What is Snowflake Data Warehouse? - A Detailed 2024 Guide

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What is Snowflake Data Warehouse? - A Detailed 2024 Guide

What is Snowflake
Snowflake Data Warehouse

It's no secret that data is a powerful tool for building a successful business in today's world. When used correctly, data can give businesses unprecedented insights into their operations and potential opportunities for improvement. 

With petabytes of data getting generated on the daily, it can get challenges for businesses to store and process data properly. That's where a platform like Snowflake can help

So why should you consider using a cloud-based Snowflake data warehouse to store and manage your company's data? 

With its unbeatable scalability, quick deployment times, cost efficiency, and effortless integration with other applications, Snowflake provides an outstanding solution for enterprise businesses looking to make the most of their valuable information assets. 

In this blog, we go over how it works and its benefits – so read on to discover everything there is to know about trusted cloud-data platforms like Snowflake!

What is Snowflake Data Warehouse?

Snowflake is an advanced cloud-native data warehouse platform that enables organizations to store and analyze vast amounts of structured and unstructured data from multiple sources, such as relational databases, transactional systems, streaming data, NoSQL databases google cloud,, and more. Snowflake's breakthrough technology allows companies to maximize the value of their data by providing a secure, affordable, and easy-to-use platform that can scale to meet the needs of any organization.

Snowflake helps companies accelerate analytics and business intelligence by allowing users to quickly and easily access and analyze data in real-time, no matter where it is stored. By providing an integrated data platform, for accessing, transforming, and leveraging all of an organization's data, Snowflake helps organizations gain valuable insights quickly, making smarter decisions faster. With the help of Snowflake's powerful analytics capabilities, companies can achieve faster time-to-value and gain a competitive edge. With Snowflake, businesses can now unify all of their data under one roof and unlock the power of their data to make smarter decisions that drive business results.

How does Snowflake Data Warehouse Work?

  • Snowflake's data warehouse is based on a new SQL database engine with a unique architecture designed for the cloud. Snowflake's multi-cluster shared data architecture provides separate compute, storage, and cloud services that can scale independently and elastically, enabling us to offer customers unprecedented performance, concurrency, and simplicity.
  • The key elements that enable Snowflake to provide unparalleled performance are a patented columnar storage technology, vectorized query execution, and massively parallel processing. Columnar storage allows Snowflake to read and write data much faster than traditional row-oriented databases. Vectorized query execution enables Snowflake to process multiple rows of data with one instruction for greater efficiency when performing calculations on large datasets.
  • Lastly, massively parallel processing (MPP) enables Snowflake to distribute query execution across multiple nodes for faster execution times.
  • Snowflake also allows customers to manage their data warehouse via its Data Sharing capabilities easily. This feature allows users to share securely and query data stored in the cloud with other authorized accounts from any location.
  • Additionally, Snowflake automatically optimizes queries to ensure they run as efficiently as possible, helping users save time and money.

With Snowflake's innovative data architecture, organizations can achieve faster query response times at a lower cost than traditional data warehouse solutions. Its scalability and flexibility make it the perfect solution for any business that needs to store and analyze large amounts of data.

Snowflake Architecture

Snowflake Architecture


Snowflake's entire data and warehouse architecture is based on a multi-cluster, shared-nothing system. It uses separate computing, storage, and cloud services that can scale independently and elastically.

Here are the key components of the Snowflake data warehouse architecture:

1. Cloud Service Layer: The cloud service layer is responsible for managing user requests and coordinating data access. It provides an API to query the warehouse, manage security, access external data sources, and more. With this layer, users can easily monitor usage and performance of virtual warehouse.

2. Compute Services Layer:

The compute services compute layer also optimizes and executes queries. It uses vectorized query execution and massively parallel processing (MPP) to process large amounts of data quickly and efficiently.

3. Cloud Storage Layer:

The cloud storage layer stores data in an optimized, columnar format. It is highly durable and secure, allowing users to store large amounts of data without sacrificing performance. Using Snowflake's unique storage metadata management architecture, users can easily and securely access their data.

Snowflake's innovative data warehouse architecture makes it easier for businesses to take advantage of the cloud and its resources. Its scalability and flexibility make it the perfect solution for storing, accessing, transforming, and leveraging data. From real-time analytics to cost savings and improved performance, Snowflake is the ideal choice for businesses that need a powerful data warehouse in the cloud.

What Makes Snowflake Better than Other Data Warehouses?

Snowflake is a modern cloud-based data warehouse that offers several advantages over traditional on-premise solutions.

