Simple BI

Let’s get to the point: you’ve got data problems, and I’m here to solve them.

As Simple BI's Principal Consultant, I help companies like yours turn data into actionable insights. Here’s a look at some of my recent work:

Westlake Chemical
-Designed a self-service Power BI architecture, integrating Snowflake and SAP, led the adoption initiative across North America. 
Results: Unified data access, reduced report creation time, and empowered teams to make data-driven decisions.

MSA Safety
-Built Power BI dashboards to standardize KPIs and track productivity across global plants. 
-Developed Power Apps for data capture at the plants and centralized data in Dataverse. 
Results: Consolidated reporting, identified efficiency improvements, and eliminated manual data entry, boosting productivity.

Wisconsin Department of Military Affairs
-Replaced a paper-based process with a fully automated, cloud system with Power Apps, Power Pages, Power Automate, Dataverse. 
Results: Cut application processing time by 3 weeks, saving over $35k+ annually, drastically improving service.

Microsoft
-Streamlined report development by refining the Business Analysis process and optimizing Power BI with Dataflows and Data Marts. 
Results: Reduced development cycles by 2 weeks, enhanced self-service analytics and User Experience.

Waste Management
-Modernized legacy reports into dynamic, user-friendly Power BI dashboards, simplified 120+ semantic models, consolidated 180+ dashboards to improve governance and security. 
Results: Enhanced report usability, empowered teams with real-time insights, and reduced report redundancy, saving storage and improving data governance.

I’ve also worked with Smile Sonrisa Dental, Dan’s Management, My Everything Store, Transdev Canada, and Sera Tech, helping them harness the full potential of their data. 

If you’re ready to turn your data into your greatest asset, let’s connect. Shoot me a message, and let’s get started on solving your toughest challenges.

Power Platform & Fabric Principal Consultant

Core BTS

Looking back at my time with Core BTS, one project really tested my skills: Kroger Pharmacy. They needed two data marts—one for CDC Controlled Substances and another for The Little Clinic. The project was behind schedule, with potential penalties looming if we missed the deadline. And strict HIPAA compliance requirements added an extra layer of complexity.

Kroger had recently shifted from Data Vault to Dimensional Modeling, so we had to rethink the architecture fast and develop strong logical and physical data models. Using the Azure Data Platform, we built a new structure from the ground up: Azure Data Factory, and Databricks to move and transform data from the Data Lake into Azure Synapse Analytics. It was all about precision, security, and speed. 

In the end, we delivered on time, avoiding penalties and giving Kroger a scalable, efficient solution. This experience taught me the critical importance of data modeling, project management under pressure, and clear communication with stakeholders.

Later, I tackled a project with Subzero Group, migrating six legacy Crystal Reports to Power BI. Their reporting was clunky and outdated. By leveraging Power BI’s Dataflows and Datamarts, I turned these reports into scalable, real-time analytics tools that gave Subzero’s team the insights they needed, when they needed them.

Fast forward to today, whether I’m working with Microsoft, MSA, Westlake Chemical or Waste Management, I’m not just delivering projects; I’m crafting data solutions that scale, adapt, and drive lasting value. I know what’s at stake with data accuracy, deadlines, and compliance—and I draw on my past experiences to ensure my clients aren’t just keeping up; they’re staying ahead.

That’s the power of hands-on experience. I’ve seen how smart data solutions can transform businesses, and now I’m excited to help my clients turn their data challenges into opportunities for growth and success.

Sr Data Analytics Engineer

Insight

Working with Insight Enterprises —a leader in digital solutions— gave me hands-on experience with transformative tech projects. 

One of the biggest challenges was migrating TruGreen from Tableau and ThoughtSpot to Power BI. I began by assessing their needs, gathering requirements, and developing a roadmap for each migration. I optimized SQL scripts, adopted star schemas, and reusable semantic models ensuring the new Power BI systems were both robust and user-friendly.

One project that stands out was building a forecasting app that integrated Power BI with Power Apps for Tricorbraun. This tool allowed their global sales team to create forecast scenarios and write them back to an Azure SQL Database in real time from the Power BI interface. Seeing users build and adjust forecasts on the go—and with such ease—showed me the power of combining services of the Power Platform.

Another rewarding experience was with the LA City Fire Department. I designed geo-location maps in Power BI that displayed each unit’s real-time location and status, using Azure Databricks’ streaming capabilities. I also implemented Tabular models in Azure Analysis Services, enabling the fire department to analyze data more effectively, enhancing both speed and accuracy in critical decisions.

