Introduction
As a data analyst, relying on a single tool for all your data analysis needs can feel like trying to fix everything with just a hammer—sometimes you need a screwdriver or even a wrench. Different tools are better suited for different requirements and knowing when to use tools like Power BI and Streamlit can significantly enhance the efficiency and clarity of your insights. Let’s dive into a detailed comparison of these tools, exploring their strengths and ideal use cases to help you maximize the effectiveness of your data analysis efforts.
Purpose and Audience of Data Analysis
Data analysis is crucial for various business roles, including project and product managers, stakeholders, engineers, and technicians. It helps in understanding market performance, evaluating campaign success, and identifying areas for improvement. As data analysts, our responsibilities extend beyond creating dashboards; we must:
- Respond to urgent data requests with customized reports.
- Analyze specific datasets to address business questions quickly.
- Streamline data processes and automate repetitive tasks using tools like SQL and Python.
- Communicate insights effectively to non-technical stakeholders.
Understanding when to use Power BI versus Streamlit is vital in fulfilling these roles efficiently.
Evaluating Trade-offs in Design and Data Analysis
Both Power BI and Streamlit offer robust functionalities for data analysts and scientists. However, the market offers a multitude of tools, each serving unique needs. Relying solely on one tool may lead to limitations, prompting the need to switch between platforms. To determine which tool suits our needs, we must first identify our primary focus.
Let’s explore two types of tools. Streamlit and Power BI share some functionalities, they serve different purposes and excel in different areas. If you’re considering Streamlit as a substitute for Power BI, it’s essential to prioritize between aesthetics and data complexity. While these are crucial factors, there are other considerations to keep in mind as well. Streamlit leans towards customization and flexibility, making it ideal for complex data analysis and machine learning applications. On the other hand, Power BI focuses on polished visualizations and seamless integration with Microsoft products, catering well to business intelligence needs.
Ingesting Data Comparison
Power BI:
Power BI excels in data ingestion, offering robust support for various data sources, including databases, cloud services, and flat files. Whether you’re pulling data from an SQL Server, Snowflake, Teradata, or Oracle, importing CSV files, or Excel files, or connecting to a web API, Power BI has you covered. It provides seamless integration with the Microsoft ecosystem, making data import straightforward and efficient.
Streamlit:
Streamlit, on the other hand, requires more manual setup for data ingestion. It leverages Python libraries like pandas and SQLAlchemy for database connections, offering flexibility but demanding more coding effort compared to Power BI’s point-and-click interface.
Below are examples of how you can connect to and fetch data from Snowflake, Teradata, Oracle, CSV files, and Web APIs.
- To connect to Snowflake, you can use the ‘snowflake-connector-python’
- For Teradata, you can use the ‘teradatasql’ library to establish a connection.
- To connect to an Oracle database, use the ‘cx_Oracle’
- You can easily load CSV files using ‘pandas’.
- To fetch data from a Web API, use the ‘requests’ library and ‘pandas’ to process the JSON response.
Handling Multiple Dimensions of Data Comparison
Power BI:
Power BI handles multi-dimensional data effortlessly through its intuitive interface. One of its standout features is its ability to automatically detect and establish relationships between tables, simplifying the process of creating complex data models. By simply dragging and dropping relevant fields, Power BI intelligently sets the cardinality and cross-filter direction, enabling users to seamlessly explore data across multiple dimensions.
Streamlit:
Streamlit is highly customizable but requires more effort to handle multi-dimensional data. Users can manually define join conditions and apply custom logic, providing greater control over data integration compared to Power BI’s automatic relationships.
Example: In Streamlit, you have the freedom to define custom join conditions, allowing for flexible data integration. For example, if one table contains IMEI numbers like “100200300” and another includes IMEI numbers prefixed with a company code like “TAC100200300”, you can specify a join condition to ignore the prefix and only match records based on the common IMEI data. This flexibility enables seamless merging of data from different sources, enhancing the versatility of your analysis without being constrained by rigid data relationships.
Aesthetics Comparison
Power BI:
Power BI enables easy creation of interactive designs, offering a variety of pre-built themes and customization options. Users can quickly create polished and professional-looking reports without the need for extensive programming. Power BI provides inbuilt features to customize visuals, such as using SVG files from tools like Figma or basic tools like PowerPoint for backgrounds and morph animation. This flexibility allows for the creation of visually appealing dashboards with minimal effort.
