Snowflake works as a logical data warehouse that is an upgrade on more traditional warehouse options. Offered as a Software-as-a-Service (SaaS), it utilizes a unique SQL database engine that provides cloud-based data storage. Thanks to its innovative engineering, Snowflake can offer its clients greater value thanks to its added functions.

Besides storage, the Snowflake database also provides the necessary infrastructure, security, and access for faster and reliable communication and data sharing. Their cloud-based solutions allow clients to enjoy maximum value for money as they only pay for the capacity and performance they use. This flexible pricing is a boon to customers looking to make use of cost-effective data warehousing services.

How Snowflake Technology Works?

As a SaaS, Snowflake technology is shared with clients entirely over the internet. Its uses a public cloud based infrastructure to link with clients. Snowflake architecture is separated into three main categories. There are cloud services, data storage, and query processing.

It features a hybrid system of shared-nothing and traditional shared-disk database structures. Here both semi-structured and structured data is accessed.  A central data repository is used for dynamic data, while massively parallel processing (MPP) compute clusters handle queries. Data can be accessed through all computer nodes in the data warehouse, with each node also capable of storing parts of entire data sets locally.

The shared disk architecture provides a single storage layer that all nodes can access. Here multiple cluster nodes can interact with the database to identify data and process it. Shared-nothing architecture is made up of distributed cluster nodes with their own storage and processors. This allows the data to be separated and stored in these clusters for better privacy.

 

Snowflake Database

 

Snowflake TechnologyClients that use Snowflake technology do not need to invest in any new hardware or software. There is no configuring, installation or management required as everything is accessible online. The Snowflake team also takes care of any security, maintenance, management, and updating of their databases. It is a true SaaS offering that enables users to enjoy cost-saving benefits and flexibility.

What is Snowflake Architecture?

As mentioned, the Snowflake architecture is split into three main parts.

1.    Cloud Services

This component oversees and coordinates the services offered by Snowflake. It is through these services that clients can execute whatever actions on the data stored. From login to processing requests, this layer facilitates various activities. In a Snowflake drawing of its data warehouse structure, this component would be at the very top. Services catered to under this layer include:

  • Authentication
  • Infrastructure management
  • Metadata management
  • SQL compilation
  • Query processing and optimization
  • Access control
  • Encryption

2.    Data Storage

Through the use of Amazon’s S3 service in its filing systems, Snowflake can micro partition its database in a column-like format. This reorganization of data in cloud storage allows for internal compression, encryption, and distribution. It delivers optimal performance for its cloud-based clients’ thanks in part to the shared-disk model that offers easy data management. It also ensures users do not have to be concerned about data distribution with multiple nodes in a shared-nothing structure.

Use of S3 is preferred as it is not affected by geographical location and offers easy data access and durability. SQL queries running on Snowflake are what provide clients with the ability to view and access data objects in storage.

3.    Query Processing

Queries are executed within this layer using virtual warehouses. Snowflake’s ability to create these warehouses allows users to simultaneously conduct queries while loading data. Virtual warehouses are compute clusters in EC2 that operate in the background. They handle large masses of data. As the warehouses access and retrieve data, they provide users with a result set. The scalability of these warehouses helps to keep costs low and allow for better management on computing.

snowflake drawing

Snowflake Architecture

Does Snowflake Have an Edge Over Other Cloud Data Warehouses?

Snowflake offers a superior experience as compared to traditional cloud data warehouses. The performance and speed of a service that is offered as SaaS are impressive. Without the need to add new hardware or software to existing systems, users can immediately get to utilizing Snowflake. The only barrier would be a slow internet connection. 

Being built on standard SQL language and configured for cloud-based use ensures that Snowflake is easily understood by any software engineer. This means teams can get up to speed and effectively utilizing the services without the need for additional training. Signing up on the Snowflake careers page to join the team is not necessary to use their technology.  Its multi-cluster architecture of virtual warehouses also aids in ably handling multiple queries concurrently without affecting loading and processing times.

Being cloud-based also ensures that Snowflake is easily accessible. Whether you are using a desktop computer or smartphone, all that is needed is an internet connection. This excellent availability means that work can be done on the go without the need of much local storage.

Data sharing is enabled across Snowflake accounts. The data providers can upload data and share with other data consumers. Though data shared is kept read-only. This ensures security and data integrity is preserved. It also keeps the process of data sharing quick and simple. This allows providers to share their data with many consumers while consumers can access shared data from many providers.

The scalability of the storage and processing components of the Snowflake template is also a big benefit to users who get to enjoy cost-effective services. Being able to store semi-structured and structured data without needing to configure it helps to ensure easier storage and querying.

How Snowflake Database Secures Data?

It can be easy for data to become corrupted or stolen. Keeping data safe is an integral aspect of any data-driven business. Snowflake has taken this responsibility seriously given the massive amounts of data it handles on behalf of clients. The high level of data stored by this provider makes it a key target for malicious users.

Data encryption is primarily used to ensure security. When relaying data over the network, there is always end-to-end encryption in use. This means it is rewritten in code whose secret key is only accessible to designated users. Encryption is done automatically whenever data is transferred to ensure whoever else happens to access it over the internet will not be able to decipher the information.

It is a vital security feature that ensures Snowflake client data is secure in the cloud and all other parts of the architecture. To accomplish this, Snowflake ensures the following:

  • Two-part authentication
  • Encrypted data import and export
  • Secure data storage
  • Role-based access control for data

Data encryption is undertaken at all stages including when being transferred and written to either a disk or to shared storage. Snowflake also makes effort to adhere to government and industry standards when it comes to data security. They have achieved the Federal Risk and Authorization Management Program (FedRAMP) Authorization to Operate at a moderate level. Snowflake also uses advanced cloud security technology to ensure a reliable and safe service for users.

How MicroAgility Helps to Successfully Implement and Execute Snowflake Technology?

MicroAgility helps businesses to develop an effective and customized organizational data warehouse through Snowflake. Here is how we do it.

1. Rebuilding

While it is possible to leverage existing systems to achieve your goals, taking advantage of new technologies does help keep you competitive and derive new benefits. At MicroAgility, we help businesses adapt to the new age by identifying suitable tech that will improve the performance of their processes and applications. Some of the key functions we evaluate before selection and adoption include data loading processes, source codes, policies, and user management.

2. Data Modeling

MicroAgility helps businesses to evaluate their data usage patterns. Understanding how your clients and systems make use of data can be useful in determining how best to utilize Snowflake. It helps in determining your organizational needs and the resources you will need to meet your data consumers’ demands. Making this assessment allows for better scaling and customization as you leverage data assets in Snowflake.

3. Integration

To identify the best pattern, MicroAgility evaluates your data loading use cases. Many businesses make use of static and persistent data that needs to be analyzed and processed. This data can stream in batches or in real-time. Whatever the terms, MicroAgility can help to discern your data sourcing needs and delivery SLAs. Once we determine your suitable ingestion pattern we can then choose a matching integration tool that is supported by Snowflake. Finding the right tool will require further assessment of your usage needs, processing capability and data volumes.

4. Management

To ensure you are getting the best value for money when adopting Snowflake, MicroAgility recommends the following:

  • Upgraded security practices: Establish strong security practices to safeguard your organization.
  • Access management: Identify the various levels of user needs and access to applications. Develop a hierarchy that will allocate the required access rights customized to specific users.
  • Resource monitoring: Establish control and monitoring protocols over storage and computing needs. Managing the scalability factor of Snowflake will help in meeting budgetary goals.