The result? I delivered scalable solutions that improved data insights, cut down on reporting time, and allowed clients to operate more efficiently. These projects at Insight Enterprises strengthened my consulting skills, data modeling expertise, and gave me experience managing complex stakeholder relationships—skills I now bring to every Simple BI project.

Today, as Principal Consultant, I bring this knowledge to clients like Westlake and Waste Management. I help them transition from outdated systems to scalable, cloud-based analytics that transform how they operate, make decisions, and grow. It’s all about leveraging experience to build future-ready solutions that help clients stay ahead of the curve.

BI Data Analytics Engineer

SMILE SONRISA DENTAL CENTER, P.C.

As a part-time Sr Business Intelligence Consultant in Smile Sonrisa Dental, one of the largest dental practices in Houston, I led, designed and implemented a custom business intelligence solution, enabling the management team to better understand their main KPIs and their drivers; in addition, I improved/automated several administrative tasks that saved a good amount of hours to this practice. Some of my more important achievements are:

• Designed and developed several Power BI reports showcasing the practice's main KPIs, providing the management team with a modern and efficient platform to understand their business and make informed decisions.
• Increased sales efficiency rate from 25% to 42% by identifying the dependence between treatment value and acceptance through regression models in Power BI and thus, adjusting the treatment proposal process.
• Slashed $3,000 per month by automating manual processes through Macros implementation in Excel (VBA).
• Trained the management team in the use and management of the dashboards and reports and, more importantly, helped them understand the importance and meaning of each KPI (from the business point of view) and the importance of the data being collected by the different business functions; through collective workshops and individual trainings.

Senior Business Intelligence Consultant

• Enabled management to make information-backed decisions by developing a BI platform in Power BI.

Business Intelligence Intern

Robert Half Management Resources

Working with Brock Group Texas as a consultant for Robert Half was a crash course in both technical skills and cultural adjustment. Coming from a Latin background, I found American office culture surprisingly distant. At Brock’s office, I rarely saw my supervisor, who was often visiting plants across the Gulf region. Feedback was hard to come by, which forced me to be resourceful and make decisions independently.

The project itself, however, was an opportunity to make a meaningful impact. Brock was using paper timesheets. I led the development of an electronic timesheet solution in Power Apps, which automated time tracking, saving over 800 hours of work in 2019—amounting to $12,000 in savings. 

At the same time, I worked on another critical project: analyzing cost drivers in maintenance projects executed at LyondellBasell's plats. Using Power BI and Excel, I built cost models that helped project managers optimize productivity and keep costs down. This was my first deep dive into data analytics on a large scale, and it taught me the value of connecting digital tools with practical, on-the-ground insights.

These projects came with their share of challenges. Navigating cultural differences, learning U.S. business practices, and managing stakeholder expectations stretched my skills and strengthened my resilience. I learned to collaborate with both field workers and office staff.

Today, as Principal Consultant at Simple BI, these experiences shape my approach with clients like Waste Management, Westlake Chemical and MSA. I bring a focus on efficiency, scalable solutions, and the ability to adapt quickly to different business environments. I know firsthand how the right digital tools can transform processes, save resources, and empower teams—whether they’re in the office or out in the field. 

My journey with Brock Group laid the foundation for the work I do now: helping businesses leverage data and technology to overcome challenges and drive meaningful change.

Business Intelligence Consultant

Tory Technologies, Inc.

Delivered data-backed recommendations which led to enhanced user’s experience, reduced clicks per transaction, leaner interfaces, shortlisted alternatives, and auto-filling capabilities, by means of multivariate quantitative analysis, decision trees models using R, Python.

Data Analytics Intern 

Agregados Rio Negro Cundinamarca SAS

In Agregados Rio Negro Cundinamarca SAS, a construction materials producer, it was my goal to optimize the production process, increase the quality of the materials produced, all this while decreasing the unit cost of production. During this time, my most important achievement was:
• Optimized and re-designed the hauling, stocking, re-loading operations through a detailed data analytics and methods engineering study, eliminating inefficient steps in the process, leading to an increased output of 8% per year and a reduction in cost of 3% of the total cost of production.

