2023 Best Open-Source Business Intelligence (1): Open-source BI tools

TL;DR: what is an open-source BI tool & some modern examples?

1. Open-source BI does not necessarily mean free.

2. It's important to distinguish between BI toolkits, BI tools, and BI platforms.

3. Most Popular open-source business intelligence tool: Grafana

4. Most Popular open-source data integration: Airbyte

5. Most Popular open-source data pipeline: Airflow

6. Most Popular open-source charting & dashboard tool: Dash by Plotly

7. You should choose an open-source BI only after you assess your resources.

8. Our open-source BI solution: Rocket.BI

Our most recent blog post discussed the importance of businesses utilizing Business Intelligence to make the most of their data. While many organizations want to manage their data and analytics themselves, starting from scratch's not always necessary. Nowadays, businesses can use open-source solutions to accelerate building their business intelligence platform. Some of these solutions can enable companies to create their BI suite in days or hours. Let's take a closer look at these options.

Introduction to Open-source Business Intelligence

BI tools are crucial for businesses to extract valuable insights from data and make informed decisions. Open-source alternatives provide flexibility and cost-effectiveness, making them a viable option. We'll explore some of the best open-source BI tools known for their capabilities, user-friendliness, and community support. First, let's review some fundamental definitions:

• Open-source software is freely available for access, use, modification, and distribution. It promotes collaboration among developers and encourages transparency and innovation in the tech industry. It's often free to use, making it accessible to many users and decentralizing software development. More information on license terms can be found on Wikipedia.

• Open-source business intelligence software falls into three categories: toolkits, tools, and platforms. Toolkits are software development frameworks that help developers create applications quickly. The most popular toolkit for data analytics is data visualization. BI tools perform specific tasks such as data integration or chart building. BI platforms handle multiple analytics tasks or an entire workflow to help businesses make data-driven decisions.

• Calculating the cost of open-source business intelligence (BI) can be difficult due to different categories and licensing terms. Expenses include data hosting, development, training, and maintenance. Some solutions may not handle large data volumes or complex queries, requiring more advanced options.

How do we select the Top open-source business intelligence?

After hours of research, we have compiled a list, including reviewing feedback and examining the corresponding Github repositories. The products on this list are considered top-tier based on these methods.

1. Definition: (1) The tool must be open-source, whether free or not. The code should be easily accessible on Github (SourceForge for older tools) or their website. However, please let me know if you know the awesome tools I'm missing. (2) The main purpose is daily business use, so data science tools will not be included. Simply put, we don't use a cannon to shoot sparrows. (3) We said tool(s), so no toolkits or code library. Let's have a separate blog for it.

2. Popularity: When determining a project's success, popularity is a crucial factor. Our approach involves analyzing three metrics: (1) the number of forks, (2) the number of stars, and (3) the trend in search volume.

3. Personal bias: After conducting thorough research for this ranking list, I knew why certain solutions had been mentioned in numerous blog articles despite being 10-15 years old. Therefore, I prioritize open-source projects with less engineering demands (preferably using JavaScript or Python over Java), modern input-output (ready for cloud integration), and intuitive design (not look like the dot com bubble era).

Most Popular open-source business intelligence tools

In the dynamic world of business intelligence, open-source tools have emerged as powerful allies in transforming data into actionable insights. These tools offer flexibility, customizability, and cost-effectiveness, making them an attractive choice for businesses of all sizes. In this blog post, we'll explore some of the most popular open-source business intelligence tools that have gained recognition for their capabilities and functionalities. This is an Alphabet list:

1. Airbyte

Airbyte is an open-source data integration platform facilitating seamless data extraction, transformation, and loading (ETL) processes. With support for various data sources, Airbyte empowers businesses to connect and sync their data efficiently. It's user-friendly interface and robust architecture make it an ideal choice for data engineers and analysts. Airbyte GitHub

2. Airflow

Apache Airflow is a powerful platform for orchestrating complex data pipelines and workflows. It allows users to define, schedule, and monitor workflows as code, enabling automated and scalable data processing. Airflow's extensive ecosystem and active community support have increased popularity among data engineers and DevOps professionals. Airflow GitHub

3. Dagster

Dagster is a cloud-native orchestration platform for data pipelines. It provides a flexible and composable framework for defining and managing complex data workflows. With support for data lineage, versioning, and monitoring, Dagster ensures data reliability and reproducibility across the pipeline. Dagster Github

4. Dash by Plotly

Dash by Plotly is an open-source Python framework for building web applications for data visualization. It seamlessly integrates with Plotly, allowing users to create interactive and responsive dashboards with Python code. Dash's simplicity and versatility have made it popular for data analysts and developers on data-driven web applications. Dash GitHub

