ETL vs. ELT: Rethinking Data Integration for Performance and Scalability

In today’s data-driven world, integrating information from various sources like Microsoft Dataverse and other cloud or on-premises platforms is no longer optional—it’s essential. However, in complex environments, data ingestion often becomes more challenging than expected. It’s not the big architectural decisions that slow us down, but rather the compounded effect of small, overlooked inefficiencies—such as unnecessary fields, poorly timed loads, or rigid transformation steps.

When faced with these challenges, the default response in many teams is to reach for the traditional ETL (Extract, Transform, Load) approach. While ETL has served us well for decades, it’s not always the best choice—especially in modern, cloud-native architectures. It’s time to take a fresh look at ELT (Extract, Load, Transform) and understand why it often outperforms ETL, both in efficiency and scalability.

What’s the Difference between ETL and ELT?

Transformation Location

ETL: Occurs before loading data (external engine/staging)

ELT: Happens after loading inside the destination system

Processing Load

ETL: Heavy on the source and transformation engine

ELT: Heavy on the destination (e.g., cloud warehouse)

Latency

ETL: Higher, due to pre-load transformations

ELT: Lower, due to direct loading before transforming

Best Use Cases

ETL: Legacy systems, smaller data sets, strict source rules

ELT: Cloud-native setups, large-scale data, real-time ingestion

Scalability

ETL: Limited by source/staging capacity

ELT: Scales easily with destination compute power

Real-World Insight: A Case from the Field

In one of the complex integration projects, the data was pulled from Dataverse and other operational sources into a central analytics platform. Initially, a traditional ETL pipeline was used:

1. Data was extracted from data sources,

2. Transformed,

3. Then loaded into a destination.

The process was painfully slow and created significant load on the source systems, sometimes even if failed. Execution times for even modest data loads stretched into hours, and debugging transformation issues became a major time sink.

After re-evaluating our pipeline, I shifted to an ELT model:

• We extracted only the necessary fields from the source—no more "select *".

• We loaded raw data directly into the destination.

• We then applied transformations inside the warehouse, using its built-in, scalable processing power.

The result? An 80% reduction in execution time. Even more importantly, it freed up the source systems, reduced transformation complexity, and made our pipeline easier to monitor and maintain.

Key Recommendations for Smarter Data Integration

Whether you’re using ETL or ELT, here are some lessons learned and best practices:

1. Only Extract What You Need Avoid the temptation of extracting entire tables. Pull only the columns and rows relevant to your analysis.

2. Use Incremental Loads When Possible Pull only new or changed data using timestamps or change tracking to minimize processing time and bandwidth.

3. Push Processing to the Destination Let your data warehouse or lakehouse do the heavy lifting. Most modern platforms are designed for this.

4. Profile Your Data Early Understand data quality and structure before designing transformations—especially if you skip a staging area.

5. Plan for Data Governance and Lineage In ELT, raw data lands in your destination untouched. Make sure you have metadata, lineage, and access controls in place.

6. Monitor and Optimize Continuously monitor pipeline performance and adjust queries, indexes, or schedules to avoid bottlenecks.

Final Thoughts

The choice between ETL and ELT isn't just about tools—it's about strategic alignment with your infrastructure, performance goals, and data volume. In the era of cloud computing, ELT provides the flexibility and scalability required to meet modern data demands.

If you’re struggling with slow pipelines or overworked source systems, don’t just tweak your ETL—rethink your architecture. The shift to ELT could be the key to unlocking faster, leaner, and more resilient data integration.

AI Strategy: It’s About Value, Not Just About Models

AI strategy isn’t just about building sophisticated models; it’s about creating tangible value for the business. I recall my BSc studies, particularly in the business analysis and design course, where we worked with various organizations to implement automation processes. At the time, our focus was primarily on the technology we used and the cost savings we achieved for the company. These savings were mainly measured in terms of direct financial benefits, such as cost reduction. However, looking back, I now realize that we missed a crucial aspect - the true value we could have provided to the organization: streamlining processes, improving customer satisfaction, adding value through differentiation, and creating long-term business value.

