what is – Page 7

What is Vertical Scaling?

January 4, 2026 by Ian

Vertical scaling is the practice of increasing the capacity of a single server by adding more resources to it. For example more CPU power, RAM, storage, or faster hardware. Instead of adding more machines to handle increased load, you make your existing machine more powerful.

In database contexts, vertical scaling means upgrading your database server to more powerful hardware so it can handle more queries, store more data, and process transactions faster. It’s the most straightforward way to improve database performance.

What is Horizontal Scaling?

January 8, 2026January 3, 2026 by Ian

Horizontal scaling is the practice of adding more servers or machines to your system to handle increased load, rather than making individual servers more powerful. Instead of upgrading one server with more CPU and RAM, you add additional servers and distribute the work across all of them.

What is Database Sharding?

January 8, 2026January 1, 2026 by Ian

Database sharding is a technique for splitting a large database into smaller, more manageable pieces called shards. Each shard contains a subset of the total data and operates as an independent database. Together, all the shards hold the complete dataset, but queries are distributed across them to improve performance and scalability.

What is a Cloud Database?

March 2, 2026December 30, 2025 by Ian

A cloud database is a database that runs on cloud computing platforms rather than on local servers or personal computers. Instead of installing and managing database software on your own hardware, you access the database over the internet through a cloud provider like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform.

Basic Database Terms for Beginners

December 29, 2025 by Ian

If you’re just getting into databases, the terminology can sometimes feel overwhelming. Between acronyms like SQL and CRUD, technical jargon like normalization and schema, and concepts that sound similar but mean different things, it’s easy to get lost before you even start.

Let’s break down the essential concepts you need to know to actually understand what people are talking about.

What is a Dimension Table?

January 21, 2026December 28, 2025 by Ian

In data warehousing and analytics systems, dimension tables provide context for the measurements stored in fact tables. While fact tables contain the numbers you want to analyze, dimension tables tell you what those numbers mean. This could be things like who was involved, what product it was, when it happened, where it occurred, etc.

Basically, dimension tables are the “descriptive” side of dimensional modeling, containing the attributes and characteristics that let you filter, group, and label your data in meaningful ways.

What is a Fact Table?

January 21, 2026December 27, 2025 by Ian

In data warehousing and analytics databases, a fact table is where you store the actual measurements and metrics of your business. It’s the table that contains the numbers you want to analyze. This could include things like sales amounts, quantities, counts, durations, or any other quantifiable data points.

Fact tables are the foundation of dimensional modeling, the approach most commonly used for organizing data in data warehouses and business intelligence systems.

What is a Star Schema?

January 29, 2026December 23, 2025 by Ian

If you’ve ever worked with data warehouses or business intelligence systems, you’ve probably encountered star schemas. Perhaps even without realizing it. Star schemas are one of the most common ways to organize data for analytics and reporting.

Star schemas look exactly like their name suggests. They consist of a central table surrounded by related tables, forming a star shape.

Star schemas are designed specifically for querying and analysis rather than transactional operations. They make it easy to slice and dice data in ways that business users actually care about.

What is Denormalization?

January 8, 2026December 22, 2025 by Ian

If you’ve spent any time working with relational databases, you’re probably well aware of the concept of normalization. This is the process of organizing data in a way that reduces redundancy and maintains consistency. It’s basically SQL Database Design 101. And for good reason.

But sometimes the “right” way to design a database isn’t necessarily the most practical way to run it. Sometimes we need to tweak the thing until we get it performing just right. And sometimes this means deviating from the norm and using a different approach. Denormalization is an example of this.

What is a Query Plan Cache?

December 21, 2025 by Ian

A query plan cache is an area of a database management system‘s memory that stores compiled execution plans for queries. When you execute a query, the database’s optimizer analyzes the query and creates an execution plan, which is basically a set of instructions for how to retrieve and process the requested data.

But compiling this plan requires computational resources, so database systems cache it in memory for reuse rather than recompiling the same plan repeatedly.

This caching mechanism is a fundamental performance optimization found in virtually all modern relational database systems. By reusing compiled plans, databases avoid the overhead of repeatedly analyzing the same queries, resulting in faster query execution and reduced CPU consumption.