Open source analytics platforms are becoming a common choice for organizations that want to start monitoring their systems or reporting on their data without the large costs of mainstream platforms. These platforms are easy to get started with and backed by active communities without requiring software licensing.
Eventually, many open source projects do evolve into a mix of free and paid offerings. The free version provides a way to get started and get a proof of concept, then companies are eased into the enterprise capabilities that are enterprise deployment options.
That raises a question most people love asking on the r/BusinessIntelligence Reddit: can an open source analytics platform fully replace an enterprise BI platform?
That question came up in a conversation with one of our clients, who told us they were using Grafana and wanted to understand how it compared to MPP BI. Our engineering team evaluated both platforms side by side.
The goal was to understand where an open source platform fits, where an enterprise BI platform offers additional capabilities, and what organizations should consider when choosing between the two.
Grafana vs. MPP BI: A Closer Look
Our engineering team compared Grafana and MPP BI across several key areas to understand how the two approaches differ. We weren't trying to prove one platform is better. We wanted to understand what each one is built to solve.
Quick comparison at a glance
| Area |
|
|
|---|---|---|
| Product class | Monitoring and visualization tool | Full-scale enterprise BI platform |
| Data storage | Doesn't store data, connects to external sources | Uses PostgreSQL for data, ClickHouse for large-scale analysis |
| Data preparation | No built-in ETL, needs data pre-cleaned | Built-in ETL through MPP ETL |
| Business logic | No built-in calculation language | Built-in calculation language |
| Data focus | Time-series data and logs | Business data and KPIs |
| Primary users | DevOps and system administrators | Analysts, managers, and executives |
| Customization | Plugins and dashboard configuration | Open architecture, deeper platform customization |
| Security and access | Basic role-based access control | Granular access control, LDAP/AD integration |
| Export and reporting | Limited in open source version, needs plugins | Built-in PDF, PowerPoint, and Excel export, scheduled delivery |
| Dashboards and reports | Basic visuals with alerting | Wide range of visuals with built-in logic |
#1. Product Class
Grafana and MPP BI are built to do different jobs, and that shows up right from the start.
Grafana: a monitoring and visualization tool
Grafana is built to show you metrics and logs, which are basically streams of data about how your systems are running, in real time. For example, a team might use Grafana to track a server's CPU and memory usage
MPP BI: a full-scale enterprise BI platform
MPP BI uses data to understand how the business is doing and make better decisions. So instead of just watching for problems, it helps you build reports, and understand trends across your whole organization.
For example, a sales team might use MPP BI to see which products are selling best in each region, and adjust their strategy based on what the data shows.
#2. Data Storage
Where your data is stored affects how you manage it and how much control you have over your analytics environment.
Grafana: doesn't store data itself
it connects to external data sources and displays the information they provide. The data stays in the original database or system, while Grafana acts as the visualization layer.
MPP BI: uses PostgreSQL and ClickHouse for data storage
PostgreSQL is the core of its platform for storing data and metadata (information that describes the data).
For large-scale analytics, MPP BI recommends using ClickHouse, an OLAP database designed to quickly analyze large volumes of data. This approach allows MPP BI to handle both the analytics layer and the data environment within the same platform.
#3: Data Preparation
Before data can be used for analytics, it often needs to be collected, cleaned, combined, and organized, and this affects how quickly teams can build reports and how reliable the results are.
Grafana: no built-in ETL
Grafana doesn’t include built-in ETL or data preparation tools. It reads data from existing sources and displays it based on the information already available in those systems.
MPP BI: built-in ETL through MPP ETL
MPP BI includes its own built-in tool for this, called MPP ETL. It lets you prepare data and build data marts, which are smaller and organized sets of data built for a specific purpose,
#4: Business logic & calculations
Analytics is more than displaying data. Organizations also need a way to define how key metrics and business rules are calculated. For example, a company may need to define customer segments or other KPIs in a consistent way across reports and dashboards.
Grafana: no built-in calculation language
Grafana doesn't include its own calculation language. Calculations are handled in the connected data source, such as a SQL database, and Grafana displays the results.
MPP BI: built-in calculation language
MPP BI includes its own internal language for managing business logic and platform behavior. It allows organizations to create calculations and apply business rules directly within the BI platform.
#5: Data Types and Focus
Different platforms are designed to work with different types of data. The type of data affects what kind of questions it can help organizations answer.
Grafana: focused on operational data
- Time-series metrics: measurements collected continuously over time, such as CPU usage, memory use, network traffic, database performance, response times, and other system metrics.
- Logs: records created by applications, servers, and other infrastructure, showing events, errors, warnings, and user actions, along with when and where each one happened.
MPP BI: focused on business data
MPP BI is designed to analyze business data and help organizations understand business performance and make informed decisions.
- Business data: Information such as sales, finance, customers, or operations, available in real time or near real time, so decisions can be based on current, up-to-date information.
- KPIs (Key Performance Indicators): Important measurements used to track business goals, such as revenue and profit.
- Scheduled reporting: Reports that are created and delivered automatically at a chosen time, such as every morning or every month.
- Self-service analytics: Allows business users to explore data and build reports on their own, without relying on technical teams for every request.
#6: Primary Users
Who uses a platform day to day says a lot about what it's built for. Grafana and MPP BI are designed with different users in mind, and that shapes the features each one prioritizes.