Optimization & Business Intelligence Manager

Various Companies

Contract Administrator, 9/2011 to 3/2013
SNC-Lavalin International – Coal Mining - Colombia

Procurement Coordinator, 2/2010 to 5/2011 
Frontera Energy Corp. (Pacific Stratus Energy) – Oil & Gas - Colombia

Various Positions

The Power BI Whisperer | Power BI, Fabric Principal Consultant @Simple BI Inc

Let’s get to the point: you’ve got data problems, and I’m here to solve them. 
 
Imagine running an operation where every decision is backed by clear, actionable insights.  

No more guesswork.  
No more surprises.  

Just a seamless flow of information that drives your company forward. 

I'm David Giraldo, Principal Consultant of Data Analytics at Simple BI.  

In every corner of the manufacturing world, I’ve seen: 
 
• Unreliable demand forecasts 
• Constant stockouts that leave customers frustrated 
• Capacity constraints bottlenecking your production, eating into your profits  

Over the past few years, I've helped companies like Microsoft*, Westlake Chemical*, Waste Management*, MSA The Safety Co*, Sub-Zero Group*, Tricorbraun*, and Kroger Pharmacy* not just navigate these issues but turn them into strengths. 
 
Here’s how I solved them: 
 
1. We start with your data.  
 
Whether it’s buried in SAP, outdated systems or siloed in 1,000 Excel files across your organization, we dig in and bring it all together.   

From there, we design advanced analytics solutions using Azure Data Platform, Microsoft Fabric to give you a 360-degree view of your operations. 
  
2. We build clarity.  

We create reports in Power BI that deliver the exact insights you need to: 
 
• Optimize your supply chain 
• Predict demand more accurately 
• Manage your inventory in real time 
 
3. We drive action.  

We work with your team to automate processes and simplify operations so you can focus on what matters:  

Growing your business without the operational headaches. 
 
My goal is simple:  

I help manufacturing leaders turn data into decisions that don't just keep businesses running, but position them to thrive in an increasingly competitive market. 
 
Ready to see how your data can work as hard as you do? 

Let's chat. Send me a message here on LinkedIn or visit www.simplebi.net. 
  
*Through third-party

David Giraldo

Every BI leader I talk to has the same dirty secret this year: 

They’re waiting for a visa, and losing six figures to delay while spreadsheets take over. 

H‑1B approvals are now a nine-month gamble.  

In the meantime, critical business decisions are starved of answers – and shadow systems creep in, fast. 

I wrote my new ebook – “BI After H‑1B” – for teams who can’t hire, can’t wait, and can’t afford another quarter of rot. 

Not theory. Not thought leadership. Field-tested fixes I’m shipping with clients, right now. 

You’ll get: 

👉🏻 The 30-day triage plan for teams stuck in “immigration limbo”  

👉🏻 Real-world case studies: see how teams cut refresh times from 42 to 9 minutes and saved hundreds of thousands after hiring stalled 

👉🏻 A nearshore delivery playbook: how to ramp up skilled BI support within two weeks – even if headcount is frozen and visas are in limbo 

👉🏻 Pricing, setup time, and retention risk – crunched: use the included breakdowns to make a credible business case for getting BI moving, not stuck in red tape 

If your BI team is waiting for the next H‑1B, you’re already behind. 

If you want the PDF, comment “EBOOK” or DM me.

Saved over $500k for clients with 25+ reporting and data analytics solutions | Azure, Power Platform & Fabric Consultant

The number your CFO actually cares about: cost of delay. 

And with the recent visa mess caused by H-1B, it’s even more important: 

Every time your new BI hire is stuck in “visa purgatory,” your CFO’s budget quietly bleeds out the side.  

Most teams brag about getting a great deal on day rates, but nobody wants to tally up the real price: four weeks of frozen reporting, blocked decisions, execs yelling for answers. 

Here’s what nobody puts in the RFP:

👉 Six months of stalled hires = $1M+ in lost margin, missed opportunities, and yet another shadow spreadsheet. 

👉 “Just wait for the visa” – meanwhile, your most valuable KPIs rot. The business loses trust, and by the time your new analyst finally arrives – the backlog doubled.  

No one remembers what “monthly revenue” even means. 

There’s a smarter math. Here’s what I use: 

• Count the decisions blocked this month (not tasks – calls that move dollars). 
• Estimate $ impact per week, per decision. 
• Multiply it out: blocked × $/week × 4 = cost of waiting. 

The fix is faster than you think. Nearshore pods ship in 10 days, not 2 quarters. They pay for themselves before HR gets a visa update. 

