Why Communication Is the Hard Part
Most of the conversation about data strategy at a community bank ends up in the wrong place. People want to talk about tools, vendors, dashboards, the latest architecture. The harder problem is sitting one floor up. The people making decisions do not need more data. They need a small set of things they can act on, and they need it consistently.
Raw Data Storage and When to Use What
In the previous posts, I covered transactional databases for real-time operations and data warehouses for historical analytics. Both work with structured data. But what happens when you need to store log files, images, sensor readings, or raw event streams? That's where data lakes come in, and understanding them has changed how I think about data architecture.
Turning Historical Data into Business Insights
In the previous post, I looked at transactional databases that power real-time operations. But I've learned the hard way that running complex analytical queries against production databases is a bad idea. It slows down the application and puts operational data at risk. Data warehouses solve this by providing a separate system optimized for analysis rather than transactions.
The Engine Behind Every Application
I've been thinking about data architecture lately, specifically how different systems serve different purposes. Every application I've built has relied on a transactional database somewhere in the stack. When a user updates their profile, places an order, or transfers money, a transactional database ensures the operation completes reliably. These systems, also called Online Transaction Processing (OLTP) databases, form the backbone of modern software.