This perspective shift became even clearer during my consultancy work, where I identified inefficiencies within business processes that led to operational gaps. These gaps, once addressed, significantly enhanced performance and outcomes. It’s not just about the tools or savings; it's about the strategic value AI can bring.

The Role of AI in Business

The same holds true for AI. Many people mistakenly believe AI will replace human workers, leading to widespread unemployment. However, AI doesn’t create value by replacing people - it enhances human capabilities. By improving efficiency, speeding up processes, and saving time, AI enables employees to focus on more valuable and impactful tasks that drive innovation and business growth.

Yet, this potential value can only be realized with a clear AI strategy. After working across various sectors, I’ve witnessed firsthand how data and information management without a strategic approach often results in costly experiments that rarely scale or deliver long-term benefits. As a data and information management professional, it’s clear that a well-defined AI strategy must align with the organization’s broader transformational goals. Without this alignment, AI adoption risks failure, as evidenced by countless cases of AI projects that have failed, whether misleading chatbots, hallucinating AI, or biased models. Premature adoption of AI can result in significant risks, including reputational and financial loss.

The key to mitigating these risks is strategic leadership - leadership that can guide AI implementation with a clear understanding of the business's priorities, identify where AI can be most beneficial, and create responsible, scalable roadmaps for its application. Above all, leaders must understand that the real challenge of AI isn’t just building models - it’s ensuring that those models align with and support the organization’s goals.

Building Trust in AI

As we continue to see AI dominating global headlines, one essential factor is becoming clear: building trust in AI is vital. AI systems must be transparent, fair, and accountable to ensure they deliver real value to organizations while maintaining ethical standards. Here are some key principles for building that trust:

1. Explainability: AI models must be interpretable and understandable, so decision-makers can trust the system's outputs. When AI systems are deployed, it’s crucial that stakeholders can comprehend how decisions are made, ensuring that AI’s actions align with business objectives and ethical guidelines.

2. Fairness: Bias in AI models can lead to unfair outcomes that harm both organizations and individuals. Ensuring fairness in AI requires carefully designed models that account for diverse datasets and avoid discriminatory patterns. An effective AI strategy incorporates fairness as a core principle, ensuring equitable outcomes for all stakeholders.

3. Robustness: AI systems must be resilient to unexpected inputs and able to perform reliably under a wide range of scenarios. A robust AI strategy ensures that models are tested for various edge cases and can maintain high performance even in challenging situations.

4. Transparency: Transparency is crucial for ensuring that AI systems can be audited and understood. Organizations must ensure that AI processes are clear, and stakeholders should be able to track the development, deployment, and outcomes of AI systems. This transparency fosters trust and accountability.

5. Privacy: Protecting the privacy of data and users is essential. AI systems should be designed with privacy in mind, ensuring that personal information is protected and handled according to ethical guidelines and regulations.

Strategic Leadership in AI

AI presents immense opportunities for businesses, but it also brings significant challenges. That’s why strategic leadership should:

• Understand and prioritize business objectives.

• Identify areas where AI can provide maximum impact.

• Develop clear, responsible AI implementation roadmaps that are scalable and sustainable.

In conclusion, as AI continues to reshape industries, it’s evident that the key to successful AI adoption is not just about building models but aligning them with business objectives. Moreover, building trust through explainable, fair, robust, transparent, and privacy-conscious AI practices is critical for long-term success.

 

Associate PowerBI Data Analyst (PL-300) Exam Tips and Challenges

 

1. Get Hands-On Experience

• Practice using Power BI regularly—real-world data projects will reinforce your understanding far better than theory alone.

2. Use Microsoft’s Official Learning Path

• Follow the structured course on Microsoft Learn: PL-300 Training Course

3. Understand the Exam Modules and Their Weights

• Know the key components and how much each contributes to your score. Focus your study accordingly.