Grafana: built for technical operations teams
- DevOps: Teams that build, deploy, and maintain software and infrastructure.
- System administrators (Sysadmins): People responsible for keeping servers and networks running smoothly.
MPP BI: built for business teams
- Analysts: they analyze and interpret data to find insights and answer business questions.
- Team leaders and managers: they use reports and dashboards to monitor performance and make operational decisions.
- Top management: CEOs or directors use high-level dashboards and KPIs to guide business strategy.
#7: Customization
Every organization has its own way of working, so flexibility is an important part of any analytics platform. Customization is a part of that flexibility to adapt to business needs.
Grafana: customizable through plugins and dashboard configuration
- Plugins: add new features or connect to more data sources.
- Dashboards: Can be created and modified through the user interface (UI) or by editing JSON (JavaScript Object Notation) to describe how a dashboard is structured and configured.
MPP BI: open architecture that allows deeper customization
- Dashboards and reports: Can be created and customized through the user interface (UI) or by editing JSON.
- Open service-oriented architecture: The platform is built in a way that allows customers to extend or reconfigure the interface. This means organizations can add new functionality or reconfigure the interface according to their own business processes.
#8: Security / Access
Every organization needs to control who can see and use which data. This is called access control, and it matters because not everyone in a company should have the same level of access to sensitive information.
Grafana: basic role-based access control
- Role-based access control (RBAC): you can give different users different levels of access, based on their role. However, how much control you have depends on the connected data source. Grafana itself doesn't add extra security on top of that, it relies on what the source system allows.
MPP BI: granular access control with enterprise integrations
- Granular access control: you can set very specific and detailed permissions, down to individual users, teams, or even parts of a dashboard.
- Integration with LDAP and Active Directory (AD): these are systems many companies already use to manage logins and user permissions across the organization. This means MPP BI can plug directly into a company's existing security setup, instead of managing access separately.
#9: Export and Scheduled Reporting
Being able to export dashboards and reports makes it easier to share information with other people, especially those who don’t use the BI platform. Scheduled reporting saves time by sending reports automatically at regular intervals.
Grafana: limited export options in the open source version
- PDF export: Available only in the Enterprise edition. In the open source (OSS) version, PDF export requires plugins or other external tools.
- Scheduled reports: Not available in the OSS version. They also require plugins or external tools.
MPP BI: built-in export and scheduled delivery
- PDF, PowerPoint (.pptx), and Excel exports: Built into the platform.
- Scheduled reporting: Reports can be generated automatically and delivered in PDF, PowerPoint, or Excel format at a chosen time, such as every day, week, or month.
#10: Dashboards and Reports
Dashboards and reports are where users interact with data. The available visualizations and built-in logic affect how easily organizations can explore information.
Grafana: focus on visualizing data and monitoring changes
- Visualizations: Supports common charts and panels to display metrics and logs.
- Alerting: Allows users to set alerts when specific conditions are met, such as when a value reaches a certain limit or when something changes unexpectedly.
MPP BI: dashboards and reports designed for business analysis
- Wide range of visualizations: Supports different types of charts and visuals to present business data in ways that fit different analysis needs.
- Built-in logic: Allows users to add calculations, rules, and business logic to dashboards and reports, helping organizations analyze data based on their specific requirements.
Can Open Source Analytics Replace Enterprise BI Platforms?
Open source analytics platforms can be a great solution for specific needs. However, the answer depends on what an organization expects from an analytics platform.
Where Open Source Tools Work Well
Open source tools can be a strong choice when organizations need:
- Monitoring and visualization: Tracking metrics, logs, and traces from existing systems.
- Flexible customization: Extending the platform based on specific technical requirements.
- Control over the environment: Managing the infrastructure and deployment process internally.
This is also why many open source projects, including Grafana, eventually move some of these capabilities into paid enterprise tiers, since building and supporting them takes ongoing investment.
Where Complete BI Platforms Add More Value
As organizations use analytics across more teams, they often need a more complete environment with ready-to-use capabilities, including:
- Executive dashboards and reporting: Helping leadership teams track business performance and make decisions.
- Advanced analytics: Supporting deeper exploration through features such as drill-downs, filtering, and interactive analysis.
- AI capabilities: Helping users discover insights and interact with business data in new ways.
- Built-in data preparation: Providing tools to prepare and organize data before analysis.
- Enterprise security and access control: Managing permissions across different users and departments.
- Flexible deployment options: Supporting cloud, on-premises, and hybrid environments.
- Professional support: Having direct access to experts who can help with implementation and troubleshooting.
Our Perspective
We often talk with organizations who are asking whether an open source tool can really meet their analytics needs. Teams start with an open source tool because it's a good fit for a specific job, then reach a point where their needs grow beyond what that tool was built to do. That's not a failure of the tool, it's a sign the organization has grown.
As a data engineering company, we spend our time helping organizations figure out what their data setup actually needs, not pushing one platform as the answer for everyone. Sometimes that means better data infrastructure, sometimes it means clearer reporting and analytics, sometimes it means bringing AI in a way that's useful. The right setup depends on the business, not on any single product.
Our goal is not to replace every existing tool, it is to help organizations build the right data and analytics environment for their needs.
If you're trying to figure out whether your current tools still fit where your business is headed, that's a conversation we're always happy to have.
