Snowflake, which launched in 2014, is currently one of the largest cloud-based data solutions in the market, with $1.85 billion in venture capital financing. Snowflake is remarkable as it offers a cloud-based data warehouse-as-a-service. This platform is capable of supporting a broad set of data workloads and assisting teams in developing modern digital innovations. Check out Snowflake Tutorial

Are you wondering how Snowflake assists your team? Continue reading this blog to discover more about the architecture and major features of Snowflake.

Table of Contents

• Snowflake Data Warehouse
• Snowflake Architecture 
• Integration Support by Snowflake
• Features of Snowflake
• Why is Snowflake So Valuable?
• Conclusion

Snowflake Data Warehouse

Snowflake is a cutting-edge cloud-based solution that is offered as a SaaS application (Software-as-a-service). It provides quicker, more user-friendly, and more configurable storage systems, computing, and monitoring solutions as compared to older systems. Snowflake is not a database or a "big data" software system like Hadoop. It blends a brand-new SQL query engine with a cloud-native design that is really unique.

Snowflake provides the customer with all of the capabilities of an enterprise analytic database, as well as numerous unique functional capabilities. Snowflake's platform abstracts away architectural complexity, enabling you to operate various workloads across all major clouds with the flexibility, performance, and scalability demanded by modern organizations. You can now learn Mindmajix’s Snowflake Online Training here. 

Snowflake Architecture 

Snowflake comprises three layers:

1. Storage Layer

Locally optimized and compressed multi-micro partitioning is used by Snowflake to arrange the data. The information is stored in a columnar manner. The shared-disk approach simplifies data administration because data is kept in the cloud. 

The query processing data is fetched from the storage layer by the compute nodes. We just pay for the monthly average storage utilized since the storage layer functions independently. Due to the Cloud-based nature of Snowflake, storage is flexible and is billed monthly based on consumption per TB.

2. Compute Layer

For query execution, Snowflake makes use of a "Virtual Warehouse." There are two distinct layers of storage in Snowflake: one for queries and another for disk storage. Information from the storage layer is used to run queries in this layer.

Snowflake's cloud-based Virtual Warehouses are MPP compute clusters made up of several nodes having CPU and Memory. Snowflake enables the creation of several Virtual Warehouses to meet a variety of workload needs. A single storage layer can be used by each virtual warehouse. A virtual Warehouse is usually isolated from other virtual warehouses since each one has its own separate computing cluster.

3. Cloud Service Layer

This layer houses all of Snowflake's authorization, encryption, data management, and query optimization functions. In this layer, you'll find services like:

• When a request for authentication is made, it must pass via this layer.
• Snowflake queries are sent to this layer's scheduler then on to the Compute Layer for execution.
• This layer stores the metadata necessary to improve a query or filter data.

All three layers are self-scaling, and Snowflake charges individually for storage and virtual warehouse. The services layer is managed inside provisioned computing nodes and so is not charged. The benefit of the Snowflake design is that each layer could be scaled irrespective of all the others.

Integration Support by Snowflake

Integrating Data

Snowflake supports ETL (Extraction, Transformation, and Loading) processes via Informatica, Kafka, and others.

• Extraction: Extracting data from a variety of sources such as CSV, Excel, and Oracle.
• Transformation: Employing lookup and rules, manipulating or altering the retrieved data as required.
• Loading: This step involves transferring the altered data to the target source.

Tools for Business Intelligence

It provides data analysis, discovery, and reporting using Power BI, Qlik, and other tools. A business intelligence tool enables the visualization of data via panels, infographics, and other visual outputs.

Data Science and Machine Learning

While business intelligence tools may be used to analyze historical data for reporting purposes, machine learning technologies can be used to analyze huge data sets to find patterns and forecast future trends. The tools include Databricks, Spark, and others.

Interface Programmatically

The Snowflake may be connected via.Net, Node.Js, or an ODBC driver. When automating DBA tasks in Snowflake, Python or other language connectors can be used.

Features of Snowflake

• Efficient Data Sharing

The technology used by Snowflake enables frictionless data sharing between its users. It enables organizations to effortlessly communicate data with all high-volume consumers. It is irrelevant whether or not these media consumers are Snowflake clients, as client profiles may be created directly from the UI. This one-of-a-kind solution enables the administrator to develop and manage the Snowflake service for a customer.

Snowflake's unique data-sharing features enable rapid collaboration with business stakeholders across the organization. Finally, you can use Snowflakes SQL capabilities to simply and effectively access files from virtually anywhere on the planet.

• Performance

Snowflake's cloud-based design overcomes many of the problems associated with traditional data warehouses, resulting in improved overall performance. Snowflake offers near-infinite scalability by isolating concurrent processes on allotted resources. 

This means that each individual, group, program, or automated task may function independently of the rest of the system without impairing the network efficiency.

• Access & Security

Snowflake is a cloud storage platform based on a multi-cluster architecture that is accessible via AWS or Azure. It is a well-known cloud-based data warehousing technology that is utilized by provincial and local governments, banks, healthcare, and security organizations. Snowflake provides built-in SOC 1 and Type II compliance safeguards, as well as the ability to incorporate additional cryptographic primitives.

• Accessibility

You may face delays or failures while using a traditional data warehouse. Snowflake's novel multi-cluster structure allays these worries. Rather than having to wait for other computer processes to complete, data engineers and analysts may quickly obtain all of the information they want.

• No Administration Needed

That is accurate. Snowflake is a cloud-based application that requires no IT installation or administration. It has built-in caching, data security, and secure data transmission, ensuring that records of any complexity may be accessed and restored instantly.

Why is Snowflake So Valuable?

Snowflake largely relies on SQL, a language that many engineers are acquainted with and that many data researchers are now educated in. SQL is among the simplest metaprogramming languages and can be picked up pretty quickly by anyone. Developers using the API can use any programming language in conjunction with SQL. Snowflake has JDBC and ODBC interfaces, which enable users to link to a Snowflake database using any coding language.

Snowflake released a new developer experience lately. Snowpark enables data architects, data analysts, and developers to create code in their preferred language using well-known programming principles and then perform tasks such as ETL/ELT, data preprocessing, and model building on Snowflake.

Snowflake has made an attempt to adopt the cloud computing concept. You may pay for real use, but you can also specify when each sort of task is deployed. As an organization, you may have greater control over the number of workloads deployed, for how long, and at what pricing.

Conclusion

Snowflake's platform is always evolving; it is already much more than the data warehouse from which it evolved. Snowflake's technology powers the Data Cloud, a worldwide network via which thousands of businesses can deploy data and erase silos.

Numerous large IT organizations use Snowflake to speed business applications and data science efforts. Over the next few years, the solution will expand and become more comprehensive as more businesses join the data cloud.

Author Bio: 

I’m Sudheer Kuragayala, an enthusiastic Digital Marketer and content writer working at Mindmajix.com. I wrote articles on trending IT-related topics such as  Artificial intelligence, Cloud Technologies, Business Tools, and Softwares. You can reach me on Linkedin: Sudheer Kuragayala