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  5. GoodData vs Superset

GoodData vs Superset

OverviewComparisonAlternatives

Overview

GoodData
GoodData
Stacks28
Followers44
Votes0
Superset
Superset
Stacks420
Followers1.0K
Votes45

GoodData vs Superset: What are the differences?

Introduction

Key differences between GoodData and Superset are:

  1. Architecture: GoodData is a cloud-based business intelligence platform that provides end-to-end analytics capabilities with a strong focus on data governance and security. On the other hand, Superset is an open-source data visualization tool that offers a flexible and customizable approach to building interactive dashboards and visualizations. GoodData provides a managed service where data processing and visualization are handled by the platform, whereas Superset requires users to set up and manage their own infrastructure.

  2. Data Source Compatibility: GoodData offers native connectors to popular data sources like Salesforce, Google Analytics, and more, simplifying the process of integrating data from various sources. In contrast, Superset provides support for connecting to a wide range of data sources through SQLAlchemy, making it more suitable for organizations with diverse data ecosystems that require integration with multiple data platforms.

  3. User Interface and Visualization Capabilities: GoodData has a user-friendly interface with pre-built templates and a focus on ease of use for business users. It offers advanced visualization capabilities and interactive dashboards tailored towards decision-makers. Superset, being an open-source tool, allows for greater customization and flexibility in building visualizations. It provides a rich set of visualization options and empowers users to create their own custom visualizations through SQL queries.

  4. Collaboration Features: GoodData includes features for collaboration and sharing insights within teams, such as commenting, annotation, and data story authoring. Conversely, Superset focuses on providing a collaborative environment through shared dashboards and interactive data exploration functionalities. Users can share dashboards and analyses with others in real-time, enabling collaborative decision-making processes.

  5. Licensing Model: GoodData operates on a subscription-based model with pricing based on the number of users and features required. In contrast, Superset is an open-source tool released under the Apache License 2.0, making it free to use and customize without any licensing fees. This difference in licensing models can influence the cost-effectiveness and scalability of adopting either platform based on the organization's budget and requirements.

  6. Security and Compliance: GoodData places a strong emphasis on data security and compliance with regulations like GDPR, HIPAA, and others. It provides robust security features such as encryption, access controls, and audit trails to ensure data protection. On the other hand, Superset's security capabilities may vary depending on the deployment environment and the measures implemented by the users to secure their data and infrastructure. Organizations dealing with sensitive data may prefer GoodData for its comprehensive security mechanisms.

In Summary, the key differences between GoodData and Superset lie in their architecture, data source compatibility, user interface, collaboration features, licensing model, and security and compliance focus.

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Detailed Comparison

GoodData
GoodData
Superset
Superset

Get a closer look at all your business data at the same time so you can gain actionable insight into sales, marketing, customer engagement and more.

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.

Track all of your important business data in one place at the same time. Graphs and charts analyze changes in revenue, performance, website traffic, customer activity and more, and match these against your business goals.;Automatically segment your audiences, products, customer feedback and other important categories related to your business.;Set indicators to monitor the status of your business goals throughout the quarter. Goal indicators alert you when you’re on target, off pace and when something needs more attention to keep you on track, minimizing potential risks and surprises.;Easily understand correlations between key business metrics: Does more effort and cost drive higher value deals? Which marketing lead sources have the highest ROI and conversion?;Measure activity at every stage of the sales funnel to identify which actions were taken, when they were taken and how many people took those actions. Compare these changes against previous weeks and set goals to evaluate the effectiveness of your campaign over a specific period of time.;Display multiple sets of related data for easy comparison. Using this information, you can identify differentiators and opportunities, and eliminate variables in your decision-making process.
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
28
Stacks
420
Followers
44
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 GoodData, 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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