You might have heard “data mart” come up in conversations about analytics or business intelligence and wondered how it’s different from a database or a data warehouse. It’s a fair question, because the terms get muddled a lot. Here’s a clear breakdown.
Database Concepts
Column-Level Security Explained
You may be aware of a concept called row-level security, which controls which rows a user can see in a table. Column-level security is a similar concept, controls which columns are visible. It solves a different problem. Same table, same rows, but some fields in those rows shouldn’t be visible to everyone.
Think about an employees table. A manager might reasonably see a list of all staff and their departments. But salary? National ID numbers? Personal contact details? These should be visible to the manager, but they probably shouldn’t be visible to most other employees, even if they’re querying the same table.
Understanding Row-Level Security (RLS)
Most developers learn about database permissions in broad strokes. You grant a user access to a table, and they can read it. Simple. But what happens when different users should only see some of the rows in that table? That’s where row-level security comes in.
Data Lineage Explained
Data lineage is one of those concepts that sounds more complicated than it is. Once you understand the basic idea, it’s actually pretty intuitive. And it solves a problem that anyone who works with data has run into.
What is a Data Lake?
Data lake is one of those terms that gets thrown around a lot in conversations about data strategy, often alongside data warehouses and data marts. But what actually is a data lake, and how does it fit into the picture? Let’s find out.
What Is ELT?
Data doesn’t always arrive clean, structured, or ready to use. And anyone who’s worked with data knows that gap between raw information and actionable insight is where a lot of the real work happens. ELT is a modern approach to bridging that gap.
Unlike its older cousin ETL, ELT flips the script by loading data into its destination first and transforming it there, taking advantage of the processing power that today’s cloud data warehouses have in abundance. The result is a faster, more flexible pipeline that’s become something of a default choice for many data teams building on platforms like Snowflake, BigQuery, or Redshift.
What is ETL?
If you’ve spent any time around data teams or analytics tools, you’ve probably heard the term ETL thrown around. It sounds technical, but the concept is actually pretty straightforward. This article looks at what the term actually means, how it works, and why it matters.
What Is a Time Series Database?
A time series database (TSDB) is a database built specifically to store and query data that’s tied to timestamps. This could include things like sensor readings, stock prices, server metrics, or IoT data. Basically anything where the when is just as important as the what.
Standard relational databases can technically store this kind of data, but they weren’t designed for it. Time series databases are optimized from the ground up for time-ordered data, which means they’re faster, more efficient, and more practical for time-heavy workloads.
What is a Serverless Database?
In the context of cloud databases, a serverless database is one that automatically manages all server resources for you, including scaling up and down based on actual usage, even scaling to zero when not in use. Unlike traditional databases where you provision specific server capacity upfront, serverless databases dynamically allocate resources as needed and charge you only for what you actually consume.
The term “serverless” doesn’t mean there are no servers involved. Servers still exist, but you don’t have to think about them. The cloud provider handles all capacity planning, scaling, and infrastructure management automatically in the background.
What is Database Normal Form?
When you’re designing a database, you need some way to organize your data that makes sense. You could just throw everything into one massive table, but that leads to problems pretty quickly. Duplicate data everywhere, weird update issues, and a general mess that’s hard to maintain.
Normal forms give you a framework for organizing data in a way that avoids these problems. They’re a series of rules or guidelines that help you structure your database tables properly.
This process of organizing data according to normal forms is called normalization, and it’s one of the fundamental concepts in relational database design.