Database Concepts

What Is ELT?

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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.

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What is ETL?

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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.

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What Is a Time Series Database?

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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.

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What is a Serverless Database?

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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.

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What is Database Normal Form?

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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.

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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 Vector Database?

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A vector database is a specialized database designed to store, index, and query high-dimensional vectors. These are arrays of numbers that represent data in mathematical space. Unlike traditional databases that store text, numbers, or structured data, vector databases work with embeddings, which are numerical representations of complex data like text, images, audio, or video that capture their semantic meaning.

These databases solve the specific problem of finding similar items based on meaning rather than exact matches. Traditional databases excel at finding exact matches or simple comparisons. For example “find all users named Bella” or “find products under $50.” Vector databases excel at similarity searches. These could look something like “find images similar to this one” or “find documents with similar meaning to this query,” even when the exact words or pixels are different.

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What is a Multi-Model Database?

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A multi-model database is a database system that supports multiple data models within a single, integrated backend. Instead of being limited to one way of organizing data (like relational tables, documents, or graphs) a multi-model database lets you store and query different types of data using the most appropriate model for each use case.

Traditional database systems typically specialize in one data model. A relational database like MySQL organizes everything into tables with rows and columns. A document database like MongoDB stores JSON-like documents. A graph database like Neo4j focuses on nodes and relationships. With a multi-model database, you get several of these capabilities in one system, storing relational data, documents, key-value pairs, and graphs side by side.

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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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What is Database as a Service (DBaaS)?

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Database as a Service (DBaaS) is a cloud computing model where a provider hosts and manages your database for you. Instead of installing database software on your own servers, configuring it, and handling ongoing maintenance, you access a fully managed database over the internet. The provider takes care of all the technical heavy lifting while you focus on using the database for your applications.

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