DEV Community: Aparna Gupta

From Fragmented Data to Near Real-Time Analytics with Microsoft Fabric

Aparna Gupta — Thu, 30 Apr 2026 17:04:35 +0000

What was happening
The impact
Approach
Implementation
Technical stack
Business impact
What did not change
Key takeaway
Final note

In one of our recent projects, reporting cycles weren’t measured in minutes or hours.

They were measured in weeks.

Data existed across multiple systems. Teams were working with it daily. But getting a consolidated, reliable view across finance and operations still took 10–14 days.

Not because the data was inherently complex.

Because it was distributed across systems without a unified data layer.

What was happening

The organization used:

SAP S/4HANA
SAP FSM
SharePoint
other operational sources

Each system functioned independently, but there was no centralized layer for consistent data integration.

A typical reporting cycle involved:

Extracting data from multiple systems
Aligning formats and structures manually
Reconciling inconsistencies
Reapplying business logic for each reporting cycle

These steps were repeated for every reporting requirement.

The Impact

Reporting cycles extended up to 10–14 days
KPI definitions varied across teams
Business users relied on manually prepared reports
Scaling to 50–100M+ records per year introduced performance and consistency challenges

The limitation was not the availability of tools, but the absence of a structured and centralized data foundation.

Approach

The objective was to establish a centralized data platform with:

standardized data ingestion
repeatable transformation workflows
governed data models for reporting

The implementation used Microsoft Fabric and Power BI.

Implementation

Centralized data platform

A Lakehouse was implemented on OneLake using a medallion (Bronze–Silver–Gold) architecture:

Bronze: raw ingested data from source systems
Silver: cleansed and standardized datasets
Gold: curated datasets aligned with reporting requirements

This provided a centralized data layer for downstream analytics.

Data ingestion and processing

Data was ingested from SAP S/4HANA, SAP FSM, SharePoint, and flat files
Fabric Dataflows Gen2, Pipelines, and Notebooks were used for ingestion and orchestration
Transformations were implemented using Fabric Notebooks (Spark) and pipeline activities
Data pipelines were scheduled at defined intervals (e.g., daily batch processing), not event-driven or streaming

These workflows reduced manual intervention and improved consistency of data preparation.

Data modeling and access

Power BI Semantic Models were built on top of Gold layer datasets
DAX was used to define KPIs and business logic
Row-Level Security (RLS) was implemented for role-based data access

This ensured that reports referenced a consistent data model.

Visualization layer

Reports were developed in Power BI Service
Direct Lake mode was used to query data from the Lakehouse without requiring data import into the model
This reduced query latency compared to import-based models, depending on model design and storage optimization
Data freshness remained dependent on upstream pipeline execution schedules

Automation and orchestration

End-to-end data workflows were orchestrated using Fabric Pipelines
Manual data extraction and consolidation steps were replaced with scheduled processes
Pipeline dependencies and execution sequences were configured to maintain data consistency across layers

Governance, monitoring, and security

Access control was managed using Azure AD
Data models and transformations enforced standardized definitions
Monitoring and alerting mechanisms were configured to track pipeline execution and failures

Technical stack

Ingestion & orchestration: Fabric Dataflows Gen2, Pipelines
Processing: Fabric Notebooks (Spark)
Storage: OneLake (Lakehouse architecture)
Modeling: Power BI Semantic Models (DAX, RLS)
Visualization: Power BI Service (Direct Lake mode)
Security: Azure AD

Business impact

Reporting timelines reduced from 10–14 days to under 24 hours, based on scheduled pipeline execution
20+ reports transitioned from manual preparation to automated workflows
60+ users accessed centralized datasets through role-based access
10+ dashboards were built using standardized KPI definitions
Automated pipelines with monitoring improved consistency of data delivery

What did not change

Data processing remained batch-based (scheduled execution)
No real-time or streaming architecture was introduced
Data quality depended on defined transformation, validation, and governance practices

Key takeaway

Most reporting delays were not caused by reporting tools. They were caused by the absence of a centralized and consistent data layer.

Once data ingestion, transformation, and modeling were standardized:

reporting became repeatable
data definitions became consistent
access to insights improved

Final note

This implementation replaced fragmented, manual reporting workflows with a structured analytics platform built on Microsoft Fabric.

The system now supports:

centralized data storage
automated and scheduled data pipelines
governed semantic models for reporting

It also provides a foundation that can support additional analytical workloads without requiring a redesign of the core data architecture.

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Single-Agent vs Multi-Agent Systems: Which One Is Right for Your Business?

