Build Data Apps Faster, Go Beyond Dashboards

Introduction: The Challenge of Data Application Development

Modern organizations struggle to move from data insight to action. Traditional dashboards are useful, but they limit your ability to automate responses, leverage AI, or build interactive workflows. Building full-fledged data applications—tools that let you act on data, not just view it—typically requires:

• Specialized engineering skills
• Complex infrastructure management
• Long project timelines from MVP to production
• Balancing customization with speed and cost

Keboola changes this dynamic. As a data operations platform, Keboola empowers teams to build, automate, and deploy interactive data applications in hours—not weeks—by integrating data pipelines, AI, and custom apps in one governed environment.

From Idea to MVP: The Classic Dilemma

Every project starts with two questions:

1. Should you build from scratch? This offers maximum customization and technical control, but usually means longer development cycles and higher costs.
2. Should you use existing tools? Leveraging platforms like Keboola accelerates MVP delivery, reduces costs, and lets you focus on business value.

Most data engineers love building from scratch. It’s fun and customizable. But business leaders want results fast. Today, using existing tools provides a far better trade-off—without sacrificing flexibility.

Example Use Case: Automating Review Responses with AI

Imagine you manage a popular attraction like the London Eye, receiving thousands of Google and TripAdvisor reviews. Responding to each review is vital for reputation management, but manual handling is slow and costly. Here’s how Keboola and modern tools solve this challenge:

• Scrape and aggregate reviews from various sources
• Clean and unify data using simple SQL queries
• Leverage AI/LLMs to translate, extract keywords, and score sentiment
• Automate personalized, polite responses to each review
• Deploy an interactive data application for your team to monitor, respond, and act—all in hours

Key technical challenges solved:

• Accessing and aggregating data from multiple platforms
• Automating data cleaning and enrichment
• Integrating with AI models (OpenAI, Azure, Google, Hugging Face, etc.)
• Transforming unstructured text into actionable, structured JSON
• Scheduling, monitoring, and controlling costs/usage
• Building secure, shareable frontend applications

Bringing AI to Your Data: How Keboola Makes It Simple

Traditionally, adding AI/ML to your data workflow required complex coding, infrastructure, and integration effort. With Keboola, it’s as simple as:

1. Connecting to data sources or uploading files
2. Configuring transformations (e.g., SQL for aggregation)
3. Adding AI enrichment: translate, summarize, extract keywords, or score sentiment with a simple prompt
4. Receiving structured outputs (e.g., JSON) ready for downstream applications

For example, to extract sentiment and keywords from reviews:

{ "translated_text": "...", "keywords": ["expensive","queue","view"], "sentiment": 0.2 }

You can design prompts to translate, extract, and normalize data, further customizing responses and outputs based on business needs.

Building the Frontend: From Dashboards to Data Applications

Dashboards are great for monitoring, but data applications let you act. With frameworks like Streamlit—easily integrated into Keboola—you can:

• Build Python-based interactive web apps in minutes
• Visualize insights (e.g., word clouds, sentiment pie charts)
• Enable business users to filter, explore, and respond to data
• Trigger actions: auto-generate AI responses, send notifications (Slack, Teams, Email), update systems, and more

Example: Empower your team to select a review, click “Generate Response,” and instantly receive a polite, AI-generated reply. All logic is customizable in Python, so you can extend functionality as needed.

Why Choose Keboola for Data Applications?

• Speed: Go from idea to MVP to production in hours, not weeks.
• Flexibility: Connect to any data source, use any AI model, and build with your favorite frameworks.
• Governance: Control access, monitor usage, and ensure data security across your organization.
• Cost Efficiency: Avoid infrastructure management and reduce development effort.
• Automation: Schedule workflows, trigger actions, and integrate with your existing stack.

Real-World Example: Automating Data Entry and Workflows

Consider legacy processes like Excel sheets with embedded VBA scripts for data entry and reporting. These are hard to maintain, lack governance, and are prone to errors. With Keboola and Streamlit:

• Replace Excel with secure, web-based entry forms
• Validate, transform, and enrich data automatically
• Authenticate users with SSO (Active Directory, Okta, Google, etc.)
• Integrate with downstream systems for seamless automation

With just a few lines of Python, you can create custom entry forms, automate data validation, and trigger business workflows—all within Keboola’s governed environment.

Customization vs. Standardization

While ultra-easy platforms like Streamlit may offer less visual customization than hand-coded apps, they dramatically accelerate delivery and reduce maintenance. Keboola provides a balanced approach—speed where you need it, flexibility where you want it, and governance throughout.

Best Practices for Building Data Applications Fast

1. Start with the business problem. Define what your users need to see and do, not just what to visualize.
2. Leverage existing connectors and components. Don’t reinvent the wheel. Keboola’s ecosystem covers most sources, transformations, and destinations.
3. Automate enrichment with AI. Use LLMs for translation, summarization, keyword extraction, and more—using prompts tailored to your context.
4. Build interactive frontends. Choose frameworks (like Streamlit) that integrate easily and let you iterate fast.
5. Integrate governance from the start. Set authentication, access controls, and monitoring before scaling usage.
6. Iterate and scale. Use Keboola’s automation and versioning to rapidly improve and deploy updates.

FAQ: Common Questions About Building Data Apps with Keboola

• Do I need to be an expert developer? No. Keboola supports both low-code and code-based workflows. Data engineers and analysts can build powerful apps with minimal effort.
• Can I use my own AI models? Yes. Integrate with any LLM provider (OpenAI, Azure, Hugging Face, etc.), and design prompts for your business needs.
• How do I control costs? Keboola provides usage monitoring, scheduling, and modular workflows—so you only pay for what you use.
• What about security and governance? Keboola is built for governed data operations. Set user permissions, audit usage, and comply with enterprise standards.
• Can I replace my Excel/VBA processes? Absolutely. Build secure, auditable web apps for any data entry, review, or reporting workflow.
• Is Streamlit open source? Yes. Keboola integrates with Streamlit and other frameworks, so you can build and deploy fast while retaining control.

Conclusion: Your Fast Lane to Interactive Data Products

If you’re tired of dashboard limitations, slow project delivery, and infrastructure headaches, Keboola provides a proven path to build, automate, and scale data applications—fast. Whether you’re automating customer feedback, building internal tools, or delivering data products, Keboola’s platform gives you unmatched speed, flexibility, and governance.

Ready to go beyond dashboards? Start building interactive data applications with Keboola today.