts;Bubble & Scatter Charts;Combined Charts;Metric collection;Local Disk Backend;Send metric data to FnordMetric Server using a simple HTTP interface;Send metric data using the statsd protocol
A rich set of visualizations to analyze your data, as well as a flexible way to extend the capabilities;An extensible, high granularity security model allowing intricate rules on who can access which features, and integration with major authentication providers (database, OpenID, LDAP, OAuth & REMOTE_USER through Flask AppBuiler);A simple semantic layer, allowing to control how data sources are displayed in the UI, by defining which fields should show up in which dropdown and which aggregation and function (metrics) are made available to the user;Deep integration with Druid allows for Caravel to stay blazing fast while slicing and dicing large, realtime datasets;
Statistics
Stacks
4
Stacks
420
Followers
15
Followers
1.0K
Votes
0
Votes
45
Pros & Cons
No community feedback yet
Pros
  • 13
    Awesome interactive filtering
  • 9
    Free
  • 6
    Wide SQL database support
  • 6
    Shareable & editable dashboards
  • 5
    Great for data collaborating on data exploration
Cons
  • 4
    Link diff db together "Data Modeling "
  • 3
    It is difficult to install on the server
  • 3
    Ugly GUI

What are some alternatives to FnordMetric, Superset?

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.

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.

Mode

Mode

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.

Google Datastudio

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

AskNed

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Shiny

Shiny

It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

Redash

Redash

Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Periscope

Periscope

Periscope is a data analysis tool that uses pre-emptive in-memory caching and statistical sampling to run data analyses really, really fast.

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