In the past, you had to buy an expensive, custom-built piece of hardware and run it in your data center to set up a data warehouse. Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts while allowing safe data exchange across the business.
What is Snowflake Data Cloud
Snowflake is built on Amazon Web Services, Microsoft Azure, and Google’s cloud services. Since there is no hardware or software to choose, install, configure, or manage, it is suitable for businesses that don’t want to spend time and money setting up, maintaining, and supporting their servers. Data may also be transferred into Snowflake using an ETL service like Stitch.
Snowflake stands out because of its unique architecture and capacity for data sharing. Snowflake architecture lets storage and computation grow separately. Customers can use and pay for storage and analysis differently. Also, the sharing feature makes it easy for organizations to send controlled and secure data quickly and in real-time.
Snowflake Architecture
Remember when buying a cable television subscription meant getting both the infrastructure and the content? Today, those things are separate (but linked), and customers have more choice over what they use and how they pay for it.
The Snowflake Architecture allows similar flexibility with large amounts of data. Snowflake decouples the store and computes tasks, so organizations with large storage requirements but less need for CPU cycles, or vice versa, do not have to pay for an integrated package that forces them to pay for both. Users may scale up or down based on their needs and only pay for the resources they use. Storage is priced per terabyte per month, while computation is billed per second.
Layers of Snowflake Architecture
The Snowflake architecture consists of three layers, and each is independently scalable.
- Services
- Storage
- Compute
1. Cloud Services
The cloud services layer coordinates the whole system. It uses ANSI SQL and eliminates the need to manage and tune the data warehouse manually. Services in this layer include:
- Access control
- Metadata management
- Authentication
- Query parsing and optimization
- Infrastructure management
2. Data Storage
All the data brought into Snowflake, both structured and unstructured, is stored in the database storage layer. Snowflake automatically handles all parts of data storage, including organization, file size, structure, compression, metadata, and analytics. This layer of storage may operate without any assistance from the computer system.
3. Computation
The compute layer consists of virtual warehouses carrying the data processing functions necessary for queries. Each virtual warehouse (or cluster) may access all data in the storage layer and then operate independently, avoiding the need for the warehouses to share or compete for computing resources. This makes it possible for automated, non-disruptive scaling, which means that computing resources can be added. At the same time, queries run without moving or rebalancing data in the storage layer.
Advantages of Snowflake
Snowflake is made for the cloud and solves many of the problems that hardware-based data warehouses have, such as limited scalability, problems with data transformation, and delays or failures caused by many queries. Here are five ways Snowflake may help your company.
1. Performance
The cloud’s elastic nature helps you scale up your virtual warehouse to take advantage of more computational resources if you need to load data quickly or execute many queries. After that, you may reduce the size of the virtual warehouse and pay for the time spent.
2. Snowflake Database
You can put structured and semi-structured data into the cloud database for analysis without turning it into a relational schema. Snowflake enhances how data is stored and queried automatically.
3. Concurrency
Snowflake’s multi-cluster design solves concurrency problems because queries from one virtual warehouse don’t affect queries from another, and each virtual warehouse can grow or shrink as needed. Data analysts and scientists don’t have to wait for other loading and processing tasks to finish before getting the required data.
4. Data Sharing
Snowflake’s design allows its users to share data. It also enables organizations to share data with any cloud server, whether or not they are Snowflake members, through reader accounts that can be established straight from the user interface. A service provider may create and manage a client’s Snowflake account with this function.
5. Security of Snowflake
Snowflake is made to keep running even if a part of the network goes down, with as minor damage as possible to clients. It does so by being distributed across availability zones of the platform on which it operates (either AWS or Azure). In addition to SOC 2 Type II certification, security features include network-wide encryption and support for Protected Health Information (PHI) data for HIPAA clients.
Conclusion
Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts while allowing secure data sharing across the organization. You can look at structured and semi-structured data in the cloud database without converting it to a relational schema. Snowflake facilitates users’ only paying for the resources they use.
References
Frank Slootman Introduced the Snowflake Data Cloud
