Snowflake Data Cloud: Features, Uses, Benefits, and How to Get Started

Key takeaways

Snowflake is a cloud-based storage company that allows you to connect applications, documents, and stored data and share and integrate data securely.

• The key features of Snowflake are its data warehousing capabilities, the ability to connect multiple cloud providers, and its secure data sharing. 

• The Snowflake data architecture includes database storage, a parallel processing layer, and a coordinated cloud service. 

• You can use Snowflake for data analytics, application development, and artificial intelligence and machine learning development. 

Learn about the features, uses, and benefits of employing the Snowflake data cloud in your business data management process. Then, explore the IBM Data Warehouse Engineer Professional Certificate, where you'll have the opportunity to learn about SQL queries, create data pipelines, and populate data warehouses. 

What is Snowflake cloud data platform?

The Snowflake data cloud is a popular example of a cloud storage system offering these advantages and more. With data management being a growing focus for businesses, companies are looking for database systems to manage the increasing amount of data they need to store and work with. Cloud solutions are becoming popular because they offer scalability, reliability, and flexibility. 

Key features of the Snowflake data cloud

Snowflake is a cloud database with innovative technology and functions, making it a standout option for data management. Essential features include scalable data warehousing, multi-cloud capabilities, and sharing data securely.

Data warehousing

Data warehouses serve as places to store data from multiple sources for analysis and business intelligence (BI) functions. Traditionally, data warehouses were purpose-built hardware, which has become impractical given the volume of data that businesses now store. 

Snowflake is different because it is a single AI platform offering cloud storage with automatic performance improvements, advanced analytics, and interactive reporting. This allows for more storage, scalability, and advanced data analytics options while saving on running costs.

Multi-cloud capabilities

An exciting feature Snowflake offers is that it allows businesses to build data warehouses across multiple cloud providers. You can use it on Azure, AWS, and Google Cloud, enabling you to use the functions and benefits of all of them.

Secure data sharing

With the Snowflake data cloud, you can share objects in your database with other Snowflake users through secure data-sharing functions that allow you to share read-only material for increased security. This might include databases, tables, charts, secure views, and secure user-defined functions (UDFs).

Read more: Data Lake vs. Data Warehouse: What’s the Difference?

Architecture of Snowflake

Snowflake’s architecture offers cohesion, scalability, and high performance. Its features make it stand out from traditional data warehouse systems.

Snowflake’s unique cloud architecture separates the data storage layer, compute (querying) layer, and cloud service layer, meaning each layer can operate independently, making the service flexible and responsive. In addition to improving performance, this model is highly cost-effective.

Database storage

Any data you add to your Snowflake cloud storage undergoes reorganization and compression for maximum optimization. You won’t be able to see this data, but you can access it through querying. 

Processing layer

Any querying you do takes place in the processing layer using virtual warehouses. These virtual warehouses are independent massively parallel processing (MPP) computer clusters, so Snowflake does not share resources between them, ensuring optimal performance. 

Cloud service

The cloud service layer coordinates services across the Snowflake platform, from login to queries to user inquiries. Through this layer, Snowflake is self-managing and relies on automation technology to ensure efficient and cost-effective service.

Is Snowflake a database or ETL?

Snowflake is a cloud-native data warehouse that lets you use extract, transform, and load tools to manage and analyze data. It helps you integrate data from multiple sources into a single platform, including various cloud services. 

Snowflake data cloud use cases

Snowflake is a flexible tool that can be used for many different purposes across industries. Whether you work in data analytics, data science, or application development, to name a few, Snowflake is a scalable resource that supports complex workloads.

Data analytics

Snowflake stores and consolidates data from various sources, providing the basis for data analysis. Businesses and organizations in all industries can use Snowflake when analyzing and reporting data at scale, utilizing the built-in BI tools that the platform integrates with. These include:

• Tableau

• Qlik Talend

• Sigma

• Alteryx One

Application development 

You can use Snowflake as the software development backend for app development. Snowflake is an excellent choice for this, given its ability to handle large volumes of data and secure data-sharing functions.

Artificial intelligence and machine learning

As a centralized data storage platform for analytics and data storage, Snowflake can support generative AI and machine learning (ML) applications, enabling model testing and training through ML framework integration.

Industries and organizations

Businesses and organizations across numerous industries see the benefits of using the Snowflake data cloud to support data management and analysis. For example, manufacturing, health care, and financial services use Snowflake in the following ways:

• Manufacturing: Machine efficiency, inventory levels, and production output are of high importance in manufacturing. Snowflake can support you to achieve supply chain optimization, predict machine maintenance before it is necessary, and implement product quality control. 

• Finance: Snowflake can support your data-driven decision-making with functions for detecting fraud, personalizing the customer experience, and consolidating data to improve risk management and compliance.

• Health care: Use Snowflake to support trend research for discovering behaviors, environmental conditions, and other factors for improving patient experiences.

Benefits of using Snowflake

The Snowflake data cloud is a popular option for businesses and organizations in a range of fields, as it comes with a number of benefits, especially over traditional database management options. Benefits include: 

• Scalability and performance: Snowflake’s multi-layer design allows for auto-scaling concurrent workloads on an enormous scale, increasing both performance and scalability.

• Data security: Snowflake is known for its high levels of security, which you can set to different regions to comply with specific guidelines. 

• Shareability: Snowflake allows for secure real-time data sharing across organizations globally, including tables, secure views, and UDFs.

Snowflake limitations

While Snowflake comes with many benefits, as with any technology solution, it comes with some limitations: 

• Cloud only: Cloud technology is a pro for many, but if you’re looking for on-site deployment, Snowflake is cloud-only. 

• Small community: Snowflake has a relatively small community of users compared to some larger cloud providers, which means you might find less community support available.

How to start with the Snowflake data cloud

If the Snowflake data cloud sounds like the solution for you, make a start on the platform with a free 30-day trial. You’ll find a range of support documentation on the Snowflake website, including sections on getting started, a library of user guides, and numerous tutorials. 

Explore our free resources for data engineering

Explore career paths, assess your skills, and connect with resume guidance while browsing our Career Resources Hub. Or if you want to learn more about data engineering, check out these free resources:

• Watch on YouTube: Career Spotlight: Data Engineer 

• Hear from an expert: 6 Questions with an IBM Data Scientist and AI Engineer 

• Study terms: SQL Glossary: SQL Terms and Definitions

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