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.
List of Query Store Configuration Options in SQL Server
SQL Server’s Query Store provides a handy way to track query performance over time, making it easier to troubleshoot issues and optimize workloads. To get the most out of it, it helps to understand the various configuration options that control how it collects, stores, and manages data.
What is a Cloud Database?
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
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?
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?
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.
How to Disable Query Store in SQL Server
Query Store captures query execution history, plans, and runtime statistics over time. If you need to stop this data collection (whether to reduce overhead, troubleshoot issues, or prepare for maintenance) you can disable Query Store on a per-database basis.
Building a Survey Results Dashboard in SQL
Survey data can be messy. You’ve got responses scattered across dozens or hundreds of rows, multiple choice answers, rating scales, and so on. And the challenge of turning all that raw data into something stakeholders can actually understand. A well-designed survey dashboard can transform those individual responses into a grid that shows patterns instantly. For example, which questions are getting strong agreement, where opinions diverge, and what trends emerge across different respondent groups.
Calculating Dynamic Date Offsets with Expressions in SQL Server’s DATEADD() Function
One of DATEADD()‘s less obvious features in SQL Server is its ability to accept expressions as the interval parameter rather than just simple numeric values. You can perform calculations, use arithmetic operations, or reference multiple columns right inside the function call. This gives you a more flexible way to calculate dates when the offset itself needs to be computed based on your data.
So, instead of adding or subtracting a fixed number of days, months, or years, you compute that number on the fly using whatever logic makes sense for your situation. Let’s look at an example that demonstrates this concept.
What is a Star Schema?
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.