- What Is Snowflake
- Key Areas of Differentiation From Other Cloud Data Warehouses
- Key Use Cases for Snowflake
What Is Snowflake?
The Snowflake data warehouse is a cloud-based tool that supplies companies with flexible and scalable storage while simultaneously hosting solutions for BI.
Key Areas of Differentiation From Other Cloud Data Warehouses
- The Snowflake data warehouse is built entirely on the cloud and employs a subscription-based model with storage and compute operating independently. With elastic storage, Snowflake’s system automatically applies hot/cold storage techniques to minimize costs and scalable compute eliminates the traditional caps on concurrency that other warehouse options present.
- Snowflake stores both structured and semi-structured data, converting it into a usable format that is SQL-compatible.
- Pricing: The company’s services are provided on-demand, empowering users to select the amount of computing and storage they need before paying for it based on usage or on a monthly fixed-rate model. Snowflake’s flexible payment structure also means that compute (which is charged per second, with a 60-second minimum) and storage can be activated for specific instances and projects without incurring long-term costs.
- The Snowflake data warehouse is cloud-agonistic, creating the option for a customer to be multi-cloud. Snowflake is currently available on Microsoft Azure, Google Cloud, and Amazon Web Services.
- Snowflake’s flexible system of cloud servers decentralizes data and allows each stakeholder or group within an organization to access specific data sets without requiring complicated data transfers.
- Users can quickly query data, without affecting the underlying data set, and receive closer to real-time data.
Key Use Cases for Snowflake
Snowflake’s scalable architecture and lightweight querying make it an ideal tool for companies that are starting their exploration of the data-driven model.
The platform’s usability factor and flexibility make it a valuable tool for companies that are seeking to test new systems, create their own analytical models, or try out different approaches to fulfill their data analysis and BI needs.
- Ad hoc analysis. Users can create their own queries in Snowflake. Snowflake’s use of virtual warehousing allows stakeholders to build their own databases with live and cached data, making for faster queries and more near real-time BI insights.
- Embedding analytics. Snowflake delivers the elasticity, scalability, and flexibility that embedded analytics applications require.
Users can also adopt a hybrid model, with a live connection to Snowflake and then Sisense’s caching layer for targeted historical analysis.
With this approach, Sisense-native data models can be used for exploring and analyzing historical data, a live Snowflake connection can provide real-time intelligence, and everything can be displayed in one dashboard.
For additional information, visit our Sisense + Snowflake resource page. For additional information about Snowflake specifically, visit www.Snowflake.com to learn more about their product offerings, read about their new Data Exchange offering, and review actual customer use cases.