A Quick Examination of Snowflake: Our Preferred Cloud-Based Data Warehouse Solution

Setting up a data warehouse used to be an expensive and complex affair. Large-scale databases used by enterprises require advanced schema designs, optimized resource usage, and a huge amount of computing and storage resources. However, SaaS-based data warehouses like Snowflake can make it easy as they handle all the responsibilities and burdens associated with setting up a data warehouse.

You can simply subscribe to the Snowflake services, scale it up and down as required, and take advantage of all the advanced data warehousing capabilities with little effort. Snowflake is entirely built to work with a cloud-based model, making it extremely flexible and efficient, which any typical data warehousing solution cannot replicate.

From our extensive knowledge and experience working with data solutions for a wide range of applications, we usually recommend using Snowflake for data warehousing needs. It enables us to ingest raw data from any source into a single centralized repository.

Overview Of Snowflake Architecture

Snowflake is designed to work under a 3-layer architecture. Unlike most other data warehouse solutions usually built on top of Big Data, Snowflake has its unique SQL engine and three-layered cloud-native architecture. It is a fully self-managed service that does not require any hardware installation, configuration, or management. It also does not require any software installation, confirmation, management, maintenance, or upgrade effort from the user. Everything is taken care of by Snowflake.

It is completely built on top of public cloud infrastructure and takes care of everything related to running the warehouse solution, from software installation to storage management and all updates.

Cloud Service Layer

This is the service layer that the user usually interacts with, providing common functionalities like:

• SQL performance optimization

• Security and authentication

• Metadata management

• Query parsing

• Access control

• Database connectivity

Computer Services Layer/Query Processing

The computer services layer is where the virtual warehouses of Snowflakes are hosted. While Snowflake comes equipped with numerous capabilities, one of its unique offerings is its ability to create unlimited virtual warehouses. This allows users to run a large number of independent workloads on data without having to worry about contention.

All of these individual warehouses can be scaled up to a 128-node cluster, allowing users to run highly resource-intensive tasks without sacrificing performance. It allows for efficient scaling of data volumes as well as automatic handling of a high number of users.

This is a transitory storage layer that enhances the overall performance of your data warehouse. All the query processing and execution are performed in this layer.

Cloud Storage Layer/Database Storage

This is the actual source storage layer where all the data is stored in the cloud and replicated into three different data centers, each with a complete built-in disaster recovery solution. Data in Snowflake is stored in an internalized, compressed format so that it is accessible only through the SQL query operations executed by Snowflake. Snowflake completely maintains the data storage.

Benefits of Snowflake

As mentioned earlier, Snowflake eliminates a lot of complex database operations and manual setup procedures. It lets you get started with your data solutions with ready-to-use SaaS-based services. Here are some ways it differs from a traditional data warehouse and the benefits it brings

• Flexibility in payment and usage

Snowflake is a cloud-based service available for use in a subscription payment model. So, you only get to pay for the services you use and need not take up the expensive initial setups, hardware investments, maintenance, and other manual effort required by traditional data warehouse systems.

• Ease of scalability

With Snowflake, you can scale up and down the resources and storage you need with little to no effort. Snowflake offers elastic storage technology, allowing you to automatically provision your storage requirements using hot/cold storage strategies.

• Cloud agnostic

Snowflake can be used with all major cloud platforms like Azure, GCP, and AWS. It is built as a cloud-native solution that is cloud-agnostic and can work well with any cloud platform.

• Extensive data format support

Snowflake supports structured and semi-structured data and provides functionality to convert your data into more acceptable formats that can be compatible with SQL queries.

• Decentralized data

Snowflake allows your data to be decentralized, thus making it possible for remote access and implementing a granularized access control. It helps avoid lengthy data transfers, which would be the case when working with centralized databases.

• Enhanced performance

Snowflake provides quick query results and advanced analytical operations as you can use the extensive cloud resources. Traditional systems could be limited because they can only use the resources available within the hard limits set by the available hardware and in-house storage systems.

• Embedded analytics

Snowflake comes equipped with several analytical applications that simplify your data operations.

Pricing

Like most other cloud-based services, Snowflake also has a subscription model where you only pay as much as you use. It allows for a high level of flexibility in choosing the computer resources you need, and you can turn on or off any resource as per your requirement. It provides linear cost scalability, allowing you to plan well and improve your analytics infrastructure.

The overall pricing for Snowflake largely depends on how much data volume you need to store and the compute time you utilize.

Snowflake offers various pricing models, such as Standard, Enterprise, and Business Critical, depending on the level of support and features provided. You can also pay for your storage uses upfront or pay per month based on your on-demand storage requirements.

Why Choose Snowflake?

Snowflake offers a lot in terms of both performance and functionality. It is especially apt for businesses developing their analytical models and customized business intelligence solutions.

With its virtual warehouses, Snowflake allows users to create multiple database instances and data models on the same data. This, in turn, can be a huge help in figuring out more novel ways to gain deeper insights and real-time querying on both live and cached data.

To connect to Snowflake services, you can make use of its:

• Web-based UI

• Command line clients such as SnowSQL

• Third-party drivers such as ODBC and JDBC

• Native connectors developed using Python, Spark, and so on

• Third-party ETL connector tools

Another important reason Snowflake fares better than any other alternative data warehousing solution is its ability to stay updated and integrate with advanced technologies. It keeps improving its offerings in the analytical space. Recently, Snowflake acquired Streamlit for a whopping $800M and thus made it possible for their users to build advanced data applications.

The additional functionality acquired from Streamlit allows Snowflake users to build data visualizations and native machine learning-based applications. This makes it an attractive option for data engineers looking to build advanced data apps without relying on full-stack engineering teams.

With Snowflake’s ability to run Python via Snowpark and predictive model and machine learning capabilities, the opportunities for leveraging data have increased by a huge magnitude.

In a Nutshell

In conclusion, Snowflake stands out as a cloud-based data warehouse solution that simplifies data management and analytics complexities. Its three-layer architecture, flexible payment model, scalability, and compatibility with major cloud platforms make it an excellent choice for businesses seeking efficiency and performance in their data operations.

With its recent acquisition of Streamlit, Snowflake has expanded its capabilities to include data visualizations and native machine learning applications, further solidifying its position as a leading choice for modern data engineering and analytics needs.

That said, if you need a trusted partner to help you with your data warehousing needs, Data Solutions Consulting Inc. is here to help you. Contact us today to learn more.