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Pros and Cons of Snowflake Data Warehouse
Business Transformation

Pros and Cons of Snowflake Data Warehouse

Snowflake data warehouse is especially useful for industries that are looking for a platform offering solutions that conventional data platforms

Snowflake data warehouse is especially useful for industries that are looking for a platform offering solutions that conventional data platforms cannot. Their superior convenience and capability have made the need for organizations to set up their own data warehouses unnecessary. The snowflake schema in data warehouse enables solutions for healthcare IT, media and technology, financial, insurance, and all other major industries.

Pros of Snowflake Data Warehouse 

1. Storage Capacity 

Snowflake can be run on Microsoft’s cloud-based blob storage, Azure, which is highly affordable, scalable, and highly user-friendly. It also has a high capacity for storage that makes it ideal for use by organizations that handle large amounts of data.

2. Multi-Cloud 

While Microsoft Azure is the preference of many, Snowflake can also be hosted on other cloud platforms including Amazon Web Services and Google Cloud Platform. These three options are best suited to providing the kind of cloud infrastructure that can support the use of this software-as-a-service (SaaS).

3. Server Capacity 

Legacy data warehouses required a massive investment in servers and other equipment. Snowflake offers far greater capacity without the need to update machinery. Everything is cloud-based with the SaaS capable of being deployed on a minute scale that can later be scaled up or down according to need.

4. Security 

Given that data can often be sensitive there must be a reassurance that it will be protected. Snowflake background provides IP whitelisting that limits access to data to only trusted users. This is coupled with two-factor authentication, AES 256 encryption, and federated authentication with SSO. Furthermore, there is an encryption of data-in-transit and at rest to ensure no tampering.

5. Performance Tuning 

Snowflake databases are user-friendly, allowing users to organize their data as they wish. This SaaS is designed to be highly responsive and perform optimally on its own without the need for constant surveillance from a specialist.

6. Disaster Recovery 

Without having physical access to the servers that store this data, some organizations may be concerned about what happens if there is a failure. Thankfully, Snowflake has contingencies for this, ensuring multiple data centers where data is replicated and easily accessible in the unlikely event that disaster recovery is needed.

7. Performance 

It is not uncommon for organizations to have periods where there are suddenly more users on the network or there is a heightened workload. Snowflake clusters can cope with these fluctuations as they are scalable up and down on demand, ensuring whatever number of additional users can be comfortably accommodated.

8. Snowflake and Star Schema

Snowflake Schema in data warehouse is extension of star schema date warehouse design methodology. There are many benefits of star schema and snowflake schema in data warehouse design.

Analytics calls for the use of large databases that run off multidimensional schema. Snowflake schema is a type of multidimensional schema whose data warehouse is arranged in such a way as to resemble a snowflake design. It is an upgrade to the more basic star schema. The star schema data warehouse resembles a star shape as its structure features a single fact table with several offshoot dimension tables.

The star schema is a simple database design that is easy to navigate and allows for fast cube processing. Snowflake schema is however better optimized for some MOLAP modeling tools and has a structure that though more complex provides better storage savings.

Cons of Snowflake Data Warehouse

Snowflake data warehouse review indicate that they do have a few downsides that though may not make them as comprehensive a solution, still does not discount them from being a top data warehouse system.

1. No support for unstructured data at the moment

Snowflake currently only caters to structured and semi-structured data. This may however change in the future to include unstructured data.

2. Only bulk data load

When migrating data from data files to Snowflake files there is much support and guidance on bulk data loading. If in need of continuous loading, users are limited to just Snowpipe.

3. No data constraints

While Snowflake is indeed scalable, allowing users to pay for just what they need, it does not set limits. This applies to both storage and computing. For some organizations it can be easy to exceed the use of these services, only realizing the problem during billing.

Why Organizations Are Moving to Snowflake Data Warehouse

Modern organizations are shifting rapidly from traditional in-house to modern cloud-based data platforms to digitally dominate their competition and ultimately achieve a competitive edge.

The need to make this change has been driven by:

Security and Data Protection

Organizations are now forced to store important and sensitive data on computers. Hackers have become more advanced in their cyberattacks. Leaving many businesses vulnerable as there is a distinct shortage of affordable expertise to manage these risks. Third-party solutions like Snowflake come with security features that help remove much of this concern.

Data Modernization

Migrating data from in-house servers to cloud databases provides businesses with access to modern computing capabilities they never had before. Snowflake allows organizations to make better use of their data in performing analysis and developing insights that better guide their operations and decisions.

Operational Cost and Performance

For businesses to have the computing power and storage capability that Snowflake provides accessible on-premises would call for massive investment in IT equipment and expertise. With Snowflake, organizations can access these services whenever and to whatever extent they need without worrying about the cost. Many are not even concerned about cost as the benefits of cloud computing become more apparent.

There is an obvious need for businesses in various industries to upgrade their data platforms to better leverage new and upcoming applications and tools. they gain better access to this and modern analytics that can help take their businesses to the next level.

However, planning and executing such a major change is not easy. It requires the support and guidance of professionals with the necessary expertise in cloud based solutions . To ensure success on this cloud migration journey, organizations should:

  • Define what goals they want to achieve with this change
  • Determine what gaps in talent and expertise exist so it can be provided for by the provider
  • Select software tools that will be of beneficial use in the long term, safeguard data, and easy to use.


Like any other cloud data platform, right implementation is the key to success. Some technology consulting firms and freelance cloud consultants are providing the following deployment options.

  1. Three Separate accounts for DEV, TEST and PROD environment.
  2. One account for PROD and one for both DEV and TEST
  3. Single account for DEV, TEST and PROD

There is no single deployment solution and it varies with the varying client and customer needs. Designing a right architecture according to the requirements is critical for technology integration specialists. To cater this cloud consulting firms are now building a data modelling approach tailored to organization needs and requirements.

MicroAgility follows a custom snowflake integration procedures to keep the client current ETL structure workflows in place. Our teams excel in taking clients existing business logic and building it in Snowflake’s SQL variation for both performance and maintainability. To streamline your data pipelines our cloud experts revamp all layers of your data architecture. MicroAgility helps you to achieve measurable results by helping you to integrate snowflake with on premise databases and analytical tools to provide a single source for historical data, predictive results and training data for machine learning models.

Posted by
Sajid Khan

Sajid Khan is the President at MicroAgility and has over three decades of management and consulting experience. He leads the efforts in many projects including operational improvements, cost reduction, and managing growth. Sajid strives to help others succeed and to create opportunities that are sustainable and uplifting for humanity — always guided by the virtues of hard work, quality, and kindness