Here are some benefits that make Snowflake stand out from other competitors in the market -

Amazon Redshift Vs Snowflake

Snowflake has a more intuitive and user-friendly UI/UX, enhanced data sharing capabilities, real-time query performance, advanced analytics tools, and automatic optimization of queries. It is also much easier to scale up or down with Snowflake than with traditional on-premise solutions. Whereas Redshift is more difficult to scale and requires manual intervention.

Snowflake also offers lower costs than Redshift and better performance for data warehouse workloads. With Snowflake, users can get fast query performance without paying for idle compute resources.

Google BigQuery Vs Snowflake

Snowflake has a much more robust feature set than BigQuery. It offers better performance, real-time query execution, enhanced data sharing capabilities, and more. Snowflake also enables customers to store and analyze both structured and semi-structured data in the same warehouse.

In comparison, BigQuery is limited when it comes to features and flexibility compared to Snowflake. It is not optimized for real-time data workloads, and it is difficult to scale up or down.

Furthermore, BigQuery is much more expensive than Snowflake when it comes to cost.

Azure SQL Data Warehouse Vs Snowflake

Azure Data Warehouse (ASDW) lacks the feature set of Snowflake. It is not optimized for real-time workloads, and it has no data sharing capabilities. Furthermore, ASDW is more expensive than Snowflake since users are charged for both computing and storage resources separately.

In comparison to ASDW, Snowflake offers enhanced performance for real-time data workloads, along with better scalability and cost savings. It is also much easier to manage, as users can easily scale up or down without manual intervention. ASDW requires manual scaling, which takes longer and is more expensive.

Overall, Snowflake is the better choice for businesses that need a modern cloud-based data warehouse. It offers enhanced performance, scalability and cost savings compared to traditional on-premise solutions and other cloud alternatives. With the ability to store, access and transform data quickly and securely, Snowflake has become an essential part of many organizations' data-driven operations.


Snowflake is priced according to usage and can be an attractive option for large organizations with big enough data management requirements. The company offers several pricing plans, including two broad pricing models - Pay-As-You-Go and Pre-purchased, monthly subscriptions, and unlimited storage options.

  • Pay-As-You-Go:

This pricing model is based on usage and users are only charged for the resources consumed. With pay-as-you-go, customers have the flexibility to scale up or down as needed. Businesses only pay for what they use.

  • Pre-Purchased: 

Pre-purchased plans provide cost savings for customers who commit to a certain amount of resources over a period of time. Customers can purchase credits upfront and redeem them as needed, or use a monthly subscription plan that offers additional benefits. This pricing model is suitable for large organizations with long-term data warehousing needs. It requires customers to purchase a set amount of storage and compute resources upfront. This option offers the most cost savings but requires an up-front commitment from customers.

The two Popular pricing models are -

1.Snowflake Standard Edition

Snowflake Standard Edition is suitable for customers with small data warehouse workloads and offers the most cost savings. It provides basic features like query acceleration, elastic scaling, secure sharing and more. This edition does not have advanced features such as time travel or zero-copy cloning.

2.Snowflake Enterprise Sensitive Data Edition

Snowflake Enterprise Sensitive Data Edition is an advanced version of Snowflake that is optimized for customers who need to store and process highly sensitive data. It provides features like secure sharing, cell-level security, multiple virtual warehouses, and more. This edition offers the most comprehensive set of features but requires additional licensing costs. 

Snowflake offers an attractive pricing model for businesses of all sizes. Its pay-as-you-go and pre-purchased plans provide the flexibility to scale up or down as needed while providing cost savings over traditional on-premise solutions.

ETL and Data Transfer in Snowflake Data Warehouse

Snowflake offers a wide range of options for data transfer and ETL. It supports loading data from files, databases, streaming sources, cloud storage services and other sources. Snowflake also includes a proprietary ETL tool called Snowpipe that enables users to load large amounts of structured or semi-structured data quickly and securely. 

ETL stands for extracting data from a source, transforming that data into a format that is compatible with the target table, and then loading that formatted data into the desired target table. Frequently, the source and target are two distinct entities or database architectures. 

Examples include moving data from a Postgres database onto a Snowflake Data Warehouse, loading data lake a flat file into an Oracle table, exporting CRM data into an Amazon Redshift table, etc.

With the help of an ODBC or JDBC connection, Snowflake may connect concurrent users to a wide range of data integrators.

Snowflake offers two main ways to move data:

Bulk Loading:

This is the simplest and fastest way to move large amounts of data into Snowflake from a variety of sources, including files stored in cloud-based object storage such as Amazon S3 and Azure Blob Storage. It uses the COPY command which helps to load massive amounts of data into the database quickly.

Continuous Loading:

This is an automated data loading process that works in real-time. It loads and refreshes data from sources such as streaming data services, databases and flat files. Snowpipe is used to load semi-structured and structured data into Snowflake with minimal effort and setup time.

