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What Is Snowflake Database - Guide for 2022

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The snowflake database is all the rage these days. But what are they, exactly? And what makes them so special?

In this guide, we'll answer those questions and more. We'll explain what snowflake databases are, how they work, and why you might want to use one in your business. Plus, we'll give you a few tips on how to get started with the snowflake database if you're not quite sure where to start.

1. What is a snowflake database?

Snowflake databases are a more modern type of database architecture. Their rise in popularity reflects the way companies use data today and what platforms they prefer to access their information.

Unlike its predecessor, the star schema, snowflake schemas allow greater flexibility when designing and managing databases. They're also easier to maintain and update than traditional relational databases (RDB). And that's not all: many find them easier to query as well.

2. What is a relational database? 

A relational database is one of the most common ways to store large amounts of data in an organized manner.

It works by associating pieces of related data with each other, forming what's called a table. A table can contain many different types of data (strings, numbers, dates, etc.), which are what make up its rows and columns.

3. What is a star schema? 

The star schema is one of the most common ways to organize databases in relational database management systems (RDBMS). It's an old method that was popular before snowflake databases came into existence. 

The data in the RDBMS is organized into what are called fact tables, which hold values, and what are called dimension tables, which hold metadata used for querying the database. These two types of tables form what's called the star schema model.

4. How does a snowflake database work? 

Snowflake databases help companies solve modern-day problems with traditional relational databases, such as the star schema. They address issues like data fragmentation, maintenance overhead, and computing power. This is what makes them so popular.

A snowflake database organizes the same types of information present in relational databases into dimensional models. The most significant difference between a snowflake model and a star schema is that the dimensions in a snowflake database don't depend on each other for storage or querying purposes. This gives you greater flexibility when thinking about what tables to build and what columns to put within them.

5. What are some advantages of using snowflake schemas over traditional relational databases? 

Many organizations, especially those dealing with large amounts of structured data, opt to use snowflake databases instead of RDBMS. Here are what some of the advantages are:

  • Flexible schema design: Snowflake databases allow you to design schemas that reflect how business users think about data. Not what the database engine needs to store the data effectively. This helps reduce complexity and boost performance.

  • Simplified management: Snowflake schemas make it easier for companies to spot problems arising from changes in their organization's data model. They're also easier to maintain because they don't require complex ETL processes as RDBMSs do. And lastly, there's less computing overhead than other types of database structures because snowflake structures distribute individual tables across multiple servers.

  • Enhanced querying capabilities: Since dimensions in a snowflake database aren't dependent on each other, there's usually little data duplication. This allows companies to query the entire snowflake more efficiently than an RDBMS.

6. What are some alternatives to snowflake databases? 

Snowflake models aren't the only way for businesses to store their data. There are a few alternative ways: what we call normalized and denormalized data storage (the latter is also known as denormalized data). They're helpful in certain situations, but they don't offer the same advantages as snowflakes do over star schemas. This is why those who need those advantages tend to prefer snowflakes over other types of database structures.

  • Normalized Data Storage: This method converts several tables into one. This helps resolve duplication issues because there's only one table. However, it can get really complicated to maintain because of all the necessary joins between tables.

  • De-Normalized Data Storage (Denormalized): This does remove normalization for what would be different tables and put the same information in instead. This, like normalized storage, reduces data redundancy. Still, it also has its own set of problems. Including making queries more complex and costly than if they were done on a snowflake model or other alternative data structure.

7. Why choose a snowflake database for your business?

Snowflake databases are what are known as dimensional models. They're typically used for online analytical processing (OLAP), which means they're great at handling large volumes of data. This makes them perfect for what businesses now need to do, such as analyzing large amounts of structured and unstructured data, pulling insights from machine learning systems, and making real-time decisions based on what the data shows.

What sets snowflakes apart is how they organize information so companies can store what matters most while also allowing them to extend this storage across multiple servers. Less computing overhead than RDBMSs, they help improve performance by efficiently filtering out unnecessary information that isn't relevant to business users' particular tasks.

8. Tips on getting started with the snowflake database 

What should your first steps be if you're just starting out with snowflake databases?
We've got a few tips for you: what better way to get started than by building what's known as a dimensional model. This is what data architects and business intelligence (BI) analysts use to map how data is connected and where it can be found within the snowflake. Data modeling is an integral part of this process. So you need to know what kind of design best suits your company's needs - whether you want to go hybrid or fully dimensional.

As far as networking goes, what you'll want to do next is install grids on each server that your snowflake will run on. These are what bring all the servers together to have what they need to work with the snowflake model. Once these grids are installed, what you should do next is import what's known as a knowledge module. These will ensure that all of your servers communicate effectively and can handle what they need to, so you get excellent performance from what might be a large amount of data.

Learning the snowflake database can be a daunting task, but it can be easy to get up to speed with the right resources. 

If you need help setting up or managing your snowflake database, don't hesitate to contact us. Our experts are more than happy to help you get started and make sure your database is running smoothly.

Thank you for reading,
Radek from Duomly

Top comments (1)

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Rasmus Schultz

Confusing article. You didn't even include a link to the product you're describing.

You make it sound as though snowflake databases are a new concept that you can compare with relational databases?

From what I could find, Snowflake is the brand-name of a proprietary data warehousing platform. (which appears to be built, in part, upon relational databases, as well as other types.)

As far as my understanding, data warehousing platforms are not an "alternative" to databases. They're tools for data analysts to dump large amounts of data into for analysis, mining and processing. Typically, this data originates from relational or other databases, from various systems around the business.

To my knowledge, no one is throwing away their databases and moving to data warehouses as first-party storage? 🤨