Streamlit:
Streamlit offers extensive customization capabilities for enhancing widget designs with advanced styling techniques. While it requires more coding effort compared to Power BI, users can utilize ‘st.columns()’ for adjustable layouts and ‘st.write()’ with HTML markup for custom CSS styling, including properties like size, margins, and shadows. CSS provides control over visual aspects like ‘box-shadow’ for shadows and ‘border-radius’ for shapes. Although Streamlit lacks direct SVG background support, CSS allows injecting SVG backgrounds, enabling users to craft visually polished interfaces with ease. Streamlit is more suited for those who prefer detailed control over aesthetics and are comfortable with front-end development.
Chart Customization Flexibility Comparison
Power BI:
Power BI provides a wide range of pre-built visuals that can be easily customized. However, users are limited to working with the visuals included in the package and may encounter challenges in handling complex visual relationships. For instance, if you want to create a chart displaying expenditure, revenue, and orders for a specific date range, you may encounter issues with visual representation due to varying scales of data. Although Power BI allows placing the orders on a secondary y-axis, this can lead to a visually misleading representation where the order line appears higher than the expenditure line due to different scales.
Streamlit:
Streamlit offers greater flexibility in chart customization, enabling users to create advanced visuals with ease. For the same scenario of displaying market expenditure, generated revenue, and number of orders, Streamlit allows users to display each line in separate graphs with a common x-axis. This approach provides clarity and avoids the issue of misleading visual representations encountered in Power BI. Additionally, Streamlit can create a single trace line covering all three graphs together in one common tooltip, offering a comprehensive view of the data for a specific date range.
Real-Time Data Handling Comparison
Power BI:
Power BI supports real-time data updates through DirectQuery mode, which connects directly to data sources like SQL databases and Azure services. This allows users to query data in real-time without importing it into Power BI. Every interaction with the report sends a query to the data source, ensuring up-to-date information. This approach is ideal for business users needing real-time business intelligence without extensive coding.
However, companies often use ODBC (Open Database Connectivity) connections in Power BI instead of DirectQuery mode due to concerns over security, performance, and compatibility. This can limit the ability to create real-time data reports. For specific scenarios like pilot product launches or ad-hoc requests, using a Streamlit app can be more efficient for providing real-time data reports with the necessary information, avoiding the need for manual data updates.
Streamlit:
Streamlit handles real-time data through dynamic data fetching, using Python libraries like requests for API calls, pandas for database queries, and specialized libraries for various data sources. Interactive widgets in Streamlit (e.g., sliders, buttons) trigger immediate data updates by default and re-render visualizations based on user inputs. Developers can schedule periodic data fetching with loops, cron jobs, or Streamlit functions like st.experimental_rerun() and st.timer().
However, Streamlit’s real-time data fetching has limitations. It isn’t optimized for high concurrent user traffic, leading to potential latency and responsiveness issues with frequent script reruns. Each widget interaction can add nested dependencies, creating performance challenges. To control data updates and avoid constant reruns, developers can use session state and manual triggers (like buttons) to fetch and update data only when explicitly needed.
Integrating Machine Learning Comparison
Power BI:
Power BI integrates with Azure Machine Learning, offering built-in features for out-of-the-box forecasting and model deployment. This allows users to perform predictive analytics and forecasting with minimal coding effort. However, while Power BI’s integration with Azure Machine Learning provides a streamlined experience for business users, it may lack the flexibility and customization that more advanced machine learning tasks require.
Cost Consideration: Using Azure Machine Learning features within Power BI might incur additional costs, as Azure Machine Learning is a separate service with its own pricing model. Users may need to pay for the Azure resources used for running machine learning models and storing data.
Example: You can connect your Power BI report to Azure ML models and use built-in features like time series forecasting to predict future sales trends based on historical data with a few clicks. This integration is particularly beneficial for business users who need to perform predictive analytics without delving into extensive coding.
