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The data age is here, and data is growing exponentially. This also means the organizations need more extensive data storage and more processing power to yield meaningful business insights. The organizations take advantage of the scalability, affordability, elasticity, better ROI, and concurrency offered by the modern cloud data warehouse solutions to solve the big data problem.  

While there are many cloud data warehouses, Snowflake has its advantages to make it your data platform. Snowflake is recognized as a leader in Gartner Magic Quadrant for data management solutions for analytics.  Let us see the features that Snowflake has that make it the best fit for the data platform. 

Centralized Data Storage 

An ideal data warehouse should adapt to the exponential increase in data and maintain it in central storage. It should also be able to retrieve it faster when required for reporting 

Snowflake has a centralized repository where data is persisted and is accessible by all the compute nodes. The storage layer can be scaled independently and is both inexpensive and infinite in size. Data is stored in an internal, optimized compressed columnar format called micro-partitions. The metadata of these micro partitions is stored, which makes data retrieval much faster. It also provides built-in redundancy for disaster recovery.  

Why Snowflake as your data platform
Figure 1: Storage, compute and cloud service layers in Snowflake architecture


The processing layer should be automatically scale-out to avoid resource contention. It should also scale in when the workload decreases. Adding additional servers for data-intensive processing should also be more straightforward.

Snowflake’s unique architecture separates compute and storage. Multiple independent compute clusters called virtual warehouses can be created to run your workloads. The number of servers that these warehouses have can be changed at any point in time. These workloads run independently of each other, accessing the same data source layer, ensuring the ELT processes run entirely isolated from end-user queries providing zero resource content.

You can automatically scale up or down on the fly by enabling multi-cluster warehouses without disruption or data redistribution. Snowflake allows creating additional compute clusters to support as many workloads or users as you need without moving or copying data. With Snowflake, you can deliver the perfect number of resources at the exact time they are required.

Data Integration 

Data warehouse’s success lies in the diversity of source systems that it can connect to and the ease of the connectivity. 

Snowflake works with a wide array of tools and technologies, enabling you to access Snowflake through an extensive network of connectors, drivers, programming languages, and utilities. Snowflake provides a comprehensive list of native connectors for Python, Spark, Kafka, and drivers for Node.js, Go, .NET, JDBC, and ODBC. It also provides a command-line client – SnowSQL. In addition to these, there are certified partners like Azure Data Factory, dbt, Fivetran, Google Cloud Dataflow, Google Cloud Data Fusion, HVR, Fivetran, which could be used for data integration with Snowflake.

Why Snowflake as your data platform
Figure 2: Data ingestion and ELT in Snowflake

Snowpipe for continuous data integration 

An ideal data warehouse would provide native facilities to ingest data as soon as it is generated and support stream data in near real-time. 

Snowpipe can seamlessly load continuously generated data into Snowflake. It’s an automated service that utilizes a REST API to asynchronously listen for new data as it arrives in a staging environment, and load it into Snowflake as it arrives, whenever it arrives. 

Snowpipe doesn’t require any manual effort to configure or run and is a serverless process that utilizes servers separate from the customer environment to ensure workload isolation. You have to pay for the server time used, keeping ingestion costs predictable and affordable. Snowpipe also help us with near-real-time data reporting.

Snowflake also brings the benefits of using streaming data using Snowpipe and Kafka streaming. 

Why Snowflake as your data platform
Figure 3: Continuous data loading using Snowpipe in Snowflake

Handling semi-structured data 

Semi-structured data has become the de facto data transfer method of web-based traffic and IoT devices. This means the data warehouse should have the innate ability to process semi-structured data efficiently and report without a need to flatten it. 

Snowflake automatically optimizes the storage and processing of structured and semi-structured data in a single system. You can directly load semi-structured data such as JSON, Parquet, ORC, Avro, and XML without transformation or a mandatory fixed schema using a unique datatype called a VARIANT. You can also report straight out of this data using the SQL FLATTEN command.

Near-Zero Management 

The administrative overhead on managing the system should be as minimal as possible. There should also be a mechanism to track usage and monitor performance. Also, the maintenance should not interfere with any ongoing workloads, degrade performance, or result in service unavailability.

Since Snowflake is a SaaS offering, system upgrades, clustering, network security, backup and recovery are taken care of for you. Spinning up servers is also made easy with virtual warehouses that can be created using a simple UI. It can scale out automatically and supports auto-resume and auto-suspend features. ACCOUNT_USAGE schema details all of the logs required for the DBA to track usage and monitor system performance. Administrators can also set up resource monitors to restrict the utilization of resources.

Data Recovery 

A modern data warehouse should manage itself to ensure the system’s durability, resiliency, and availability.  

With Snowflake, it is possible to recreate a consistent view of any database, schema, or table at any point in time, up to 90 days in the past, using its time travel feature. Time travel is automatically enabled in Snowflake with a 1-day data retention period by default and can be configured for more extended data retention periods of up to 90 days. It is also possible to restore a dropped table or schema, or database using the UNDROP command.

Data Security and Data Governance 

A modern data warehouse must also support multilevel, role-based access control (RBAC). This ensures users only have access to data they are permitted to see. Encrypting the data, which means applying an encryption algorithm to translate the clear text into ciphertext, is another required security feature. 

Snowflake provides role-based access control for granular control with flexible user management. You will be able to create secure views with cell-level security and mask your PII data. It also provides row-level and column-level security. Audit trails are maintained for every action a user performs. Snowflake provides an array of security features, including IP whitelisting, multi-factor authentication, and AES 256 end-to-end solid encryption.


With modern data warehousing, your service fee should cover everything for a small fraction of the cost of a conventional warehousing solution.  

Snowflake provides a simple data warehouse pricing model with independent costs for storage and compute resources, with all charges being usage-based. Automatic data compression in Snowflake saves your storage cost significantly by providing a 3:1 compression ratio. The virtual data warehouses, which are the compute engines, are available in 8 different sizes. Each has compute credit based on size, allowing you to pick the appropriate one for query execution. Snowflake does not charge for idle compute time when the warehouses are suspended. Also, results sets are cached for reuse without requiring additional compute costs.

Skills and Tools 

A modern data warehouse should be architected with leading technology but built on inclusive and established standards (such as SQL) compatible with skills and tools commonly available in the industry.

Snowflake is a complete SQL database. It is built to use standard SQL, so it does not require data users to learn new or specialized tools and skills to gain quick, easy access to the data they need. Since Snowflake is also ACID compliant, routine data updates and deletions are easy to perform, simplifying the analytics pipeline.

Data Sharing 

A modern data platform should enable your data to be quickly and securely shared internally and externally, ensuring data governance.

Snowflake data sharing allows you to share data with customers who don’t have a Snowflake account by creating a reader account with read-only access to your live data. Data sharing between Snowflake accounts have the same data governance and ease of use as the reader accounts but with the benefit of users joining their data to create enriched data.  Also, with Snowflake’s Data Market Place, get live, ready-to-query data with no delays from 3rd party data providers.

Why Snowflake as your data platform
Figure 4: Snowflake data sharing

We have discussed just a preview of Snowflake’s unique features that depict Snowflake as a better fit for your modern data warehouse solution. 

To learn more about Snowflake, check out these blogs. Reach out to us here  today if you are interested in evaluating if Snowflake is suitable for you. 

Learn more about Visual BI’s Snowflake offerings here

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