Snowflake’s cloud data platform enables a wide variety of workloads and applications on any cloud, including data warehouses, data lakes, data pipelines, and data exchanges as well as business intelligence, data science, and data analytics applications.
Deliver insights in real time with a high performance, scalable, zero management solution.
Having a Data Lake in the cloud can bypass some of the usual issues faced by conventional Data Lakes
Snowflake has been named a leader in the Gartner Magic Quadrant for Data Management Solutions for Analytics in 2019
Change how you think about Data Warehousing
Built to maintain performance, even at heavy workloads.
The Snowflake data warehouse is not built on an existing database or ‘big data’ software platform such as Hadoop. It uses a new SQL data base engine with architecture specifically designed for the cloud. Snowflake’s unique architecture consists of three different layers that allow for key performance benefits:
Data loaded into Snowflake is reorganised into its internal optimised, compressed, columnar format. This data is then stored in the cloud. Snowflake manages all aspect of the data storage, including organisation, file size, structure, compression, metadata and statistics. The data objects that are stored are not directly visible or accessible by customers and can only be accessed through SQL query operations run through Snowflake.
In the processing layer, Snowflake queries using ‘virtual warehouses’. Each virtual warehouse is an MPP compute cluster consisting of multiple compute nodes. The warehouses are independent compute clusters that do not share compute resources with the other warehouses. This means that each warehouse has no impact on the performance of other virtual warehouses.
The cloud services layer ties together all the different components of Snowflake in order to process user requests. Among the services in this layer include:
Snowflake built security into the design of the product from the beginning. Snowflake achieves best practice for managing an enterprise’s data sources, protecting their data through encrypting everything.This includes encryption keysand the use of Amazon CloudHSM to store and use Snowflake’s master keys.
One of the ways in which Snowflake stress tests its systems is through systematically running ‘penetration tests’ on its own systems. These are controlled attempts to exploit vulnerabilities to determine whether unauthorised access or other malicious activity is possible within the target environment. Snowflake engages globally recognised experts to perform these tests, the methodology of which can be found here.
Snowflake’s focus on security is demonstrated through their stellar portfolio of compliance reports and certifications. To view the reports and certifications demonstrating Snowflake’s commitment to enforcing the highest global security standards, please click here.
Snowflake can support unlimited concurrency with its unique multi-cluster, shared data architecture. This allows multiple compute clusters to operate simultaneously on the same data without degrading performance.
Furthermore, Snowflake can scale automatically with concurrency demands by transparently adding compute resources during peak periods and scaling down when larger loads are not needed.