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Preset. Power Tool for Data Exploration

Today businesses constantly seek ways to harness vast information to make informed decisions and gain a competitive edge. It is where business intelligence (BI) tools come into play. BI tools provide organizations with the means to transform raw data into actionable insights, empowering decision-makers at all levels to unlock the true potential of their data. With the right BI tool, companies can make data-driven decisions, identify opportunities, mitigate risks, and improve performance.

What is the Preset instrument? is a powerful business intelligence (BI) tool that builds upon the capabilities of Apache Superset, an open-source data exploration and visualization platform. Developed by the company Preset, this tool aims to simplify the process of data analysis and visualization, making it more accessible for business users. With Preset, users can easily connect to various data sources, create interactive dashboards, and explore data through natural language queries. By combining the flexibility of Apache Superset with enhanced features and user-friendly interfaces, Preset empowers organizations to uncover valuable insights and make data-driven decisions quickly.

Key Advantages of Preset

Preset works in the cloud on Python. Its main benefits are:

• a massive selection of databases for summarizing data

• SQL templating

• multilingual interface

• convenient SQL query editor, similar to playground

• a large selection of schedules

• powerful configuration of rights based on roles

• the ability to take a screenshot of each graph or dashboard as a whole

• it has a notification system.

Preset vs Apache Superset

At its core, Preset is the same Apache Superset but in the cloud. Unlike Apache Superset, the Preset product is a commercial service.

Preset provides a low-code platform for creating data-driven applications without the need for extensive coding or development expertise. Some key features of Preset include:

  • it offers a visual interface and a set of pre-built components that allow users to design and develop data applications with minimal coding. It provides a user-friendly environment for creating interactive dashboards, data pipelines, and workflows

  • it enables integration with various data sources and services, including databases, APIs, and cloud platforms. It allows users to connect to different data systems and easily ingest, transform, and load data for analysis and visualization purposes

  • it provides collaboration features that allow teams to work together on data projects. Users can share dashboards, reports, and data applications with others, enabling seamless collaboration and knowledge sharing within an organization

  • it incorporates data governance and security features to ensure the protection and compliance of data assets. It provides access controls, authentication mechanisms, and data privacy settings to manage data access and enforce security policies.

Apache Superset is designed to help users create interactive and customizable dashboards, reports, and data visualizations.

Here are some key features of Apache Superset:

  • it offers a wide range of visualization options, including charts, graphs, and geospatial visualizations. It allows users to explore and analyze data from various sources through interactive visualizations

  • it supports connectivity to a broad range of data sources, including databases, data lakes, and API services. It provides connectors and integrations that enable users to easily connect and retrieve data from different systems

  • it allows users to build interactive dashboards using a drag-and-drop interface. Users can customize and arrange visualizations, apply filters, and create interactive controls to navigate and explore data within the dashboards

  • it provides role-based access controls to manage data access and permissions. It allows administrators to define user roles and permissions at various levels, ensuring that data is accessible only to authorized users

  • it is highly extensible and customizable. It provides a rich set of APIs and hooks that allow developers to extend its functionality and integrate with other systems. Users can also customize the look and feel of Superset to match their specific requirements.

While Preset and Apache Superset have their strengths, there are some potential disadvantages or limitations tools:

Apache Superset disadvantages:

  • you need an experienced DevOps specialist to deploy the application

  • it is necessary to install software packages for databases that are not supported out of the box

  • it is difficult to make changes to the program code and the application interface

  • weak customization of schedules

  • non-functional data export

Preset disadvantages:

  • it is impossible make changes to the application code and interface

  • weak customization of schedules

  • non-functional data export

Both Preset and Apache Superset are powerful tools for data exploration and visualization. However, Preset focuses on providing a low-code platform for building data applications, while Apache Superset specializes in interactive dashboards and visualizations. Depending on your specific needs and preferences, you can choose the tool that best aligns with your requirements in terms of ease of use, extensibility, and the range of features provided.

AWS offers one more service of BI Amazon QuickSight. It is a native cloud-based tool that helps build dashboards, visualize data, find insights and has built-in machine learning capabilities. Points of comparison between QuickSight and Preset are:

  • Both Amazon QuickSight and are cloud-based platforms. QuickSight is an Amazon Web Services (AWS) service, tightly integrated with other AWS offerings like S3 and Redshift., on the other hand, positions itself as an agnostic platform that can integrate with multiple cloud providers and on-premises environments.

  • Both tools offer connectivity to a variety of data sources, such as databases, data warehouses, and cloud storage systems. The specific data sources and integration capabilities may vary between QuickSight and, so it's important to review the supported sources and connectors for each tool.

  • Both tools provide a range of visualization options, allowing users to create charts, graphs, and dashboards. The specific features, customization options, and ease of use may differ between the two platforms. It's worth exploring the capabilities of each tool to determine which one aligns better with your visualization and analytics requirements.

  • Amazon QuickSight is backed by the infrastructure of AWS, which offers robust scalability and performance for handling large datasets and complex queries.

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