1. What is Snowflake Data Cloud?

Snowflake is built on the cloud infrastructures of Amazon Web Services, Microsoft Azure, and Google. Because there is no hardware or software to choose, install, configure, or manage, it is ideal for organisations that do not want to devote resources to the setup, maintenance, and support of in-house servers. Data can also be easily moved into Snowflake using an ETL solution such as Stitch.

Snowflake’s architecture and data sharing capabilities, on the other hand, set it apart. Customers can use and pay for storage and computation separately thanks to the Snowflake architecture’s ability to scale storage and compute independently. Furthermore, the sharing functionality enables organisations to quickly share governed and secure data in real time.

2. The true differentiator is snowflake architecture.

Remember when buying a cable television service meant getting both the infrastructure and the content? Today, those things are distinct (but intertwined), and people have more control over what they use and how they pay for it.

Snowflake’s architecture allows for similar flexibility when dealing with big data. Snowflake decouples storage and compute functions, which means that organisations with high storage requirements but little need for CPU cycles, or vice versa, they are not required to pay for a combined bundle that requires them to pay for both. Users can scale up or down as needed, paying only for the resources they use. Storage is charged in terabytes per month, while computation is charged per second.

In fact, the Snowflake architecture is made up of three layers, each of which can be scaled independently: storage, compute, and services.

Database storage:

The database storage layer contains all of the data that has been loaded into Snowflake, including structured and semistructured data. Snowflake automatically manages all aspects of data storage, including file organisation, file size, structure, compression, metadata, and statistics. The compute resources have no effect on this storage layer.

Compute layer:

The compute layer consists of virtual warehouses that perform data processing tasks required for queries. Each virtual warehouse (or cluster) can access all of the data in the storage layer and then work independently, preventing the warehouses from sharing or competing for compute resources. This enables non disruptive, automatic scaling, which means that compute resources can scale while queries are running without the need to redistribute or rebalance data in the storage layer.

Cloud services:

The cloud services layer coordinates the entire system and uses ANSI SQL. It does away with the need for manual data warehouse management and tuning. This layer’s services include:

• Authentication
• Infrastructure management
• Metadata management
• Query parsing and optimization
• Access control

3. 5 Snowflake Advantages for Your Company

Snowflake is designed specifically for the cloud, and it addresses many of the issues that plagued older hardware-based data warehouses, such as limited scalability, data transformation issues, and delays or failures due to high query volumes. Here are five ways Snowflake can help your company.

Speed and performance:

Because the cloud is elastic, you can scale up your virtual warehouse to take advantage of extra compute resources if you need to load data faster or run a high volume of queries. After that, you can reduce the size of the virtual warehouse and only pay for the time you used.

Structured and semistructured data storage and support:

For analysis, you can combine structured and semistructured data and load it into the cloud database without first converting or transforming it into a fixed relational schema. Snowflake optimises the storage and querying of data automatically.

Accessibility and concurrency:

When too many queries compete for resources in a traditional data warehouse with a large number of users or use cases, you may encounter concurrency issues (such as delays or failures).

Snowflake’s unique multicluster architecture addresses concurrency issues: queries from one virtual warehouse never affect queries from another, and each virtual warehouse can scale up or down as needed. Data analysts and data scientists can get what they need when they need it, rather than having to wait for other loading and processing tasks to finish.

Data sharing that is seamless:

Snowflake’s architecture allows Snowflake users to share data. It also enables organisations to share data with any data consumer, whether or not they are a Snowflake customer, via reader accounts that can be created directly from the user interface. This feature enables the provider to set up and manage a Snowflake account for a customer.

Security and accessibility:

Snowflake is distributed across the availability zones of the platform on which it runs — either AWS or Azure — and is designed to run continuously while tolerating component and network failures with minimal impact on customers. It is SOC 2 Type II certified, and additional levels of security are available, such as PHI data support for HIPAA customers and encryption across all network communications.

4. Link your ecosystem

If you have a diverse data ecosystem or an IoT solutions database, you’ll need a cloud-based data platform with nearly infinite expansion, scalability, and usability. You’ll also need a data integration solution that is cloud-optimized. The use of Stitch to extract and load data simplifies migration, and users can run transformations on data stored in Snowflake.

Stitch, as a Snowflake Partner, makes it simple to set up Snowflake. New users can move an unlimited amount of data from more than 90 data sources, including popular platforms like Google Analytics and Google Ads, Shopify, Salesforce, and Stripe, during a free 14-day trial.