Snowflake vs Star Schema: Beginners Guide for Snowflake Architecture – Starweaver
Snowflake vs Star Schema Beginners Guide for Snowflake Architecture

Snowflake vs Star Schema Beginners Guide for Snowflake Architecture

When comparing Star Schemas and Snowflakes Schemas data warehouses, it is important to note that the two are quite similar in the fact tables, both have the same dimensions; the main difference is that the dimension tables of a snowflake schema are normalized (SnowFlaking) while in Star Schemas it is not.

So to understand this better, let's first define what a data warehouse is: A data warehouse, which is also called an enterprise data warehouse, can be described as a system that is used for data analysis and reporting, and it is an important component of Business intelligence.

Before comparing these two, let's first begin with an in-depth discussion of what these two data houses are and briefly look at their features.

What is a snowflake?

A snowflake can be described as a cloud-based Data Warehouse solution offered as a "Software-as-a-service" (SaaS) and has the full support of ANSI SQL. Snowflake Schemas dimension tables are normalized; they are designed with a unique SnowFlake architecture, which enables its users to create tables and input querying data with minimal administrations.

The snowflake architecture comprises sophisticated hybrid shared-nothing and traditional shared disk architecture, which provides the best of each. The SnowFlake architecture is a hybrid model that has three different levels that include:

  • Storage Layer- SnowFlake organizes the data into very many micro partitions that are optimized and compressed internally. The data is stored in a columnar format in the cloud storage and works as a shared disc model.
  • Compute Layer-The Snowflake architecture makes use of Virtual warehouses to run queries. The query processing layer is separated from the disc storage by SnowFlake.
  • Cloud services Layer-It takes care of other crucial processes of the SnowFlake Architecture, such as security, authentication, and Metadata management of query optimizer and the loaded data.

Features of Snowflake Data Warehouse

  • Connectivity:  SnowFlake has the ability to support an extensive set of users' drivers and connectors like Spark connector, Python connectors, NET driver, Node.js driver, and others.
  • Data protection and security: The Snowflake data warehouse provides upgraded authentication through Federal authentication, Multi-Factor authentication, and Single Sign-on. Additionally, the communication between server and client is highly protected by TSL.
  • Standard and Extended SQL Support: Supports most DML, DDL, and other commands of SQL.
  • Data Sharing: SnowFlake allows secure sharing of data with other SnowFlake accounts. It also easily provides data for those accounts to use and consume data provided by other accounts.
  • Database Failover and Replication: Snowflake supports syncing and replicating databases across different SnowFlakes accounts located in different areas. Also, provide configuration to database failover to Snowflakes accounts, thus promoting recovery and business continuity.
  • Tools and Interfaces: Provide Web-based GUI for general management, querying data, and resource monitoring &system usage.
  • Data Export and Import

SnowFlake supports bulk unloading and loading of data into and out of the tables. Some of the functions include loading delimited data files and compressing data from S3 data files.

Challenges of snowflakes

SnowFlake is a very sophisticated data warehouse that requires knowledge to handle, and these are the challenges that can be faced include:

  • It is slow at processing cube data due to a lack of transnational assurance during normalization.
  • Quite complex data schemas: Snowflakes Schemas has multiple complex levels while it normalizes a star schema's characteristics, thus complicated joins.

What is Star Schema?

A star Schema is made up of dimension tables, single or multiple central fact tables, and the connections that join the fact tables to the dimension tables. It is the simplest but very fundamental among the data mart schema.

The Star Schema works by separating business processes data into facts responsible for holding the quantitative business data, measurable business data, and dimensions, which are the descriptive attributes related to the fact data. Here are some of the components of the Star Schema.

Features of Star Schema Data Warehouse

  • Feeding cubes-Star Schemas is used by every OLAP systems to make OLAP cubes efficiently.
  • It is business reporting logic is simple.
  • Has the capacity of filtering data-Star Schema filters data from normalized data and then offers data warehousing needs.
  • Provides fast aggregations and calculation

Challenges of Star Schemas

  • Due to star Schema's simple dimension structure, they do not function well for "many-many data relationship."
  • Star schemas do not have sufficient capability of handling complex and diverse queries. They are mostly built for specific analytical needs.
  • Reduced data integrity: Star Schemas have denormalized data structure, and the integrity of data is not enforced sufficiently.

Snowflake vs. Star Schema

From the above explanations, we have seen Star Schema and Snowflake Schema's descriptions, which are ways of organizing data marts using relational databases. When comparing these two data warehouses, we will analyze them based on the following characteristics.

1. Normalization

SnowFlakes Schemas have normalized dimension tables while the Star schema dimension tables are not normalized.

2. Query Complexity

The SnowFlake Schema query is complex because its dimensional tables are normalized. In contrast, the Star Schema query is simpler because it only connects the fact table with the dimension table leading to faster and simpler SQL queries.

3. Storage space

SnowFlake uses little space to store dimensional tables due to normalization and thus less redundant files. At the same time, Star Schema has lots of redundant data and uses a lot of storage space.

4. Data integrity

Star Schema contains denormalized data subject to integrity problems that might make it difficult to maintain and very complicated modify. SnowFlakes are normalized, and thus the rule is the data is less redundant, which means that it is easy to maintain and modify in the future.

5. Data Complexity

SnowFlake is made up of complex designs, while the Star schema is made of simpler designs.

6. SnowFlakes contain fact tables, surrounded by dimensional tables and sub dimensional tables, while Star Schemas contains fact tables surrounded by dimensional tables.

7. Snowflakes are suitable for data warehouses, while star schemas are good for DataMart that have simple dimensional relationships.

After looking at SnowFlake and Star Schema in detail, it is clear that they are quite different in features, design, functionality, and comparisons even though both are relational models used to store data. So, while these two may be suitable for different functions, then the question remains still which one is recommendable.

Which one to choose between SnowFlake vs. Star Schema?

SnowFlake has an advantage when it comes to data storage; due to the normalization of data, it uses less space during storage. Therefore, the SnowFlake Schema is suitable for use when implementing a data warehouse because it saves on space.

On the other hand, Star Schema is recommended for DataMart; the DataMart are subsets taken from the central data warehouse. While using the Star Schema saving storage space is not a high priority. The advantage of this setting is that the analysis of data is simplified, and thus the future actions of its future users are made easy. It will be easy for a future business user to understand the database and formulate queries, thus making life easy for business users.

 

 

This article has focused on Star Schema, SnowFlake Schema and a few details about SnowFlake architecture and the comparisons between the SnowFlake and Star Schema. A business should choose a relational database that fulfils their data needs sufficiently.

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