SQL

What is Relationship Cardinality?

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When you’re designing a relational database, you need to define how tables relate to each other. You do this by creating relationships between the tables. Relationship cardinality describes how many records in one table can be associated with records in another table. It’s a fundamental concept that affects how you structure your database and write your queries.

Understanding cardinality helps you avoid design mistakes that lead to data integrity issues, performance problems, or queries that return unexpected results.

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What is a Snowflake Schema?

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In relational databases, a snowflake schema is a variation of the star schema where dimension tables are normalized into multiple related tables. Instead of keeping all dimensional attributes in a single wide table, you break them down into a hierarchy of tables that branch out like (you guessed it) a snowflake.

It’s basically what happens when you apply database normalization principles to a star schema’s dimension tables while keeping the central fact table intact.

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Getting Started with the Netflix Sample Database in SQL

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If you’re learning SQL or looking for a real-world dataset to sharpen your database skills, the Netflix sample database could be an option. This free, open-source database is based on publicly available information from Netflix’s Engagement Report and Global Top 10 weekly lists, making it both relevant and engaging for practice purposes.

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What is a Slowly Changing Dimension?

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In data warehousing, a slowly changing dimension (SCD) is a dimension table where the attributes change over time, but not very frequently. The term “slowly changing” refers to the fact that these changes happen occasionally (perhaps days, weeks, or months apart) rather than constantly like transactional data.

The challenge is figuring out how to handle these changes while maintaining accurate historical analysis. When a customer moves to a new state or a product gets recategorized, you need a strategy that preserves the integrity of your historical data.

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What is a Columnstore Index?

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A columnstore index is a type of database index that stores data by column rather than by row. Traditional indexes (and tables) store data row-by-row, where all the values for a single record are kept together. Columnstore indexes flip this around, storing all values from a single column together instead.

This might seem like a small difference, but it fundamentally changes how the database reads and processes data. Columnstore indexes are designed for analytical queries that scan large amounts of data, performing aggregations, calculations, and filtering across millions or billions of rows.

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What is a Dimension Table?

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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.

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What is a Fact Table?

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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.

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What is a Star Schema?

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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.

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What is Denormalization?

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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.

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What is a Query Plan Cache?

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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.

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