Posts

You run a query to get total revenue per customer. Customer #1 should have $500 in orders. Your query says $1,500. The raw data checks out. So what is wrong here? You just hit the fan trap — a sneaky SQL join issue that multiplies your numbers without any warning. Let me show you how it happens and how to fix it. 🤔 What Is the Fan Trap? The fan trap happens when you join tables along a one-to-many relationship and then aggregate. The “many” side fans out the rows from the “one” side, duplicating them before your SUM or COUNT ever runs. ...

You run a query to calculate total sales for Order #1. The result shows 16 items sold when your customer only bought 8. You check the database - the raw data is correct. So why is your query playing mind games? Welcome to the chasm trap. It’s a data modeling issue that silently doubles (or triples, or worse) your aggregation results. Let me show you exactly what’s happening and how to fix it. ...

🎯 Introduction “I’ll just throw it in DynamoDB.” I’ve heard this line dozens of times from engineers building AI agents. It sounds reasonable. You need to store conversation history, maybe some user preferences. DynamoDB is fast, scalable, and you already use it. How hard could it be? Here’s the thing: agent memory isn’t just storage. It’s what separates an agent that forgets everything you said five minutes ago from one that actually remembers your last conversation, pulls up context from three weeks ago, and knows which details matter and what data is fresh. ...

The 87-Millisecond Gap Your database says it’s 10:00:00.000. The atomic clock in Colorado says it’s 10:00:00.087. The difference that had been made? A melting glacier in Greenland, an earthquake in Chile, and a $2 quartz crystal vibrating inside your server. Somewhere in that 87-millisecond gap, a $50,000 transaction just disappeared from your revenue report. Here’s what happened: You processed the same Kafka topic twice. Same code, same data, same time range. First run reported $10.2M in transactions. Second run reported $11.4M. You were missing $1.2M worth of payments, and nobody noticed for three months. ...

Your data quality dashboard shows all green. Your pipeline just merged duplicate records and nobody noticed for a week. Or maybe it’s the opposite. Your unit tests all pass. You deploy with confidence. Then your pipeline breaks in production because the upstream API changed a field name. Does this bring vivid memories? 😊 Here’s the fact: most data engineering teams either over-rely on data quality checks or confuse them with unit tests. ...

Remember when chatbots were the future of customer service? Fast-forward five years, and we’re already talking about AI agents that can handle your entire sales pipeline, analyze market data, and even make procurement decisions without human intervention. This isn’t science fiction. Companies like Salesforce and Microsoft are rolling out agentic AI systems that go far beyond answering “What are your hours?” They’re building virtual employees that think, plan, and execute complex business tasks. ...

What if I told you that you could run vector searches directly on S3 without spinning up a single database or compute cluster? For years, we’ve been stuck with a painful pipeline: extract data from S3, chunk it, generate embeddings, load everything into OpenSearch or Pinecone, and manage all that infrastructure. Amazon just changed the game with S3 Vectors – it’s S3 that can do vector math natively, no compute engine required. This means up to 90% cost savings and zero infrastructure management. Let me show you exactly how this works and why it might replace your vector database entirely. ...

Ever had a Spark job that keeps re-reading the same data over and over? You might need caching. But cache at the wrong time, and you’ll actually slow things down. Here’s when caching helps, when it doesn’t, and how to use it right. This blog will be short but sweet! 🔍 What is Caching? Think of caching like keeping your frequently used files on your desk instead of walking to the filing cabinet every time you need them. ...

Introduction Have you ever wondered how Pinterest finds visually similar images or how Google Photos recognizes faces across thousands of pictures? The technology that powers these features isn’t magic—it’s vector similarity search. Today, modern vector databases make it possible for developers to build these powerful visual search capabilities without needing a PhD in computer vision. In this post, I’ll guide you through the process of building your own image similarity search engine. We’ll cover everything from understanding vector embeddings to implementing a working solution that can find visually similar images in milliseconds. ...

Introduction Picture this: Your marketing team needs customer data for an upcoming campaign. You submit a request to IT, initiating a complex process that involves multiple teams, approval chains, and coordination across departments. The request joins a backlog of similar requests, each requiring data team resources. This scenario plays out daily in enterprises worldwide. What should be simple data requests turn into lengthy processes that can stretch for weeks or months. ...