So: Are you still comparing vendors on day rate – or are you ready to put a dollar sign on waiting?

If one person holds your model in their head, the H-1B hiring freeze just turned them into a single point of failure. You know the pattern:  
 
Refresh fails at 6:05, a schema shifts, a KPI drifts 2–3%, and only one person knows the fix.  

They’re on PTO. Or in another meeting. Or they left. 
 
Here’s how I de‑risk this without adding headcount. 
 
Run the quarterly bus test and role swap: 
 
• One day per quarter, your hero is hands off. A second analyst runs the show. 

• They must ship one change and recover one failure using docs only. 

• Track where they get stuck past 15 minutes. Those are your documentation gaps for the next sprint. 
 
Also, bake resilience into your Definition of Done. More in the playbook below: 
 
––– 
PS. If your hero took two weeks off tomorrow, what would break first?

De-risk your BI (without adding headcount)

Do the Data

Founder

Deloitte

Data Analytics Manager

Novartis

Lead a team of data analysts and oversee the design and implementation of healthcare data analytics projects

Develop and maintain healthcare data management systems, ensuring data accuracy, completeness, and security

Develop and implement data analysis plans to support clinical and operational decision-making processes

Collaborate with stakeholders across the organization to identify and prioritize business intelligence needs, and ensure that data analytics projects align with the overall strategic goals of the organization
Analyze large datasets using statistical and data visualization tools to identify trends, patterns, and insights that can be used to drive business performance

Ensure compliance with data privacy and security regulations such as HIPAA, and work with legal and compliance teams to address any data-related issues

Develop and deliver presentations to communicate complex data analysis to non-technical stakeholders, including executive leadership, and make recommendations for process improvements based on data-driven insights

Stay up-to-date with the latest trends and technologies in healthcare data analytics and provide guidance on how the organization can leverage them to improve performance and patient outcomes

Data Analytics - Turning Coffee into Insights, One Caffeine-Fueled Query at a Time! | Healthcare Data | Financial Expert | Driving Business Growth | Data Science Consultant | Data Strategy

As a data analytics professional, I have a passion for turning raw data into meaningful insights that drive business decisions. With my experience in data visualization, statistical analysis, and data modeling, I can help your organization harness the power of data to optimize operations, improve customer experiences, and achieve your strategic goals.

🔍 Discovering Insights

My expertise lies in exploring complex data sets, uncovering hidden trends and patterns, and providing actionable insights that support informed decision-making. I can help you answer questions like "What are our customer's buying habits?" or "What factors are impacting our sales performance?", and use data to guide your business strategy.

🎨 Data Storytelling

I believe that data storytelling is an art form, and I love using creative and engaging data visualizations to communicate complex information in a way that's easy to understand. Whether it's through interactive dashboards or custom reports, I can help you see your data in a new light, and use it to tell the story of your business.

🧠 Continuous Learning

I'm a lifelong learner, and I'm always exploring new data tools and techniques to stay at the forefront of the industry. Whether it's machine learning, artificial intelligence, or predictive analytics, I'm excited to apply the latest advancements to help you gain a competitive edge.

🤝 Let's Connect

If you're looking for a data analytics professional who can help you unlock the power of your data, let's connect! Whether you're a startup or a large corporation, I'm excited to collaborate with you and help you achieve your business goals. Feel free to reach out at Ravenaravs@gmail.com

Ravena O

Python for Data Professionals: Why It’s the Preferred Choice

📍 Why is Python the go-to language for data roles?

Python isn’t just a programming language—it’s an essential tool for data professionals. Its simplicity, versatility, and vast ecosystem of libraries make it a top choice for handling complex data challenges with ease.

📍 Where does Python fit in the data ecosystem?

🔴 Data ingestion, cleaning, and transformation
🔴 Storage and orchestration
🔴 Visualization and reporting
🔴 Machine learning and model deployment

From building ETL pipelines to creating interactive dashboards, Python empowers data teams at every level.

📍 Why choose Python over Scala/Java?