Aparna Gupta — Mon, 27 Apr 2026 07:37:19 +0000

> Agentic AI is a powerful autonomous tool to automate complex processes and decision-making at scale. Here, we’ll discuss single-agent vs multi-agent systems to understand when an enterprise requires more than one AI solution.

With advancements in artificial intelligence, businesses can integrate various powerful tools to streamline and transform their systems. One such solution is agentic AI, or an AI agent, an autonomous intelligent system capable of handling complex instructions and making decisions with limited or no human intervention. It goes beyond simple automation and gives enterprises an edge over competitors.

Statistics show that the global market value of agentic AI is expected to be $89.6 billion in 2026, with the enterprise segment having the largest market share of 76% ($68.2 billion). The healthcare and life sciences industry leads the table with 21% of total investments in agentic AI, followed by the financial services industry (18%) and enterprise/manufacturing (16%).

AI agents can further be classified into single-agent vs multi-agent systems, each catering to different requirements. However, CTOs and CIOs should determine which AI system is best for their business and when the enterprise would benefit from using multi-agent systems.

In this blog, we’ll compare single-agent vs multi-agent systems and understand when to use the models and how they can help an organization achieve its goals.

What are AI Agents?

AI agents have four building blocks or features that make them different from regular automation tools. Firstly, AI agents make autonomous decisions by choosing their actions and ‘thought’ processes to deliver the output.

Secondly, agentic AI exhibits goal-directed behavior in which it does what is necessary to achieve the expected goal. For example, if the user asks the agent to perform a task, the system determines the best way to execute it. Thirdly, AI agents can perceive and respond to changes in the operating environment, making them more ‘conscious’ of the context.

This results in more relevant and useful output. Finally, AI agents are adaptive and can refine their approach/ processes based on the memory of past interactions and requirements. All these make agentic AI a powerful addition to the business.

By partnering with an AI agent development company, executives and IT directors can redesign their systems to support employees in enhancing their performance without adding to their workload. Moreover, agentic AI developers offer tailored services to build and deploy custom systems for specific use cases in each enterprise.

What are Single-Agent Systems?

A single-agent system is a centralized approach where reasoning, memory, and tool execution are consolidated into a single AI instance. Rather than distributing each task to a specialized system, everything is handled by the same system. Think of it as the human brain multitasking and running several ideas/ thoughts in parallel.

Organizations can hire custom AI agent development services to build single-agent systems to handle specific tasks and decisions. Since everything is performed by the same system, it consumes fewer resources (comparison between single-agent vs multi-agent systems) and delivers fast outcomes. Moreover, the single-agent systems are easy to build and can be launched quickly. That said, it cannot handle complex tasks or support horizontal scaling across different domains. Additionally, a technical failure can result in unexpected downtime as it might affect the entire infrastructure.

What are Multi-Agent Systems?

Multi-agent systems, as the name suggests, have more than one agent, each handling specialized tasks. The agents use explicit coordinated mechanisms to deliver the output quickly and efficiently. It is simply a setup of multiple agents collaborating in a shared environment to perform a complex task. The agents are connected in such a way that one’s output becomes the input for another, and this continues until the system gives users the desired result.

The order of agents is determined based on routing logic and workflow requirements. It could be sequential, parallel, or hierarchical. The demand for multi-agent AI development services has increased in recent times, with C-suites intent on transforming their business operations using advanced technologies. Since multi-agent systems require more LLMs (large language models), the costs can pile up easily. However, with strategic optimization, enterprises can enjoy high ROI and make smart decisions to boost the business.

Single-Agent vs Multi-Agent Systems: The Comparison

The differences between single-agent vs multi-agent systems can be seen in the design, complexity, cost, and other factors listed below.

Architecture

Single-agent systems have a unified pathway, where one autonomous agent handles all tasks from end-to-end. Multi-agent systems have several specialized agents collaborating in a complex environment to perform a task.

Complexity

Single-agent systems can handle activities with low to moderate complexity as they have a simpler design. Multi-agent systems have a complex design with orchestration requirements that allow them to process sophisticated instructions and tasks.

Cost of Investment

Due to the simplicity of the project, single-agent systems are less expensive compared to multi-agent systems. The actual cost could vary based on your specifications. Both systems will require monthly maintenance and optimization.

Development Speed

Single-agent systems are quicker and easier to develop than multi-agent systems, which is a time-consuming process. The initial phase in multi-agent development is slower as modular design and orchestration require expert skills and knowledge.