5. Grafana

Grafana is an open-source analytics and monitoring platform that allows users to create interactive and customizable dashboards. It supports many data sources, including databases, cloud services, and even IoT devices. Grafana's rich visualization options and alerting capabilities have made it a favorite among data analysts and system administrators. Grafana Github

6. Kafka

Apache Kafka is a distributed streaming platform that handles real-time data feeds and processing. It is widely used for building scalable and reliable event-driven architectures. Kafka's ability to handle high-throughput data streams has become a core component in many big data and data integration pipelines. Kafka Github

7. Kibana

Kibana is an open-source data visualization platform explicitly designed for Elasticsearch. It allows users to explore, visualize, and analyze data stored in Elasticsearch indices. Kibana's ability to create real-time visualizations and dashboards makes it a powerful tool for monitoring and analyzing logs and metrics. Kibana Github

8. Prefect

Prefect is a workflow orchestration tool that empowers developers to build, observe, and react to data pipelines. With its intuitive API and robust scheduling capabilities, Prefect simplifies creating and managing data workflows. Its modern approach to workflow orchestration has earned it a loyal user base in the data engineering community. Prefect GitHub

Choosing open-source BI tools for your business

The answer to this question depends on your organization's unique requirements and objectives. Open-source BI tools offer a diverse range of functionalities, and to build your own BI suite, you may need to combine several open-source solutions or integrate them with some free or paid software. This approach allows you to tailor your BI environment to suit your specific data analytics needs and workflow.

Here are some considerations to keep in mind while choosing the right combination of open-source BI tools:

• Data Sources: Assess the data sources your business utilizes. Ensure the chosen BI tools can seamlessly connect and integrate with the databases, APIs, or file formats used to store your data.

• Visualization Capabilities: Consider the visualization options offered by the BI tools. Interactive and visually appealing dashboards and charts can significantly enhance the understanding and analysis of data.

• Scalability and Performance: As your business grows, your BI environment should scale efficiently to handle increased data volume and complexity. Choose tools that can meet your future analytical needs.

• Integration and Customization: Look for BI tools with open APIs and extensible architectures. This allows smooth integration with existing systems and provides room for customization to match your specific workflows.

• User-Friendly Interface: Evaluate the BI tools' ease of use and learning curve. A user-friendly interface enables technical and non-technical users to work with data effectively.

• Community Support: Active community support ensures continuous improvement and updates to the BI tools. It also provides access to valuable resources, tutorials, and forums for troubleshooting.

• Security and Data Privacy: Data security is paramount. Ensure the chosen BI tools adhere to the security protocols and comply with data privacy regulations.

• Total Cost of Ownership (TCO): While open-source tools are cost-effective, consider the overall TCO, including implementation, training, maintenance, and support.

By carefully evaluating these factors and experimenting with different combinations of open-source BI tools, you can create a tailored BI suite that aligns with your business objectives.

Introducing Rocket.BI - Datainsider's open-source business intelligence tool

Using open-source BI tools is excellent when you need flexibility & customization, but most require engineering resources. To enable self-service BI, you must use other closed-source tools like PowerBI and LookerStudio or deploy an open-source platform like Metabase or Superset. We like to introduce you to the missing piece, Rocket.BI - a feature-rich open-source business intelligence tool that delivers an unparalleled self-service analytics experience.

• No-Code Data Visualization: Rocket.BI empowers business users with a no-code data visualization interface. With simple drag-and-drop functionality, anyone can effortlessly build their own self-service BI dashboards, eliminating the need for technical expertise.

• Fully Integrated with ClickHouse: Say goodbye to slow analytics! Rocket.BI is fully integrated with ClickHouse, the quickest DBMS for analytics, ensuring lightning-fast data processing and analysis. And Clickhouse itself is open-source database.

• Future Integrations: Not stopping at ClickHouse, Rocket.BI has plans to integrate with other cloud solutions, Google Cloud and Snowflake, providing users with flexible data warehouse options.

• Fully Customized Charts: Customize your charts easily on the user interface - no coding required. Rocket.BI offers complete control over chart appearance, ensuring visually appealing and informative data visualizations.

• Data Enrichment with Ease: Rocket.BI enables users to enrich their data using an easy-to-learn syntax without relying on Data or Engineering teams. Unlock deeper insights and make data-driven decisions independently.

• Interactive Dashboards: Rocket.BI brings interactivity to the extreme, allowing zoom in and out, drilling down into granular data, or drilling through to detailed reports.

• Collaboration and Security: Collaborate with a secured password and row-level security; we ensure data integrity and control access to sensitive information.

Rocket.BI is the open-source BI powerhouse; we combine the visualized strengths of Plotly, the dashboard from Grafana, & the quick response from Kibana into one comprehensive tool. Whether you're a business analyst, data engineer, or decision-maker, Rocket.BI empowers you to quickly turn data into actionable insights, making data-driven decisions.