Both of these methods are designed to help customers quickly and effectively transfer data into and out of their Snowflake Data Warehouse. The platform's intelligent query optimizer and cloud-native architecture make it a cost-effective choice for businesses of all sizes. With its flexible pricing model, the Snowflake data platform makes it easy to scale storage and compute resources as needed without requiring an upfront commitment.

Snowflake's Use Cases

Snowflake is an ideal solution for businesses of all sizes looking to manage and store their data more efficiently. It's scalable cloud-based architecture makes it easy to deploy and manage. Here are some of the most common use cases for Snowflake:

1. Ad hoc Analysis:

Snowflake is a great tool for companies who need to perform ad hoc analysis, as it provides fast access to up-to-date data. It's ability to query semi-structured and structured data makes it ideal for complex queries. With the ability to spin up compute clusters full of servers on demand and scale compute resources as needed, Snowflake enables customers to quickly analyze large volumes of data.

2. Embedded Analytics:

Snowflake is well-suited for customers looking to embed analytics in their applications. Through a secure API-connected gateway, Snowflake enables customers to access data stored in the warehouse and query it using SQL. It's intuitive, the cloud-based architecture makes it easy to query data on demand with minimal latency.

Key features of Snowflake Data Warehouse

Snowflake provides a secure and powerful cloud data warehouse platform that enables customers to store, manage and query their valuable data with ease. Here are some of the key features of the Snowflake Data Warehouse:

1. Scalability: Snowflake is designed for scalability and flexibility. It allows customers to increase or decrease computing power as needed, meaning they can scale their resources up and down without any downtime.

2. High Availability: Snowflake's high-availability architecture guarantees that customers' data is always available and protected from outages and unplanned restarts. Snowflake also leverages advanced encryption to ensure data is secure and protected.

3. Query Optimization: Snowflake's intelligent query optimizer enables customers to quickly access and analyze their data with minimal latency, regardless of the complexity of the query.

4. Cost-effectiveness: With its pay-as-you-go pricing model, Snowflake makes it easy for customers to manage their costs with minimal upfront commitment. Customers can also take advantage of Snowflake's low storage fees and cloud-based architecture to reduce total cost of ownership.

5. Security: Snowflake's multi-layered security protocols ensure that customers' data is always protected. From strong authentication protocols to granular access control, Snowflake helps protect customers' data from unauthorized access and malicious attacks.

With its powerful features and efficient architecture, Snowflake makes it easy for businesses of all sizes to store, manage and analyze their data in an efficient and cost-effective manner.

Pros and Cons

Snowflake Data warehouse has many pros, such as scalability and a pay-as-you-go pricing model. However, there are also some cons to consider.

Here's a quick overview of the pros and cons of Snowflake:


1. Scalability: Snowflake's flexible, cloud-based architecture makes it easy to scale storage and compute resources as needed.

2. Low cost: Snowflake's pay-as-you-go pricing model makes it an affordable option for businesses of all sizes.

3. Query optimization: Snowflake's intelligent query optimizer enables customers to quickly access and analyze their data with minimal latency.

4. Security: Snowflake's multi-layered security protocols help protect customer data from unauthorised access and malicious attacks.

5. Easy integration: Snowflake integrates easily with other cloud-based platforms and applications, making it easy to use.


1. Complex setup: Setting up Snowflake can be complex and time-consuming, especially for customers who are unfamiliar with the platform.

2. Limited features: Compared to other data warehouses, Snowflake has limited features and capabilities.

3. Limited support: Snowflake's customer support is lacking in terms of response time, availability, and quality.

4. Unclear pricing: The pay-as-you-go pricing model can be difficult to understand and may not be cost effective for certain customers.

5. Slow performance: Snowflake's query optimization capabilities are effective, but performance can still be slow at times.

Final Verdict

Snowflake Data warehouse is a powerful, cloud-based data storage platform that makes it easy to access and analyze large amounts of data quickly. With its scalability, low cost, query optimization capabilities, and security protocols it can be a great option for businesses looking for an affordable data warehousing solution. However, there are also some downsides to using Snowflake such as a complex setup, limited features and support, and slow performance. That's why you need a better platform. 

Sprinkledata is a data science, engineering and analytics platform that simplifies the process of setting up and managing Snowflake Data Warehouse. It offers a secure, automated system that helps businesses easily manage and optimize their data warehouses, enabling them to get maximum performance and scalability out of their investments. With Sprinkledata, businesses can trust in a secure and reliable Snowflake Data Warehouse solution that allows them to access and analyze their data quickly and easily.​Get started now!

Written by
Soham Dutta


What is Snowflake Data Warehouse? - A Detailed 2024 Guide