4. Leverage Broader Data Experience

Power BI knowledge is crucial, but experience in:

• Data modeling
• Database management (SQL, etc.)
• Data storytelling and visualization design

...all contribute to better understanding and decision-making in the exam.

Common Exam Challenges

1. Scenario-Based Questions

• Some questions rely on a scenario presented earlier, but you won’t see that scenario again in follow-up questions. Try to memorize the important parts and the data structure

Leading with Data: Building the Human‑Centered AI Organization

The New Data Leadership Paradigm: Decisions Before Data

Week 1: The Data Leadership Paradigm

Don’t be “data‑driven” be decision‑driven. Align data, analytics, and AI to the decisions that matter. Build trustworthy architecture, cultivate a curious culture, and treat insights like products. Keep AI explainable and governed so people can act with confidence.

 

Why does matter

For years, organizations have said they want to be data‑driven. In reality, what they need is to be decision‑driven. Where data, analytics, and AI are mapped to the outcomes/decisions that matter. This is the heart of modern data leadership: aligning technology, teams, and governance to create measurable value while staying human‑centered.

In both humanitarian and private‑sector, the same pattern is: teams produce dashboards, reports, and models. Yet it doesn’t help leaders to act with confidence. The gap isn’t a lack of tools or technology; it’s a lack of clarity: What value we want to achieve and what decisions we trying to take? We always, think the solution is nice dashboard

The data leadership paradigm

1. Decisions before data: you should decide about the critical decisions you need to make and it’s frequencies weekly, monthly, and in a crisis. Then work backward to define the minimum viable data, metrics, and models that will improve those decisions. This prevents report sprawl and focuses teams on outcomes, not outputs. I noticed data professional and end users always jump to conclusion to decide about data fields with assumption if we have the data we can report. This behavior creates excessive data collection activities which might violate organization and country data protection standard and protocols. We should always, remember minimum data collection necessary to operate.

2. Architecture serves action: Data architecture including data integration, shared standards, and governed pipelines aren’t IT checkboxes, they’re the foundation of trust among stakeholder and building confidence on the data and generated reports. When systems interoperate and lineage is transparent, leaders can move faster because they know where the numbers come from and what they mean. Meaning, leaders know the data source and it credibility.

3. Culture over tools: A data culture isn’t about everyone learning data tools/scripting such as SQL. It’s about the culture of asking better questions, such as: What are we trying to learn? What would change our decision? What risks and biases might be hidden in our data? In many occupations, leaders forget about the impact of the biased data (refer to my post about responsible AI: here)

4. AI as a strategic ally: Predictive models, GenAI, agentic AI, and RAG can help to accelerate decisions, but only when they are explainable, governed, and responsibility used, and aligned to policy and ethics. Responsible AI means you can answer: Why did the model suggest this? Who is accountable? What’s the human‑in‑the‑loop step?

What this looks like in practice

Operating: Establish a mindset where decisions, not dashboards, are the agenda.

Product mindset: Treat key insights like products with owners, roadmaps, SLAs, and user feedback - not one‑off reports.

Capability building: Invest in data literacy for leaders and domain literacy for data teams, so each side speaks the other’s language. Refer to my previous post here

Governance that enables. Write policies that are practical, measurable, and automated where possible - making the responsible path the easiest path.

Leadership sets the tone

When leaders insist on clarity, ask “what will we do differently?” and hold teams accountable for outcomes, data becomes a strategic asset - not a museum of charts.

What’s next in this series

Over the next nine weeks, Leading with Data: Building the Human‑Centered AI Organization will unpack these themes - from breaking data silos and turning analytics into action, to AI strategy, governance, ethics, and sector‑specific lessons. The goal is to share practical playbooks that help leaders move from aspiration to execution - responsibly, transparently, and at human scale.

 

Have a question you’d like covered? Drop it in the comments and I’ll weave it into upcoming articles.