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Pros and Cons of Snowflake Data Warehouse | Saras Analytics
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Pros and Cons of Snowflake Data Warehouse

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Table of Contents

Snowflake is one of the leading cloud data warehouse technologies today. Snowflake is a preferred platform for data warehousing, data engineering, data application development, data lakes, and secure data sharing real-time data consumption. Developed in 2012, Snowflake has introduced many out-of-the-box features like on-the-fly scalable computing, data cloning, and third-party tool support to support ever-growing enterprise needs. The Snowflake data warehouse’s advantages are essential for anyone considering investing in a cloud data warehouse.

Pros of using Snowflake Data Warehouse

Server Capacity

Server capacity ceases to be an issue with Snowflake. Traditional data warehouses require the purchase, regular maintenance, and expensive hardware upgrades. Snowflake data warehouse turns on in a few minutes and automatically scales up or down without requiring manual intervention.

Storage Capacity

Snowflake runs on cloud-based infrastructure, which is inexpensive and infinitely scalable. In addition, Snowflake organizes data into micro partitions, optimizes it, and automatically compresses it. As a result, the physical space occupied is less than the combined raw data sizes.

Performance Tuning

Snowflake completely manages performance tuning on its own. The database is user-friendly, and if users follow the published best practices around organizing data, they have a highly responsive, optimally performing system in their midst. In addition, Snowflake does not require a DBA to be on staff full-time.


Security features built into the Snowflake data warehouse give users powerful, easily configurable capabilities. Snowflake boasts a slew of security features including, but not limited to, IP whitelisting, two-factor authentication, federated authentication with SSO, AES 256 encryption, encryption of data-in-transit, and at rest.

Automated Replication

Automated Replication ensures that disaster recovery is no longer an issue. Snowflake automatically replicates data across the availability zones or availability domains within a region and across regions. In addition, the snowflake design enables it to endure the loss of up to two data centers.


Concurrency, performance, and scalability are no longer an issue. Clusters scale up or down automatically to support fluctuating demands of workloads or to support a sudden increase in the number of users.

Multi-Cloud deployment

The multi-Cloud deployment choice for Snowflake makes it the only fully managed data warehouse available in multiple clouds while retaining the same user experience. As a result, the snowflake data warehouse meets its users where they are comfortable and, by doing so, reduces the need to move data back and forth from their cloud environment to Snowflake over the internet. Snowflake is available on Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

Sharing and Collaboration

Sharing and collaboration features in Snowflake offer data owners capabilities to share their data with partners or other consumers without needing to create a new copy of the data and subjecting themselves to increased risk exposure. The data consumer only pays for the data processing as no data movement is involved, and their storage is not utilized. Avoid the hassles involved in FTP or email by using native sharing features Snowflake provides that you can invoke via native SQL.

Third-party Data Integration

The Snowflake Data Marketplace lets you connect your third-party application and data services with Snowflake to extend your workflows. You can integrate Snowflake with other data services using a data pipeline tool like Daton. Daton’s pre-built Snowflake connector makes it easy for your team to create extended data pipelines to automate various workflows throughout the enterprise.

Snowflake Schemas

Snowflake offers a Snowflake schema that is an extension of the star schema design methodology. Both star schema and Snowflake combined offer multidimensional schema that is easy to navigate. As analysts call for vast databases running off a multidimensional schema, the Snowflake schema provides better-optimized MOLAP modeling tools, complex structures, and better storage savings.

Cons of using Snowflake Data Warehouse

Bulk Load Issues

While Snowflake offers exciting benefits, some users have mentioned that they faced issues while processing data in bulk, allegedly. Snowpipe, Snowflake’s data loader might not be the preferred choice for most users.

No Data Constraints

Snowflake is infinitely scalable, and users must pay for what they need. However, there are no data constraints or set limits to both computing and storage. In case the organizations are not mindful, they can easily exceed the use of Snowflake services only to realize the problem during the billing process.


Snowflake offers many advantages over traditional on-premises-based solutions like Oracle, IBM, Teradata, and others by being innovative, nimble, and cost-effective. For organizations that are open to adopting cloud technologies, Snowflake offers a compelling data warehousing solution that you can trust with your data.

Want to learn more about Snowflake architecture? Please read our detailed article on Snowflake Architecture. you can also read about Google Big Query and Amazon Redshift before deciding which data warehouse to select.

Saras Analytics is an official Snowflake ETL Partner. Our product, Daton, seamlessly replicates data from various data sources into Snowflake without you having to write a single line of code. With 100+ connectors to different data sources, Daton is the fastest and easiest way to replicate data to Snowflake.

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