Re-imagining Semantic Search Inside Power BI

Posted on October 17, 2025October 17, 2025 by snp

The Hidden Cost of “Simple” Search Apps

Many teams we talk to already use Azure AI Search. It’s a powerful service for making text and documents searchable with semantic and vector search.

But here’s the pattern we see over and over:

A new web app is built (maybe Streamlit, maybe a custom React app).
It’s hosted on Azure App Service or VMs.
It duplicates authentication, hosting, monitoring, DevOps pipelines…
And at the end of the day, users just get a search box + results table.

The business value is real, but the delivery is complex and costly for what it achieves.

Imagine if your users could

Type a search query,
Get semantic results with highlights and summaries,
And see them right inside the Power BI dashboards they already use every day.

No new app.
No separate portal.
No extra infra to maintain.

Just a familiar search box in Power BI, powered by Azure AI Search + AI summarization.

Also note that this approach requires no PowerApps or Power Automate — it runs entirely within Power BI.

What does it include

Power BI report provides:

Free text input for users to type search queries.
Native Power BI filtering (date, region, product, etc.) alongside semantic search results.
Paging support to navigate through large result sets.
Export options to Excel, CSV, PDF, and more.
Multiple report pages to query across different indexes, documents or datasets.
Power BI native authentication to the report

High-Impact Use Cases

Organizations like yours can quickly benefit from this approach for scenarios such as

Customer Service: Mine complaint text for themes (refunds, delivery delays, product defects).
Compliance & Legal: surface contract clauses or policy excerpts directly in dashboards.
Ops & IT: search across incident logs and root cause notes.
HR & Internal Comms: make policies instantly discoverable by employees.

All without building another app that IT must support.

Why it matters?

Cost savings: no Azure App Service, no custom UI hosting, no redundant auth.
User adoption: everyone already knows Power BI. No training needed.
Speed: what used to take weeks of app dev can now be delivered in days.

Where This Approach Fits Best

This design is intentionally simple and focused. It excels when you need:

A single query to retrieve relevant results.
Clear insights (summaries, highlights, tags) displayed directly in Power BI.
Seamless integration with existing dashboards and metrics.

It’s built for search + analytics, not for chatbot-style experiences.

So, if your scenario requires things like:

Conversational Q&A with follow-up questions,
Multi-turn history or context retention,
Uploading and reasoning over new documents during query,

Those are better served by a different architecture.

Think of this as “one search → one set of insights → shown in Power BI” — fast, clean, and highly effective for dashboards.

Your Next Step Toward Smarter Search

The exciting part here isn’t that it’s impossible tech or solution — it’s that the approach is far simpler than many expect, and that simplicity makes it faster, cheaper, and easier to adopt.

If you’re juggling multiple initiatives, this approach is lightweight and ideal for proving value before scaling further. You don’t need to build a full-blown application — instead, you can get something off the ground in days, not weeks. The sooner you see it running on your own data, the faster you’ll recognize its value.

Bring us your use case, and we’ll show you results fast (POC/implementation). You can skip the app dev overhead and light it up search inside Power BI directly. You’ll be surprised how simple (and cost-effective) it can be.

Automating Backup, Retention, And Restoration For Lakehouse In Microsoft Fabric

Posted on October 17, 2025October 17, 2025 by snp

Data resilience is a cornerstone of modern analytics platforms. In Microsoft Fabric, maintaining backups and implementing automated policies for retention and restoration can elevate data management.

While Fabric is a robust platform, disaster recovery (DR) is not designed to address operational issues like data refresh failures or accidental deletions, which necessitate an automated approach to bridge the gaps and ensure operational continuity.

Effective backup, retention, restoration strategies are essential to maintaining reliable data platform, particularly in scenarios involving refresh failures or data corruption.

Note: This is not a substitute for disaster recovery feature of Microsoft Fabric, but a complementary approach to enhance resilience, streamline restoration processes, and minimize downtime through automation and proactive configurations.

Here’s an overview of setting up, configuring, and automating these processes while addressing challenges and their solutions.

Setting Up Backup and Retention Policies

Microsoft Fabric’s Lakehouse and OneLake provide unique capabilities for handling data. Backing up data involves:

Daily Incremental Backups: Ensuring minimal data loss by creating daily snapshots.
Retention Policy Configuration: Establishing tiers like daily, weekly, monthly, and yearly retention to balance storage costs and compliance.
Automation with Notebooks: Using Fabric notebooks to schedule backups and enforce retention policies, such as retaining the last 7 daily backups or 6 monthly backups and cleaning up obsolete ones.

Automation Highlights:

Backup Creation: Scheduled scripts create snapshots at specific intervals. For example, Spark jobs can efficiently copy data using APIs like mssparkutils.
Retention Enforcement: A policy-driven approach automatically removes outdated backups while preserving critical ones for auditing or recovery.
Logging and Monitoring: Every backup, cleanup, and restoration action is logged to ensure transparency and auditability.

Restoration: Recovering from Data Loss

Fabric allows for full or selective restoration of data from backups. Restoration tasks involve:

Restoring entire Lakehouse or specific tables from a backup.
Using structured logs to identify and resolve errors during the restoration process.
Minimizing downtime by enabling rapid data recovery with scripts or automation tools.

Why Automate Backup and DR in Microsoft Fabric?

Automation mitigates risks and improves efficiency:

Data Integrity: Automated backups ensure all critical data is consistently safeguarded.
Operational Continuity: Quick restoration scripts minimize business downtime.
Cost Optimization: Automating cleanup eliminates outdated backups, reducing unnecessary storage expenses.
Scalability: Structured policies can accommodate growing datasets without additional manual effort.