Task Specialization

Typically, single-agent systems are used for general purposes like chatbots, virtual assistants, summarizing emails and reports, etc. On the other hand, multi-agent systems deal with domain-specific tasks that require greater reliability and accuracy. Marketing automation pipelines, financial reporting, automating multi-step workflows, etc., are some examples of multi-agent systems.

Context Management

Single-agent systems rely on a centralized architecture. The memory and context are also stored in a centralized location, making it easy for the tool to complete straightforward tasks. Multi-agent systems follow a decentralized architecture where the context is across agents and is structured to ensure relevance, accuracy, and effectiveness.

Resource Efficiency

Single-agent systems usually use large language models (LLMs) for simple tasks. While they give good results, there can be inefficiencies when the system is not fully optimized. Multi-agent systems are built using lightweight models for simple tasks and heavy models for complex tasks. This combination makes it easier to optimize the entire setup and increase overall efficiency.

Scalability

Scalability is a vital factor in today’s world. Both systems are scalable. However, between single-agent vs multi-agent systems, the multi-agent model is more flexible and scalable. Hence, it is quickly becoming a preferred choice for large enterprises. CTOs can hire AI consulting services for their businesses to get a tailored strategic design for implementing advanced solutions.

Governance and Compliance

The unified architecture of the single-agent system makes it simpler to implement the governance framework and ensure compliance. Data security, governance, and compliance can be complicated for multi-agent systems as each agent requires separate access controls, security features, etc., and the entire system has to be transparent to meet the industry standards.

Single-Agent vs Multi-Agent Systems: What to Choose and When

CTOs and CIOs should carefully choose between single-agent vs multi-agent systems, not just based on the existing infrastructure, but based on the long-term objectives and plans. While startups can rely on single-agent systems, a growing organization is more likely to benefit from multi-agent models. Let’s check out when to choose the agents.

Single-Agent Systems

Well-Defined Workflow Patterns

An enterprise with well-defined workflows and predictable patterns or fixed sequences will find it effective to use single-agent systems, as they can easily handle straightforward tasks and enhance productivity.

Cost Constraints

Single AI agent development is a preferred choice when businesses have a limited budget to build and deploy advanced tools. It is still powerful enough to be customized for specific needs and can deliver quality output.

Faster Results

Single-agent systems are relatively simpler and can be built in less time, thus reducing the time to market. The product can be quickly launched and used in the business. It is helpful when you are on a tight schedule.

Narrow Domain Use Cases

Single-agent systems are beneficial if department heads and directors want to implement AI solutions only for specific use cases instead of maintaining an extensive setup with complex functionalities.

Multi-Agent Systems

Multi-Domain Scaling

Multi-agent systems are not limited to specific use cases or departments. They can be scaled horizontally and implemented in different departments to streamline and automate various tasks and reduce employee workload.

Diverse Teams Collaborations

Enterprise AI solutions providers recommend building multi-agent systems when individual teams with specialized responsibilities work on the same project and have access to the same setup. This allows greater collaboration irrespective of time zones and location.

Scalability and Future-Proofing the System

Multi-agent systems can be easily scaled as the enterprise expands. Executives use them to future-proof infrastructure and processes, ensuring daily activities are performed seamlessly and without interruption.

Several Data Sources

A multi-agent system is a more convenient choice if you collect data from several sources and process large datasets. The decentralized framework allows the agents to access data to complete their tasks and give the output.

Faster Execution and Better Fault Tolerance

Since multi-agent systems use multiple agents to perform a task, they can process information in parallel, reducing the overall time required to complete a complex task. Moreover, even if there’s an issue or error in one agent, the other agents in the system can rectify it and ensure the final output is reliable.

Conclusion

The choice between single-agent vs multi-agent systems depends on the complexity of the tasks, scalability, and performance goals. Both systems have their advantages and offer great benefits to the enterprise. However, a multi-agent system is a preferred choice for growing organizations with varying demands.

In either case, executives will find it beneficial to hire an intelligent automation solutions provider to build the systems and implement them in the organization. This also ensures long-term support and maintenance, as well as ongoing optimization and development to keep the AI systems aligned with your business values and objectives.

More in AI Product Development Services Providers

Enterprise AI product development services are aimed at providing full-spectrum, end-to-end solutions, from strategy creation to designing, building, deploying, and integrating the AI tools into the business systems. Additionally, service providers take care of data security and a governance framework to ensure compliance and reduce risks. Tailored AI product development services empower organizations to gain a competitive edge and accelerate success