While Scala and Java are excellent for large-scale data processing and system robustness, Python stands out for its flexibility. It’s a universal tool, adaptable across various data domains, making it the preferred choice for many professionals.
📍 Key data roles where Python is invaluable:
🔴 Data Analytics – Analyze data using Pandas, NumPy
🔴 Data Visualization – Create stunning visuals with Seaborn, Matplotlib, Plotly
🔴 Machine Learning – Build models with Scikit-learn, TensorFlow, Keras
🔴 Web Scraping – Extract data using Scrapy, BeautifulSoup, Selenium
🔴 Data Engineering – Implement pipelines with Spark, Kafka, Flink
🔴 Databases – Work with SQLite, PostgreSQL, MongoDB, MySQL

📍 Top free platforms to upskill in Python:
 - realpython.com
- pythonprogramming.net
- datacamp.com (free basic courses)
- w3schools.com/python
- https://lnkd.in/gCDhJvA2
- https://lnkd.in/grqYWfGW

 📍 Follow these Python experts for insights: 🐍

Matt Harrison, Khuyen Tran, Guido van Rossum, Dan Bader

Python remains an essential skill for data professionals—whether you're analyzing, engineering, or visualizing data, it has the tools to get the job done!

CC:Brij Kishore Pandey

Ravena  O

How do we move from playing with AI agents to actually running them in production on AWS. The new AWS Marketplace and Agentic AI updates are basically trying to solve that gap. I am blown with 2300+ AI Agents and Tools listing that I saw in the Keynote announcements

You can even learn more about it here - https://lnkd.in/dkmvXM9m

What does this mean? Let me break it down

- Marketplace now has a clear area for AI agents and tools. Not just random listings. You see agents, products with agents inside, tools, and services in one place, sorted by what they help you automate

- Discovery feels more like “tell me the problem” and less like “guess the right keyword.” With things like agent mode, you describe what you want and get a few real options instead of pages of noise

- For a growing set of partners, you can land all this on Amazon Bedrock AgentCore, so you are not rebuilding identity, memory, actions, and monitoring from scratch every time

On top of that, AWS has added new Agentic AI categories in the AWS AI Competency. One for applications, one for tools, one for consulting.

If you are here at re:Invent and trying to figure out where to start with agents on AWS, I would use those two signals as a filter.

Check who is in the Agentic AI categories and how they show up in Marketplace, then go talk to those teams first.

#awsreinvent #aws AWS Partners Amazon Web Services (AWS) #awspartners #ravenaondata

We throw around the term “AI agents” a lot, but this visual breaks down how we actually got here and where we are going.

Here is how I read stages in the image.

- LLM processing flow
This is the basic setup most people started with.
You type a prompt, the large language model processes it, and you get a text answer.
Helpful, but it has no real view of your world, your data, or your systems.

- LLM with document processing
Next, we plug in longer documents.
The model can now read PDFs, reports, knowledge bases and respond using that content.
This is where many “chat with your documents” tools live today.
Still, it is mostly a single shot question and answer pattern.

- LLM with RAGs and tools
Now we add retrieval and tools.
Retrieval means the agent can search your own data sources in real time instead of relying only on training data.
Tools mean it can call APIs, run code, hit a database or a SaaS application.
This is where the system stops being just a chat box and starts doing work.

- Multi modal LLM workflow
Here the model works across different input types, not only text.
It can look at images, screenshots, maybe audio or video, and combine that with text.
You also see memory and a more complete workflow.
The system can keep track of previous steps and use them to answer better next time.

Advanced AI agent architecture
In this stage, the agent does more than respond.
It can:

remember past conversations and actions

break a task into smaller steps

choose when to use tools, when to ask for more info, and when to stop

produce structured outputs that plug into other systems
This is closer to a digital teammate that can own a small business workflow end to end.

- Future AI agent architecture
The last box is where things get really interesting.
You do not have a single agent. You have many agents working together behind the scenes.
On top of them, there is an output layer that checks for safety, control, responsibility and interpretability.
This layer makes sure answers are not only useful, but also explainable, auditable and aligned with company rules.

For me, this evolution is a useful lens to judge any “AI agent” product. I ask myself where it sits on this path and what is missing for it to become a reliable, safe part of real business workflows.

CC: Brij Kishore Pandey

Agentic AI just got a scoreboard at Amazon Web Services (AWS). At re: Invent AWS has formally recognized 60 partners in new Agentic AI categories inside its AI Competency. In a crowded AI market, this is one of the first clear signals of who can actually deliver working agent systems on AWS, not just demos. Check out more details here – https://lnkd.in/dk34XpGK

At a high level, AWS is doing three things here:

- Setting a higher bar for what “agent ready” means
- Sorting partners into clear categories that match how customers actually buy
- Connecting this to real demand for AI agents in AWS Marketplace

The competency breaks partners into three groups:
- Agentic AI applications
Vendors who ship products where agents are already solving real business problems end to end

- Agentic AI tools
Building blocks that help teams design, orchestrate, and monitor agents on AWS.

