From my experience, it's hard to find a big data processing job that doesn't involve grouping data. Think about customer transactions, user activity, or application logs. We often need to bring related records together to understand what happened. Data skew happens when some groups contain significantly
Software teams spend a lot of time testing whether code works correctly. We write unit tests, integration tests, review business logic, and test edge cases. But we spend far less time asking: When the software fails, does it leave enough evidence behind to understand what happened? I was discussing this
When people think about analytics, the natural instinct is to use all the data. But that is not always necessary. Sometimes you are not trying to calculate an exact number. You are trying to see a signal, understand a trend, or figure out where to investigate next. That is where
The versioning problem we ran into while porting Zinzu to a containerized Native App What happens when you deploy new code while the old code is still running? In a simple application, you may not think much about it. You deploy Version 2, replace Version 1, and move on. Long-
In serious data engineering, you often deal with massive datasets and complicated processing logic. When a functional bug appears, I repeatedly see engineers make the same mistake: they rerun the entire large-scale job after every attempted fix. This is a lazy and expensive way to debug. Every full-scale
A customer doesn’t simply abandon a purchase. They search, view a product, encounter friction, retry, and then leave. A transaction doesn’t simply fail. It moves through a series of states before reaching that outcome. A system doesn’t suddenly become unavailable. A chain of deployments, warnings, latency changes,
Meta’s legal battles over children using its platforms raise a much bigger question: How does any internet company actually know how old a user is? Most online services ask for a date of birth during signup. But that does not prove anything. A 12-year-old can simply enter
I interact with people every day who talk about data lakes and lakehouses. Within a few minutes, it often becomes clear that they do not fully understand what those terms mean. When someone keeps relying on fancy terminology instead of explaining the underlying idea, it usually means the concept is
Leverage looks attractive when stocks are rising. A stock rises 5%, and a 2× leveraged ETF may rise roughly 10% that day. Investors make money faster without needing twice as much capital. But leverage works exactly the same way in reverse. South Korea recently provided a dramatic example of what
DATA ENGINEERING & ARCHITECTURE Why applying relational database update patterns to cloud lakehouses causes write amplification, high compute costs, and degraded query performance. Apache Iceberg is getting a lot of attention—and for good reason. It brings capabilities such as ACID transactions, snapshot isolation, schema evolution, time travel, and support
Columnar storage formats like Parquet and ORC, and columnar execution engines such as BigQuery, are widely used in analytics systems because they store data column by column on disk instead of row by row. When a query reads only the columns it needs, only those columns are read from disk,
Conversations about data formats often focus on Parquet versus Avro, or columnar versus row-oriented storage. What is discussed less often is the block-based structure that actually enables files to be split and processed in parallel across distributed systems. This article explains that underlying mechanism. A block (e.g.