Data has proliferated over the past ten years. Data already controls a sizable amount of our consumer lives, thanks to improvements in wireless connectivity, processing power, and the spread of the Internet of Things (IoT) devices.

The same holds for businesses that are relying more and more on data to enhance their offerings, processes, and revenue.

There are no indications that this trend is slowing down, as market research firm IDC projects that by 2025, the amount of data produced annually will reach 160 ZB, a tenfold increase from the volume produced in 2017.

Businesses must develop a means to interpret the vast amounts of data that are already available. However, the adoption of multi-cloud and the distribution of this data between on-premises and cloud environments provide serious difficulty. Today, maintaining a mix of on-premise or cloud data warehousing solutions is a challenge for many enterprises.

In this article, we'll discuss the advantages and difficulties of cloud-based data warehouses and take a close look at snowflake, the top cloud-independent platform for data warehouses, including its advantages and contrast with other cloud data platforms. We'll show how utilizing snowflake enables businesses to manage enormous amounts of data dispersed across several clouds and on-premises, allowing them to concentrate on data gathering and make better choices based on their data.

Data Platform as a Cloud Service

As many businesses struggle to make sense of all their data, choosing a data platform that can handle enormous volumes of big data is the ease of use, reliability, and fast speeds.

As part of a long-term strategic commitment to transform into a cloud-first, data-driven company, many businesses already use cloud data platforms or are considering doing so.

The most widely used option, snowflake, supports multiple cloud infrastructures, including those from GCP, Microsoft, and Amazon, Microsoft. Users may concentrate on data analysis rather than managing and optimizing, thanks to its highly scalable cloud data warehouse. 

One of the few enterprise-ready online data warehouses that offers simplicity without forgoing functionality is snowflake, so let us explore it.

What is Snowflake?

To achieve the ideal combination of performance and cost, it automatically scales up and down. The unique selling point of snowflake is how it isolates computing from storage. This is essential since virtually every other database, including Redshift, blends the two, necessitating that you size for your maximum workload and pay the associated costs.

With snowflake, you can centrally store all of your data and scale your computation independently. For instance, you can script a sizable snowflake warehouse for the data load and scale it back if you need near-real-time data loading for sophisticated transformations but only a small number of difficult queries in your reporting. 

For instance, if your reporting has relatively few sophisticated queries, but you need near-real-time data loads for a range of processes, you can create a sizable snowflake warehouse for the data load and scale it back down once it's finished- all in real-time. This reduces costs without compromising the objectives of your solution.

To understand the snowflake database better, let us look at its architecture.

Snowflake Architecture

Snowflake's hybrid shared-nothing and classic shared-disk data architectures allow it to produce results so quickly. Similar to the shared disk database, it employs a single repository for persisting data that is available from all compute nodes. On the other hand, snowflake performs queries utilizing MPP (massively parallel processing) compute clusters, which are akin to shared-nothing architectures in that each node maintains a subset of the full data set locally.

With this strategy, the performance and scale-out advantages of shared-nothing architecture are combined with the ease of shared-disk architecture.

Three fundamental layers make up the distinctive architecture of snowflake: 

To understand these better, let us understand each of them in detail.

Cloud Services

The Snowflake Cloud Services layer serves as the system's central nervous system, directing and controlling the entire system. These services connect all of snowflake's various parts to handle user requests, from login through query dispatch. A snowflake completely maintains the services layer, which utilizes computer instances that the cloud provider has provided to snowflake.

The following services are controlled by this layer:

  • Access control measures and user authentication
  • Infrastructure Management through the control of virtual storage and warehouses
  • Manages sessions, secures and protects data and assembles and optimizes queries
  • The Metadata Store of tables and micro partitions, which drives several distinctive snowflake capabilities, such as time travel, data sharing and zero-copy cloning, is an essential part of the services layer.

Query Processing

When handling query execution, this layer makes use of resources that a cloud provider has made available. With snowflake, you may build distinct MPP compute clusters (referred to as Virtual Warehouses) that don't share compute resources and don't affect performance. 

The following are the main advantages of virtual warehouses:

  • Scalability: Scaling up or down a virtual warehouse is possible without any interruption or damage.
  • Zero Contention: Due to dedicated hardware, no data warehouse is dependent on the other.
  • Auto-resume: If a new SQL query needs to be executed, it can be resumed in milliseconds.
  • Data changes: Any data changes are immediately shared with all due to the shared data storage.
  • Auto-suspend: When queries are not running on a data warehouse, it is automatically suspended.
  • Pay as you go: Pay only for the compute resources you use as compute, and storage is decoupled on snowflake.

Database Storage

Using JSON, AVRO, and Parquet as examples, snowflake uses infinitely scalable and cloud security storage to store organized and semi-structured data. Tables, schemas, or databases are the components of the storage layer. The data is then rearranged by snowflake into its own optimized, compressed columnar format. 

Data items kept in snowflake are hidden from users and only accessible through SQL queries through the Compute layer. The storage management layer is made up of several scale-aware encrypted micro partitions.

We have learnt about snowflake and its architecture, but what are the benefits of using snowflake? Let us dive into its various benefits!

Benefits of Snowflake

To address many of the concerns with traditional hardware-based data warehouses, such as restricted scalability, data transformation problems, and delays or failures brought on by high query rates, snowflake was built expressly for the cloud. 