- Agentic AI consulting services
Partners who know how to design architectures, handle data access, and take these systems into production.

The interesting part is how this lines up with what we are already seeing in AWS Marketplace.

AI agents on Marketplace have grown far faster than expected, with adoption numbers reported at more than 40 times the internal targets. That tells you customers are not just experimenting. They are actively looking for packaged ways to bring agents into their environments.

On top of that, Marketplace has been evolving beyond single products:
Solution based offers where customers can buy a whole stack in one go

Bundles that combine software, services, and integration work

New “agent mode” style experiences that help teams discover and deploy agents faster

Put it together and a picture emerges:

- Marketplace becomes the front door for discovering and trying agentic solutions
- The Agentic AI competency tells you which partners have already hit a certain technical and customer bar
- The three categories make it easier to plan your mix of applications, tools, and services

Why does this matter?

Because most enterprises are now asking very practical questions:
- Where can we use agents safely in production today
- What should we buy off the shelf versus build
- Which partners have already solved problems similar to ours

The 60 partners in these new Agentic AI categories now sit at the intersection of those questions. 

This is not the end state, but it is a useful early map of who is doing serious work in agentic AI on AWS, and how buyers can navigate that ecosystem without getting lost in the noise.

#awsreinvent #aws AWS Partners Amazon Web Services (AWS) #awspartners #ravenaondata

RAG was introduced with a clear promise:
 to make LLMs smarter, give them memory, and anchor their responses in verifiable facts.
But there’s an important reality that often goes unspoken.
Most RAG implementations function like enhanced search engines.
 They retrieve documents, insert them into context, and expect the LLM to make sense of it.
That isn’t true intelligence — it’s structured copy-and-paste.
What’s emerging now represents a meaningful shift:
 agents are beginning to manage the entire retrieval and reasoning workflow.
Platforms like Glean, Perplexity, and Harvey are not simply retrieving documents.
 They are reasoning before retrieval, after retrieval, and at times choosing not to retrieve at all.

This changes the entire paradigm:

🔴 Instead of embedding every query by default, an agent evaluates:
 “What information is actually required here?”
🔴 Instead of flooding context with irrelevant chunks, it determines:
 “Which sources matter for this specific question?”
🔴 Instead of generating a single response, it reflects:
 “Did this answer fully address the user’s intent?”
🔴 Memory becomes functional:
 short-term for the current interaction, long-term for patterns across sessions.
🔴 A broader toolset becomes accessible:
 search engines, APIs, databases — the agent selects the right tool for the task.
🔴 The LLM stops being an isolated generator and becomes an integral component in a coordinated reasoning system.

This is Agentic RAG.
Not an incremental improvement in retrieval —
 but a fundamentally different architecture for enterprise intelligence.
And once you see it working inside real, complex workflows,
 traditional RAG begins to feel… noticeably incomplete.

CC: Om Nalinde

If your RAG system isn’t performing well, the problem is usually not the model — it’s the chunking strategy.

Chunking decides what your system actually “sees,” retrieves, and reasons over.
 Get it wrong, and even the best LLM will hallucinate.
 Get it right, and your RAG pipeline becomes dramatically more accurate.

Here’s a quick breakdown of the 10 major chunking techniques every RAG practitioner should know:

 ➡️ Fixed-length Chunking – Simple, consistent, great for baseline setups.
 ➡️ Semantic Chunking – Splits content based on meaning, not size.
 ➡️ Overlapping Chunking – Keeps context intact across boundaries.
 ➡️ Sliding Window Chunking – Keeps a rolling view of dynamic context.
 ➡️ Hierarchical Chunking – Multi-level and structured for complex docs.
 ➡️ Dynamic Chunking – Adapts chunk size to content length and density.
 ➡️ Sentence-based Chunking – Clean, natural breaks for readability.
 ➡️ Paragraph-based Chunking – Ideal for cohesive topics and narratives.
 ➡️ Token-based Chunking – Precise, model-friendly token-level control.
 ➡️ Contextual Chunking – Adjusts based on the specific retrieval goal.
Great RAG isn’t magic — it’s design.

 Master chunking, and the rest of the pipeline becomes significantly easier.