Streamlit:
Streamlit excels in integrating custom machine learning models, leveraging a wide range of Python-based libraries such as scikit-learn, TensorFlow, and PyTorch. This allows data scientists and developers to build, deploy, and interact with highly customized machine learning models directly within Streamlit apps. The platform’s flexibility and customization make it ideal for developing interactive dashboards tailored to specific machine-learning needs.
Example: Streamlit allows you to use libraries like scikit-learn, TensorFlow, or PyTorch to build and deploy machine learning models. You can create an interactive web app where users can input data, trigger the model to run predictions, and see the results in real-time. This level of integration is particularly useful for showcasing machine learning models and getting immediate feedback from stakeholders.
Publishing a Dashboard Comparison
Power BI:
Power BI seamlessly integrates with other Microsoft products and services, such as Azure, Excel, and SharePoint. This integration enables easy data import, collaboration, and sharing across the organization. Power BI supports various data connectivity options, including DirectQuery, import, and live connections to databases, cloud services, APIs, and files. Users can connect to a wide range of data sources and integrate data into their dashboards.
Dashboards created in Power BI can be published to the Power BI service, where they can be shared and accessed by users across the organization. Users can set up data refresh schedules, manage access permissions, and collaborate on shared dashboards. Power BI provides robust security features, including role-based access control (RBAC), data encryption, and compliance certifications (e.g., GDPR, HIPAA). This ensures that sensitive data is protected and compliant with regulatory requirements.
Publishing and Sharing Costs:
Power BI Pro: To publish and share dashboards within the Power BI service, users need a Power BI Pro license, which costs approximately $9.99 per user per month. This allows for sharing dashboards and reports, setting up data refresh schedules, and managing access permissions.
Power BI Premium: For larger organizations, Power BI Premium offers additional features, such as higher storage capacity, larger dataset sizes, and advanced AI capabilities. Power BI Premium pricing starts at $20 per user per month for the Per-User plan or $4,995 per capacity per month for the capacity-based plan.
Streamlit:
While Streamlit itself doesn’t have native integration with the Microsoft ecosystem like Power BI does, it can still interact with Microsoft services and tools to some extent, especially through the Python ecosystem. By leveraging Python libraries and APIs, users can integrate Streamlit applications with Microsoft SQL Server, Azure SQL Database, and Excel files, using Python libraries such as pyodbc for database connectivity or pandas for data manipulation. Python libraries like azure-sdk-for-python can access Azure services such as Azure Blob Storage, Azure Cognitive Services, or Azure Machine Learning.
Streamlit apps can be deployed on various platforms, including Azure App Service, Azure Container Instances, or Azure Kubernetes Service. Users can leverage Azure’s infrastructure and services to deploy and host Streamlit applications securely and at scale. Streamlit applications can implement authentication and authorization mechanisms using Microsoft identity services such as Azure Active Directory (AAD). Users can integrate Streamlit apps with AAD for user authentication, access control, and single sign-on (SSO) capabilities.
Deployment and Sharing Costs:
Self-Hosting: Streamlit apps can be deployed on various platforms, including Azure App Service, Azure Container Instances, or Azure Kubernetes Service. The cost will depend on the chosen Azure service and usage. Azure App Service, for example, has a free tier, but higher tiers have costs that vary based on the resources allocated.
Streamlit Cloud: Streamlit also offers Streamlit Cloud for easy deployment and sharing, which has a free tier for individuals and small teams. For advanced features and more collaborators, the Pro plan costs around $39 per user per month.
Free Sharing: Even without deploying the dashboard or app, Streamlit allows sharing apps for free. You can share your Streamlit app directly by sharing the script with others who can run it locally on their own machines.
Example Steps for Deploying a Streamlit App:
- Develop the Application: Write your Streamlit app in Python, using libraries like pandas and pyodbc to connect to data sources.
- Deploy to Azure App Service: Create an Azure App Service: Set up a new web app in the Azure portal.
- Deploy the App: Use Git or Azure DevOps to deploy your Streamlit app to the Azure App Service.
- Configure Authentication: Use Azure Active Directory (AAD) to set up authentication and authorization for your app.
- Access Azure Services: Leverage azure-sdk-for-python to integrate Azure services like Blob Storage or Cognitive Services.
- Share the App: Provide users with the URL to access your deployed Streamlit app.