Here are seven advantages snowflake can provide for your company:

  • Accessibility and Concurrency
  • Security and Availability
  • Speed and Performance
  • Flexibility and Elasticity
  • Seamless data sharing
  • Support and storage for structured and semistructured data
  • Scalability

We will learn about each of these in detail now.

Accessibility and Concurrency

You might encounter concurrency problems (such as delays or failures) with a typical data warehouse and a sizable number of clients or use cases when too many queries are competing for resources.

With its distinctive multicluster architecture, snowflake addresses concurrency issues: queries from one virtual warehouse rarely affect queries from the other, and each virtual warehouse may scale up or down as necessary. Data scientists and analysts don't have to wait for other load and processing processes to finish; they can acquire what they need right away.

Security and Availability

Snowflake is spread across the AWS or Azure availability zones of the platform on which it operates and is made to run constantly and withstand component and network failures with little impact on users. Additional security levels are offered, including support for PHI data for HIPAA clients and encryption for all network connections. It is SOC 2 Type II certified.

Speed and Performance

Due to the elastic nature of the cloud, you may ramp up your virtual warehouse to take advantage of additional compute resources if you need to load data more quickly or execute a large number of queries. After that, you can reduce the size of the virtual warehouse and only pay for the time you spend.

Flexibility and Elasticity

It provides greater accessibility, elasticity, adaptability, and value. The user can utilize the query services and the warehouse in the same data store. The snowflake is more adaptable in terms of usage because it can only be used when it is required.

Seamless Data Sharing

The architecture of snowflake facilitates data sharing between snowflake users. Additionally, with reader accounts that are applied to applications from the user interface, companies can share data with just about any data consumer without regard to whether they're a snowflake user or not. With the aid of this functionality, a customer's snowflake account can be created and maintained by the supplier.

Support and Storage for Structured and Semistructured Data

Without first converting or transforming your data into a set relational schema, you can combine organized and semi-structured and unstructured information for analysis and put it into a cloud database. Snowflake automatically improves the data's archival and querying processes.


When there is a spike in demand, snowflake provides immediate data warehouse scaling to handle concurrent issues. It scales without requiring the redistribution of data, which can cause end users a great deal of inconvenience.

We have ventured so much into the snowflake database, but how is it different from Data Platforms? This is what we will discuss next!

Difference Between Snowflake and Other Data Platforms

Modern data warehousing solutions are being built in the cloud by businesses employing top cloud providers like GCP, Microsoft Azure, and AWS, along with integration from snowflake.

All of them provide highly scalable and dependable data warehouse solutions, although the table below lists some discrepancies in technical specifications and pricing structures.

Based on your use cases and requirements, you and your company must choose the finest cloud data warehouse platform. If you're at a loss, Contino may work with your company to examine your business requirements, recommend the finest cloud data warehouse platform, and assist in its development.

The major differences between snowflake and some other popular data warehouses are summarized below:

Basis of Distinction


Google BigQuery

Azure Synapse

Amazon RedShift



(Shared Nothing and Shared Disk architecture)



Shared-nothing MPP




Require some manual maintenance



Removes/adds nodes automatically.

It lets users compute and scale storage independently.

It handles scaling automatically. 

Computes and scales independently.

Additional storage is needed for the dedicated option but automatic for the serverless option.

Compute RA3 nodes and perform storage decouple.

Data types

Semi-structured and structured

Semi-structured and structured

Semi-structured and structured

Semi-structured and structured

Analytics Ecosystem

Supports main Data Analytic and BI tools.

Locker, Google Workspace and Business Intelligence.

PowerBI for business and Azure ecosystems for analytics.

AWS Quicksight for Business Intelligence and other BI tools integration.

In-memory Capability






Payment for computing time and storage.

Flat rate and on-demand.

Payment for computing time and storage.

Reserved instances or on-demand.

Database Model










Recovery and Data backup





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1. What is Snowflake Architecture?

The architecture of snowflake is a cross between the conventional shared-disk or shared-nothing database designs. Snowflake utilizes a central data source for persisting data that is accessible from across all multiple processors in the platform, much like shared-disk systems.

2. What is a Snowflake data warehouse?

Using the cloud-native platform snowflake, organizations can share data securely without the need for different data warehouses, lakes, and marts.

3. What kind of database is Snowflake?

A relational database stored in the cloud called snowflake is used to create data warehouses. It is constructed using the Google cloud platforms, Azure, and AWS and combines the features of conventional databases with a number of fresh and innovative options. It is distinctive in how it responds to the shifting needs of enterprises.

4. Is Snowflake the same as SQL?

No, they are different. The most widely used standardized version of SQL, ANSI, is supported by the data platform and data warehouse snowflake. This means that snowflake can be used for all of the most popular processes. Additionally, snowflake supports each and every operation—including create, update, insert, and so forth—that permits data warehousing processes.

5. How does a Snowflake database work?

For operations including loading, manipulating, and querying data, snowflake distributes compute resources among clusters for massively parallel processing (MPP). Users can isolate workloads inside certain virtual warehouses using this feature.

6. Is Snowflake a database tool?

No current database technology or "big data" software platforms like Hadoop are used to build the snowflake data platform. Instead, snowflake blends a cutting-edge architecture specifically created for the cloud with a brand-new SQL query engine.

7. Is Snowflake an ETL tool?

Snowflake works with a variety of data integration technologies, including Informatica, Matillion, Tableau, Talend, and others, and supports both ETL and ELT.

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