Conclusion

While Microsoft Fabric is a promising data platform, addressing data corruptions, accidental deletion challenges require a proactive and automated approach. By leveraging our automation for backup, retention, cleanup, and restoration, organizations can safeguard data, ensure business continuity, it provides significant value for the business.

From Reports to Data Agents: The New Way of Accessing Data

Posted on October 17, 2025October 17, 2025 by snp

In today’s fast-moving business environment, access to timely and trusted data is no longer a nice-to-have—it’s a necessity. Yet, most organizations still face hurdles that slow down decision-making and create frustrating bottlenecks.
And to be clear, we’re not talking about futuristic “real-time intelligence” from IoT or streaming data pipelines—we’re talking about something far more fundamental: the ability to quickly get reliable insights from the databases businesses already rely on every single day.

The Challenges of Accessing Data

For many business users, getting the answers they need from data looks something like this:

• Waiting for standard reports that only scratch the surface of what they truly need.
• Relying on SQL expertise to query the database for anything beyond the basics.
• Depending on IT teams or analysts to interpret and deliver answers.

These delays not only eat up valuable time but also mean that insights often arrive too late to influence critical decisions.

A New Approach: Conversational Access to Data

Now imagine an alternative: instead of waiting days or weeks for reports, a business user simply asks a question in natural language—and gets a trusted, contextual answer instantly.

With Data Agents, business leaders, analysts, and even frontline teams can access the data they need without knowing SQL, without relying on IT, and without waiting for the next report cycle.
• No SQL required.
• No long waits.
• Empowering business teams like never before.

The result? Data-driven culture, decision-making becomes truly self-service.

Building Trusted Data Agents: Beyond Just Turning On a Feature

Of course, setting up a Data Agent isn’t just about flipping a switch. To deliver reliable, trusted answers at scale, organizations need to plan, design, deliver, and continuously improve the ecosystem around their Data Agent. Let’s break down what this entails.

1. Planning and Cost Considerations
Before diving in, organizations must define clear goals and use cases. What business problems should the Data Agent solve first? At the same time, thoughtful cost planning is essential. Beyond the technology itself, budgets must account for data preparation, infrastructure, governance, and ongoing support. A Data Agent can reduce downstream costs by freeing up analysts’ time, but it requires upfront investment in design and setup.

2. Data Preparation
A Data Agent is only as good as the data it has access to. That means cleaning, transforming, and organizing your datasets before connecting them. Removing duplicates, standardizing formats, and ensuring completeness are critical steps to avoid misleading or incomplete answers.

3. Metadata Enrichment
Context is what makes data usable. By enriching datasets with metadata—like descriptions, business glossary terms, you help the Data Agent interpret questions more accurately and provide answers in the right business context.

4. Modelling and Design
Well-structured data models ensure that relationships between different entities, organizational metrics are clearly defined. Without proper modelling, Data Agents risk providing fragmented or inaccurate insights. Designing semantic models allows for richer, more intuitive answers that align with how the business actually operates.

5. Defining Agent and Data Source Instructions
Think of this as training your Data Agent. Defining role, rules, and usage boundaries helps ensure the Agent interprets user intent correctly and queries the right data sources. This is crucial for context, consistency and relevance.

6. Security and Governance
Opening up data access doesn’t mean compromising on security. Role-based permissions, data masking, and compliance checks need to be in place so that users only see the data they’re authorized to access. Equally important is governance: setting standards for how data is cataloged, consumed, and maintained ensures long-term trust in the system.

7. Delivery and Deployment Strategy
Designing a deployment strategy for Data Agents goes beyond an internal rollout—it’s about choosing the simplest and secure way to expose them where they create the most value. That could mean embedding the Agent into Microsoft Teams for seamless daily use, integrating it into a website or customer portal, creating a dedicated self-service page, or exposing it as an API so organizations can plug natural language access to data into any system they choose. A phased rollout often works best: begin with a high-impact use case, demonstrate value, then expand gradually.

8. Maintenance and Monitoring
A Data Agent isn’t “set and forget.” To stay effective, it requires ongoing care—regularly validating responses, updating data models, refining instructions, and monitoring performance to ensure it continues delivering accurate and trusted insights as business needs evolve. Metrics such as query response times, adoption rates, and user satisfaction also need to be tracked to ensure the system is delivering as intended.

9. User Feedback and Continuous Improvement
Adoption hinges on trust, and trust is built over time. Encouraging feedback from business users helps identify gaps, misunderstood queries, or areas where answers could be improved. Iterating on those insights ensures the Data Agent evolves with the business.

10. Scalability and Improvement Loops
As the organization matures in its use of Data Agents, improvements should be baked into the cycle—expanding to new data domains, refining models. A mature Data Agent doesn’t just answer questions; it proactively surfaces new opportunities.

The Path to a Self-Service Data Culture

When organizations invest in building Data Agents the right way, the payoff is huge:

• Business users get instant, contextual insights.
• Analysts spend less time firefighting ad-hoc requests and more time on strategic analysis.
• Leaders can make smarter decisions, faster.

In short, you create a true self-service data culture—where insights are no longer bottlenecked but flow seamlessly to the people who need them.

How to Get Started

💡 Our recommendation: start small. Pick a focused use case where quick wins are possible, ensure the Data Agent is delivering trusted answers, and then refine the agent, scale across teams and functions to maximize value.
We’re helping organizations set up their own Data Agents in Fabric—making data more accessible, reliable, and actionable than ever.

If you want to empower your business with instant, trusted insights, let’s connect.