Pipelines

Data Quality: What Can Actually Go Wrong You’ve built an elegant data pipeline. Your data model is clean. Queries are fast. Your dashboard shows revenue up 50% this quarter. The Business controller makes decisions based on it. And then, three weeks later, you discover: the revenue calculation was wrong the whole time because someone entered “500,000” as “500,00” in a legacy system, and nobody caught it. That’s a data quality problem. And here’s the scary part: nobody noticed until you dug deeper. Your pipeline ran perfectly. Your warehouse loaded perfectly. Your SQL was correct. But the data itself was broken, silently, invisibly, for weeks. Think of it like a supply chain Imagine you’re running a restaurant and ordering ingredients. Good supply chain: You order 100 tomatoes. They arrive. You inspect them: most look good, but you spot 3 that are rotten. You remove them. You use 97 good tomatoes. Dinner is great. Bad supply chain: You order 100 tomatoes. They arrive in unmarked boxes. You dump them in the fridge without checking. You don’t know if any are rotten until a customer bites into a bad one during dinner. Now you have an angry customer and a problem. Your data pipeline is the supply chain. If you don’t check quality before it reaches your customers (dashboards, reports, decisions), you’ll serve them rotten data without knowing it. ...

Data Quality: What Can Actually Go Wrong You’ve built an elegant data pipeline. Your data model is clean. Queries are fast. Your dashboard shows revenue up 50% this quarter. The Business controller makes decisions based on it. And then, three weeks later, you discover: the revenue calculation was wrong the whole time because someone entered “500,000” as “500,00” in a legacy system, and nobody caught it. That’s a data quality problem. And here’s the scary part: nobody noticed until you dug deeper. Your pipeline ran perfectly. Your warehouse loaded perfectly. Your SQL was correct. But the data itself was broken — silently, invisibly, for weeks. Think of it like a supply chain Imagine you’re running a restaurant and ordering ingredients. Good supply chain: You order 100 tomatoes. They arrive. You inspect them — most look good, but you spot 3 that are rotten. You remove them. You use 97 good tomatoes. Dinner is great. Bad supply chain: You order 100 tomatoes. They arrive in unmarked boxes. You dump them in the fridge without checking. You don’t know if any are rotten until a customer bites into a bad one during dinner. Now you have an angry customer and a problem. Your data pipeline is the supply chain. If you don’t check quality before it reaches your customers (dashboards, reports, decisions), you’ll serve them rotten data without knowing it. ...

What Is a Data Pipeline, Really? By now, you’ve probably heard the terms ETL vs ELT, you know about data warehouses and data lakes, and you understand batch vs streaming processing. But here’s the thing: those are all just pieces. A data pipeline is how all these pieces fit together into one complete picture, from the moment data is born all the way to the moment someone uses it to make a decision. Think of it like a pizza delivery system Imagine you’re running a pizza restaurant, and you want to track everything that happens to every pizza. Raw data is all the orders coming in: what toppings, delivery address, time ordered. Extraction is grabbing those orders from the phone, email, and app: getting all the information out of wherever it lives. Transformation is organizing all that information into a standard format: customer name, address, toppings list, price, delivery time. Storage is putting that organized information in a safe place, maybe a database where you can look it up later. Analysis is looking at all the pizza data and asking questions: “What’s our most popular topping? Are we getting faster at deliveries? Which neighborhoods order the most?” That entire journey, from someone calling to order a pizza to you understanding your business better, that’s a data pipeline. It’s the whole assembly line, not just one machine in it. ...

What Is a Data Pipeline, Really? By now, you’ve probably heard the terms ETL vs ELT, you know about data warehouses and data lakes, and you understand batch vs streaming processing. But here’s the thing: those are all just pieces. A data pipeline is how all these pieces fit together into one complete picture — from the moment data is born all the way to the moment someone uses it to make a decision. Think of it like a pizza delivery system Imagine you’re running a pizza restaurant, and you want to track everything that happens to every pizza. Raw data is all the orders coming in: what toppings, delivery address, time ordered. Extraction is grabbing those orders from the phone, email, and app — getting all the information out of wherever it lives. Transformation is organizing all that information into a standard format: customer name, address, toppings list, price, delivery time. Storage is putting that organized information in a safe place — maybe a database where you can look it up later. Analysis is looking at all the pizza data and asking questions: “What’s our most popular topping? Are we getting faster at deliveries? Which neighborhoods order the most?” That entire journey — from someone calling to order a pizza to you understanding your business better — that’s a data pipeline. It’s the whole assembly line, not just one machine in it. ...

ELT vs ETL: What’s the Difference? If you’ve read about OLTP vs OLAP, you know that data usually starts life in an OLTP system (like the database behind an app) and needs to make its way into an OLAP system (like a data warehouse) so people can analyze it. The question is: how does that data get there? That’s where ETL and ELT come in. They’re two strategies for moving and preparing data, and the difference comes down to one thing: when you transform the data. The three steps Both approaches share the same three ingredients: Extract: pull the data out of the source system (a database, an API, a file, etc.) Transform: clean it, reshape it, join it, aggregate it, turn raw data into something useful Load: put the data into its destination (usually a data warehouse) The letters are the same. The order is not. And that small difference in order changes a lot about how the pipeline works. ETL: Extract, Transform, Load In ETL, you transform the data before it reaches the warehouse. flowchart LR A[Source System] --> B[Extract] B --> C[Transform<br/>separate processing engine] C --> D[Load] D --> E[(Warehouse)] The transformation happens in a separate system (often a dedicated ETL tool or a processing cluster) sitting between the source and the destination. By the time data lands in the warehouse, it’s already clean, structured, and ready to query. ...

ELT vs ETL: What’s the Difference? If you’ve read about OLTP vs OLAP, you know that data usually starts life in an OLTP system (like the database behind an app) and needs to make its way into an OLAP system (like a data warehouse) so people can analyze it. The question is: how does that data get there? That’s where ETL and ELT come in. They’re two strategies for moving and preparing data — and the difference comes down to one thing: when you transform the data. The three steps Both approaches share the same three ingredients: Extract — pull the data out of the source system (a database, an API, a file, etc.) Transform — clean it, reshape it, join it, aggregate it — turn raw data into something useful Load — put the data into its destination (usually a data warehouse) The letters are the same. The order is not. And that small difference in order changes a lot about how the pipeline works. ETL: Extract, Transform, Load In ETL, you transform the data before it reaches the warehouse. flowchart LR A[Source System] --> B[Extract] B --> C[Transform<br/>separate processing engine] C --> D[Load] D --> E[(Warehouse)] The transformation happens in a separate system — often a dedicated ETL tool or a processing cluster — sitting between the source and the destination. By the time data lands in the warehouse, it’s already clean, structured, and ready to query. ...