CC: Harshit Ahluwalia

#RAG #LLM #AIEngineering #VectorSearch

Chunking

You can build a RAG demo in an hour.
But building production-grade RAG is what interviews really test.

RAG is not just embed → retrieve → generate.
It’s a full engineering stack spanning parsing, chunking, embeddings, vector search, reranking, guardrails, latency, and evaluation.

To make your prep easier, I’ve compiled 50 real RAG interview questions—from fundamentals to deep production challenges.

Here’s what’s inside the cheat sheet 👇

🔴 1. Parsing & Data Prep
	•	PDF parsing failures
	•	OCR vs. text extraction
	•	Cleaning, deduping, noise detection

🔴 2. Chunking Strategy
	•	Fixed, semantic, recursive, hierarchical
	•	Overlap logic & boundary effects
	•	Chunking code vs. text

🔴 3. Embeddings & Vector Stores
	•	Model selection
	•	Drift, noise, normalization
	•	Index types: Flat, HNSW, IVF

🔴 4. Retrieval & Reranking
	•	Hybrid search
	•	ANN recall tuning
	•	Cross-encoders, MMR, HyDE
	•	Multi-hop retrieval

🔴 5. Latency, Cost & Scaling
	•	Query caching
	•	Parallel retrieval
	•	Token budgeting
	•	Dimensionality trade-offs

🔴 6. RAG Guardrails
	•	Hallucination control
	•	Citation enforcement
	•	Retrieval poisoning defense
	•	Jailbreak detection

🔴 7. Memory & Agentic Extensions
	•	Short-term vs long-term memory
	•	Chunk routing
	•	Agentic RAG workflows

👉 If you’re preparing for RAG-heavy roles or building real-world retrieval systems, this cheat sheet helps you understand the why behind every design choice.

🔖 Save for interview prep.
♻️ Repost to help others level up their RAG skills.

CC: Naresh Edagotti

50 RAG interview Questions 

Why Vector Databases Are Now Core to Modern AI

Vector databases quietly power the AI experiences we use every day. Behind every smart search, every RAG pipeline, and every reliable chatbot—embeddings and similarity search do the heavy lifting.

🔴 What They Do
Store and retrieve high-dimensional embeddings so systems can search by meaning, not keywords.

🔴 Why AI Uses Vectors
Embeddings capture intent, context, tone, and relationships—enabling semantic understanding.

🔴 How They Work
Embed → Index → Similarity Search → Rank → Reason.
The backbone of RAG and intelligent search.

🔴 Why They Matter
They reduce hallucinations, provide long-term memory, and enable stable, enterprise-grade reasoning.

🔴 Where They’re Used
	•	Chatbots (context + domain knowledge)
	•	Search engines (semantic + multimodal search)
	•	Recommendation systems (personalized, context-aware suggestions)

🔴 Popular Vector DBs (2025)
Pinecone, Weaviate, ChromaDB, FAISS, Milvus, Qdrant.

🔴 Key Features to Know
ANN search, hybrid retrieval, distributed indexing, sharding, real-time embedding updates, LLM re-ranking.

Vector databases are no longer optional—they’re a foundational layer of the AI stack. Understanding embeddings and similarity search is now a real competitive advantage.

CC: Greg Coquillo

Understanding Vector Database

If Data Engineering feels overwhelming, it’s rarely the tools — it’s the terminology.

Master these core concepts, and the entire data ecosystem becomes clear.
Here’s a concise breakdown of 30 fundamentals every DE, analyst, and ML engineer should know:

🟣 DATA MOVEMENT
 🔴 ETL vs ELT: ETL transforms before loading; ELT loads raw and transforms  later.
 🔴 Stream vs Batch: Stream = real-time; Batch = scheduled processing.
 🔴 Data Pipeline: Automated flow from source to destination.

🟣 DATA STORAGE
 🔴 Data Warehouse: Structured, analytics-ready (Snowflake, BigQuery).
 🔴 Data Lake: Stores raw, unprocessed data.
 🔴 Data Lakehouse: Lake flexibility + warehouse reliability (Delta, Iceberg).

🟣 DATA STRUCTURE
 🔴 Star Schema: Simple, fast for analytics.
 🔴 Snowflake Schema: Normalized variant with more joins.
 🔴 Data Modeling: Designing relationships for clean, efficient queries.

🟣 DATA PROCESSING
 🔴 OLTP: Real-time transactions.
 🔴 OLAP: Analytical workloads.
 🔴 Partitioning: Slice data to reduce full scans.
 🔴 Sharding: Distribute data across servers for scale.

