What Are Advantages and Disadvantages of Using Snowflake?

What Are Advantages and Disadvantages of Using Snowflake?

Snowflake is a cloud data warehouse designed for high-performance business analytics. Its architecture decouples storage and compute functions, allowing customers to use and pay for these separately.

It is a good choice for companies looking to store large volumes of data at low costs. Its scalable architecture and sharing capabilities make it an ideal data management solution.


Snowflake is a pay-per-use storage solution that has an on-demand, pay-as-you-go pricing model. This allows users to monitor their usage and costs in real-time, allowing them to make smart decisions about how they allocate their resources.

The main cost element of Snowflake is compute power. This is used to power queries, analysis, ETL processes, and other data warehouse functions. This is a very large component of your overall Snowflake costs, so it’s important to be aware of how this costs are calculated and what impact it has on your monthly cost.

Another major factor that affects your cost is the size of the warehouse you choose to run. The larger the warehouse, the higher your compute costs will be.

With the right optimization strategies, you can reduce these costs by up to 30% while also maximizing query performance. Fortunately, Snowflake has some native features that can help you do this, including micro-partitioning, automatic clustering, and table clustering.

These features work slightly differently but all have the same goal — ensuring that tables are optimized to the way that your data is being queried. This helps to improve query times and reduce credit consumption, as well.

It’s also important to monitor your data usage at the account, warehouse, database, and table level, to identify and remove any unused or unnecessary rows that are contributing to your overall cost. This is a great way to prevent unwanted queries and optimize your data cloud costs.

If you’re considering Snowflake for your next data cloud project, the best way to get started is by signing up for a free trial. This will allow you to test the platform and see if it’s the right fit for your organization.


Snowflake is a cloud-based data warehouse solution that can be run on AWS, Google Cloud, and Microsoft Azure. It offers high performance and scalability for your data warehousing and analytics needs.

Snowflake uses a hybrid architecture that combines the benefits of both shared-disk and shared-nothing massively parallel processing (MPP) architectures. This unique design allows you to scale up and down independently to meet your performance and cost requirements as they change over time.

This is a key advantage over Redshift, which bundles compute, storage, and cloud services together making it difficult to optimize or scale each function independently. In addition, Snowflake has a built-in security feature that ensures data is protected against malicious attacks.

Moreover, it offers multiple virtual warehouses that can scale vertically and horizontally to accommodate your query workloads. This helps you to load and retrieve your data faster than using a single data warehouse alone.

In addition, Snowflake can handle data from both structured and unstructured sources, including JSON, XML, Avro, and Parquet files. This makes it a good choice for teams that need to store unstructured data like videos, audio, and customer event logs.

However, Snowflake can only load data with a maximum file size of 8 MB for character strings and 16 MB for binary files. The company recommends breaking up larger files into smaller ones and compressing them before loading them to reduce data volume.

Snowflake also supports a data type called VARIANT which allows users to store unstructured JSON, Avro, and XML records natively in the data warehouse. This option gives teams the flexibility they need to analyze data in a variety of ways, from streaming to ad hoc queries.


Snowflake is a cloud data warehouse (DWH) that stores analytical data for data scientists, analysts, and machine learning engineers to query. It enables data teams to quickly and easily deploy, scale, and manage their analytics workloads.

It also offers a pay-as-you-go pricing model that is highly transparent and accountable. This helps ensure you have the best possible performance without sacrificing your budget.

Another great thing about Snowflake is that it automatically caches data in both its virtual warehouse and resulting cache, which can dramatically improve query performance. In addition, Snowflake resizes its micro-partition storage to make sure that only relevant information is retrieved by users, which results in reduced query latency.

The optimizer also takes advantage of partition metadata to decide which data files it needs to read before executing a query, known as a full table scan. It can also perform a process called pruning, which skips unnecessary data to reduce scan time and improve query performance.

This optimizer is a big part of why Snowflake can handle complex queries and scale to millions of rows of data. Its top-k ordered selection optimization reduces latency by a few times, while other enhancements like column-level partition pruning and row-level primary key recursion can significantly speed up query execution time.

With so many features and a high-performing cloud DWH, Snowflake is a good choice for organizations that have an existing analytical database or are looking to migrate from an on-premise one. However, it is important to consider the needs of your organization before making a decision on which data warehouse solution to use.


Snowflake is a cloud-based data warehouse that is ideal for organizations looking to store large amounts of data. It can also help with analyzing, processing, and cleaning information from different sources. It can be used by businesses of all sizes.

Snowflake’s security measures are designed to protect PII, personal health information (PHI), and other sensitive data from unauthorized access or misuse. They include encryption at rest and in transit, data governance, and a centralized identity management system.

Data governance refers to the roles that a user has in a Snowflake account and the privileges that each role can grant to other users. This helps ensure that all users have the proper access to the data they need to perform their job functions.

OvalEdge supports two-way syncing of user roles and permissions, which means that customers can control the visibility and permissions to data at the source and in Snowflake. This allows them to use data masking and external tokenization to restrict access to certain columns.

As a result, users have the ability to determine which data is protected by a higher level of security and which data can be shared with other employees or partners. This allows businesses to share information across their entire enterprise, making it easy for employees and business leaders to make decisions based on data.

Snowflake offers various authentication methods, including username/password, OAuth, keypair, and external browser authentication. It also provides fail-safe storage, which offers a 7-day recovery period in case of data loss due to a system failure.


There are many integrations available with Snowflake allowing users to access and work with various types of data. These include ETL tools, analytics platforms, and other tools to support the data pipeline.

For example, Viva Goals offers an integration that allows you to link the success of your OKRs directly to data in a Snowflake warehouse. This helps you build a data-driven strategy and ensure that your team can reach their goals by leveraging accurate data in an efficient manner.

Another integration, Snowflake Policy Push, enables Immuta to apply row access and column masking policies to Snowflake tables. These policies are pushed into the database within Snowflake and kept up-to-date by Immuta through webhooks.

Streams, another integration, helps you track changes to your data in real-time. These can be changes to your data files, changes to tables, or other changes. You can even use these streams to automate ad hoc queries.

As a result, it can be easy to implement this integration. However, you should make sure that your Immuta policies, user attributes, and data sources are properly setup.

Additionally, you should ensure that your Snowflake account is set up to accept the S3 bucket's security and access management policies. This is because AWS requires these permissions before it can execute instructions to read and write to the bucket.

As a result, it's important to check that these requirements are met before you integrate your data with Snowflake. This will help you avoid any issues later on down the line. Also, make sure that you use the right type of encryption for the data. In this way, you can protect it from theft and fraud.