Ease of Sharing Comparison
Power BI:
Power BI allows users to publish dashboards to the Power BI service, enabling easy sharing and collaboration within the organization. It supports various sharing options, including email, embedding in websites, and integration with other Microsoft tools.
Streamlit:
Streamlit apps can be shared via a browser with locally shareable links or deployed on platforms like AWS, Azure, and Streamlit Cloud. This makes it convenient to share interactive applications, but it requires more setup compared to Power BI’s built-in sharing features.
Conclusion
Choosing the right tool for data visualization is like selecting the right utensil in the kitchen—sometimes you need a whisk, not a blender. Both Streamlit and Power BI have their unique strengths and are suited for different scenarios.
Streamlit: Flexibility and Speed
For quick, ad-hoc analyses where flexibility and speed are paramount, Streamlit shines. It’s your agile toolkit, perfect for rapid prototyping and creating interactive, customizable visualizations. When building Streamlit apps, focus on demonstrating only a few highly valuable and focused ideas. If you anticipate that your app will grow in scope and size, consider using tools like Power BI for crowded dashboards or switch to Panel for more complex and approachable apps, or H2O Wave for real-time dashboards.
Remember, it’s important to prioritize simple analytical visualizations when using Streamlit. Avoid incorporating flashy designs and overcrowding the app with pay-subscriptions, Google authentication, login forms, an about page, or chatbots. Streamlit is not intended for developing full-fledged apps or websites.
Scenario 1: Rapid Prototyping
You’re tasked with delivering an immediate analysis of a recent marketing campaign’s performance. Streamlit allows you to quickly build a visualization app that can be adjusted on the fly as new data comes in or as the PM requests different views.
Scenario 2: Customized Interactivity
Suppose a data scientist needs to create an app where users can filter data dynamically, select different variables, and see real-time results. Streamlit’s ability to integrate with various Python libraries makes it ideal for building highly interactive applications.
Scenario 3: Demonstrating Key Insights
You need to present the impact of a new strategy on sales figures to a small group of stakeholders. Streamlit helps you create a focused, interactive presentation that highlights specific metrics without overwhelming the audience with too much information.
Power BI: Comprehensive Reporting and Scalability
Power BI excels in creating comprehensive, detailed dashboards and reports, making it ideal for projects that require robust, scalable solutions. There are other tools, like Power BI, that may have slightly different features, like Tableau, Looker, etc. which could suffice your requirements over Power BI.
Scenario 1: Company-Wide Dashboards
You need to develop a dashboard that tracks company-wide sales, marketing performance, and operational metrics. Power BI’s ability to aggregate data from multiple sources provides a holistic view that is essential for strategic decision-making.
Scenario 2: Standardized Reporting
In a large organization where different teams need consistent reporting tools, Power BI offers a user-friendly interface with powerful features like drag-and-drop, pre-built templates, and extensive data connectors, ensuring everyone can create and understand the reports.
Scenario 3: Microsoft Ecosystem Integration
If your organization relies heavily on Microsoft products such as Excel, SQL Server, and Azure, Power BI’s seamless integration with these tools enhances productivity and streamlines data management.
Other Tools to Consider
While Streamlit and Power BI cover a wide range of needs, other tools can offer specific advantages depending on the requirements:
Datawrapper: A tool for creating charts, maps, and tables for data visualization, with a focus on simplicity and ease of use.
KNIME Analytics Platform: A tool for data analytics, reporting, and integration, with a visual workflow designer.
Apache Superset: A data exploration and visualization platform designed to be visual, intuitive, and interactive.
Metabase: An easy-to-use tool for data visualization and business intelligence, allowing users to create charts and dashboards without writing SQL.
Redash: A tool for querying, visualizing, and collaborating on data, connecting to various data sources.
Grafana: A platform for monitoring and observability, but can also be used for creating dynamic dashboards for analyzing and visualizing data.
OpenRefine: A tool for cleaning and transforming data, particularly useful for messy data sets.
Now that you have a better understanding of when to use Power BI versus Streamlit for your data analysis needs, take your insights to the next level with expert guidance. Explore our Data consulting services in BI & Analytics and see how we can help streamline your data processes and maximize efficiency.
