DataEase is an open-source business intelligence and data visualization tool, written in Java and licensed under GPL-3.0, built for analysts and business teams who want to explore data and build charts by clicking and dragging rather than by writing code.

What it is

DataEase is an open-source BI tool developed by FIT2CLOUD (飞致云) and distributed under the GNU General Public License version 3. It helps users analyse data quickly and identify business trends so that they can improve and optimise their operations. Analysis is produced through drag-and-drop chart building, and the resulting views can be shared with others. The project sits in the Data & Analytics / Business Intelligence & Reporting category, and its stack combines a Vue.js and Element front end, the AntV chart library, a Spring Boot back end, MySQL for storage, Apache Calcite and Apache SeaTunnel for data processing, and Docker for packaging and deployment.

The concrete problem it solves is the gap between raw data scattered across many systems and a decision-ready dashboard. Instead of exporting files or building a bespoke reporting layer, users connect DataEase directly to the systems they already run: OLTP databases such as MySQL, Oracle, SQL Server, PostgreSQL, MariaDB, Db2, TiDB and MongoDB-BI; OLAP engines such as ClickHouse, Apache Doris, Apache Impala and StarRocks; warehouses such as Amazon RedShift; flat files such as Excel and CSV; and API data sources. It lives in the BI ecosystem and is positioned in its own tagline as an open-source BI tool alternative to Tableau, while its topic list also places it alongside Apache Superset, Kettle and ECharts.

Key capabilities

• Chart and dashboard authoring through mouse clicks and drag-and-drop interaction, with no coding required to complete an analysis.
• Broad data source connectivity covering OLTP databases (MySQL, Oracle, SQL Server, PostgreSQL, MariaDB, Db2, TiDB, MongoDB-BI), OLAP engines (ClickHouse, Apache Doris, Apache Impala, StarRocks), the Amazon RedShift data warehouse, Excel and CSV data files, and API data sources.
• AI-assisted querying through seamless integration with SQLBot, the project's intelligent data question-and-answer component.
• Multiple data sharing methods with an explicit emphasis on keeping shared data secure.
• Multi-platform installation together with diverse embedding support for placing dashboards inside other systems.
• Monthly iteration alongside a zero-barrier installation path, so new builds are obtainable and installable online without a purchase step.
• A data processing layer built on Apache Calcite and Apache SeaTunnel, with Docker used across the deployment path.

Who uses it and how

• Small teams can start on a single Linux server with 2 cores and 4 GB of memory or more, running the published quick-start script as the root user.
• Production environments can instead use the offline installation package, which the README recommends for that case.
• Embedded deployments use the project's embedding support to surface dashboards inside existing applications.
• Self-hosted community users get help through the online documentation, the community forum at bbs.fit2cloud.com, and a WeChat group.
• Teams getting up to speed can use the two-minute introduction video, the accompanying PPT material, and the book 《DataEase 数据可视化分析与实践》.

Getting started

On a Linux server with at least 2 cores and 4 GB of memory, run the one-line script as root: curl -sSL https://dataease.oss-cn-hangzhou.aliyuncs.com/quick_start_v2.sh | bash, then sign in with the username admin and the password DataEase@123456. For production the README recommends the offline installation and upgrade package instead, with further documentation hosted at dataease.cn.

How it compares

DataEase is offered as an open-source alternative to Tableau, the commercial product named in its own tagline. The licence axis separates them: DataEase is GPL-3.0, while the counterpart it is measured against is commercial. The deployment axis separates them too, because DataEase is installed and run on infrastructure the operator controls, using either the quick-start script or the offline package, so the data stays inside the operator's own databases and server. Apache Superset and Kettle also appear in the project's topic list, so anyone evaluating open-source BI will generally weigh DataEase against those as well.

When to use it — and when not to

DataEase suits teams willing to run their own Linux server, MySQL database and the Docker-based stack, including Apache Calcite and Apache SeaTunnel for data processing, because the operator keeps control of both the data and the deployment. It is a poor fit for organisations that cannot self-host, and GPL-3.0 copyleft obligations deserve attention from anyone planning to embed BI inside a proprietary product. Two practical caveats: the README is written primarily in Chinese, and the documented default credentials should be changed immediately after the first login.