🟣 DATA DISCOVERY
 🔴 Data Catalog: Metadata and dataset discovery.
 🔴 Data Lineage: Track data end-to-end.
 🔴 Indexing: Faster lookups on large tables.

🟣 QUALITY & GOVERNANCE
 🔴 Data Quality: Accuracy, completeness, consistency.
 🔴 Data Governance: Access control, security, compliance.
 🔴 Caching: Quick access to frequently used data.

🟣 SYSTEM DESIGN
 🔴 Distributed Systems: Many machines acting as one.
 🔴 Message Queue: Async communication (Kafka, RabbitMQ).
 🔴 Orchestration: Workflow control (Airflow, Prefect).
 🔴 Fault Tolerance: Operate even during failures.
 🔴 Scalability: Grow without re-architecting.

💡 Truth: Tools change constantly. These concepts don’t.
 Master them, and you’re already ahead of most professionals in the field.

Image Credits: Shalini Goyal

What happens when your AI can’t reason, can’t coordinate tools, and can’t operate safely at scale?

You don’t have an agent — you have a chatbot.
Agentic AI is evolving into a full technology stack.
Not just models, but an entire ecosystem designed for autonomous, tool-using, decision-making systems.

Here’s the stack broken down into seven essential layers:

🟡 Layer 1 — Foundation Models & Infrastructure
 LLMs, vector databases, cloud compute (GPU/TPU), data pipelines, and hosting.
 This layer provides the raw reasoning and generative power.

🟡 Layer 2 — Agent Runtime & Execution Infrastructure
 Docker, Kubernetes, workflow engines, sandboxed runtimes.
 Secure, scalable environments where agents operate autonomously.

🟡 Layer 3 — Protocols & Interoperability
 MCP, AGP, function-calling APIs, tool invocation standards, agent-to-agent communication.
 The language that allows systems to work together.

🟡 Layer 4 — Orchestration
 LangGraph, CrewAI, Semantic Kernel, AutoGPT, Camel AI, PyDanticAI.
 The frameworks that manage multi-step workflows, memory, and reasoning loops.

🟡 Layer 5 — Tooling & Enrichment
 Retrieval systems, vector stores, browser automation, APIs, scraping engines, memory layers.
 Everything that expands an agent’s capabilities and context.

🟡 Layer 6 — Applications
 Copilots, autonomous teammates, AI operations assistants, research agents, domain-specific apps.
 Where real business value emerges.

🟡 Layer 7 — Observability & Governance
 Guardrails, Lakera, LangSmith, Arize, Helicone, Langfuse.
 The safety, monitoring, and reliability layer that makes agentic systems enterprise-ready.

Agentic AI isn’t a feature or a product —
 it’s a layered architecture.

When all seven layers work together, AI gains the ability to reason, plan, act, coordinate tools, collaborate with humans, and operate safely at scale.
This is the blueprint for the next generation of intelligent enterprise systems.

Data & AI Governance is one of the most misunderstood capabilities in the enterprise.

Some see it as control. Others see it as compliance.
In reality, governance is the discipline that creates trust, alignment, and accountability—especially as intelligence becomes distributed across systems, teams, and AI agents.

Its role today is simple:
Governance ensures data doesn’t just move—it moves with meaning, securely, ethically, and with intent.

🔴 Why Governance Matters More Than Ever

🔴 Distributed intelligence needs shared accountability.
As humans, systems, and AI agents co-execute decisions, accountability can’t sit in one place. Governance is the only way to preserve intent across autonomous actors that adapt and learn independently.

🔴 Business operations now are data products.
Modern operations run on APIs, pipelines, and platform logic—not org charts. Governance ensures these data-driven processes remain intelligible, traceable, and aligned to strategic goals.

🔴 Federated models require a shared grammar.
Decentralization works only when every domain speaks a compatible language. Governance isn’t control—it’s choreography. It allows federated teams to scale without fragmenting.

🔴 Democratization requires responsibility, not just access.
Giving teams and agents more data power is cultural and ethical, not technical. Governance turns access into responsible action, ensuring coherence as capabilities grow.

The mission of Data Governance:
When intelligence scales, integrity must scale with it.
The organizations that win will be the ones where human and AI intelligence act with coherence—not in isolation.

Where does your organization stand on this spectrum?

CC: Respective Owner

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