Snowflake deployment: stages and best practices

What is Snowflake and Big Data?

Snowflake is an advanced cloud-based data management platform that allows you to store, process and analyse large data sets (Big Data) in a flexible and scalable way. It was designed to address modern business needs, offering a wide range of features and analytical tools.

One of the key architecture elements of Snowflake is its unique approach to data storage, based on a three-tier architecture. The architecture consists of a data storage layer, a computing layer and a metadata management layer, which enables flexible management and automated resource scaling (users only pay when they need to use the platform).

Importantly, Snowflake is offered in the SaaS model (Software as a Service), integrated with the three largest cloud providers: Azure, AWS and GCP, allowing companies to easily move and process their data between different cloud environments.

Why do companies choose Snowflake?

“Pay as you go”

One of the main drivers of its popularity is that Snowflake does not require you to invest in your own data infrastructure. In the cloud, companies only pay for the resources that they actually use. They no longer need to worry about the operation and maintenance of an in-house environment, or about its scalability.

Snowflake architecture

What makes Snowflake stand out from other cloud solutions, as mentioned earlier, is its innovative three-tier architecture, which separates between computing, storage and metadata. Each layer works independently, which enables flexible scaling, dynamic resource scaling to match business needs and effective cost optimisation.

Data security in Snowflake

Snowflake offers advanced security features such as data encryption, row-level access control or operational auditing, which ensures the most rigorous data protection.

Secure sharing

Snowflake provides advanced access control mechanisms, allowing companies to manage access to data more effectively and securely share data with internal users and external partners, which is crucial in the current business landscape.

Effectiveness

Organisations appreciate its speed and performance. Snowflake ensures instant data access and can process large data volumes in a very short time. This speeds up analytics, reporting and decision-making.

Easy integration

Snowflake also offers easy integration features with other tools and applications, which helps companies move and process data between different environments. The platform can also be integrated with various analytics tools and enables easy data access from the level of popular visualisation tools such as, e.g. Power BI.

Snowflake Marketplace

Thanks to Snowflake Marketplace, companies can quickly expand their analytical and operational capabilities through ready-made solutions all available in a single place. It is a platform where Snowflake users can browse, buy and use ready-made solutions and tools for data analytics, integration, visualisation and other data-related tasks. Snowflake Marketplace provides access to various applications, data sets, ETL tools and solutions and technologies based on artificial intelligence and machine learning, which can be easily integrated with the Snowflake platform.

Case study: Snowflake in e-commerce

Based on a case study of an e-commerce company that decided to deploy Snowflake, we will analyse the key stages of the process, including aspects such as: needs analysis, planning, deployment and configuration, and the importance of training for getting the most out of the platform.

Company needs analysis

An e-commerce company, after several years on the market, decided to improve its ability to process increasing data volumes from different sources, including transactions, customer data, marketing campaign data, website traffic data, photos, multimedia, etc. The company needed a platform that would be able to handle various data types and perform different analytics, such as ad hoc analysis or predictive analytics.

Moreover, the company placed an important emphasis on real-time or close-to-real-time reporting. Of course, security was also important, because customer data are extremely valuable and require special protection. The company had already stored some of its data on a Microsoft Azure public cloud platform and whenever it needed a new solution, it had opted for serverless options.

Following a thorough needs analysis, the company decided to deploy Snowflake.

Planning

At the outset, the company needed to analyse its current data architecture, which encompassed data held in the Azure Data Lake Storage, as well as those stored in other locations. This was followed by an analysis of the data structure and format so that the data could be adjusted to Snowflake requirements and maintain post-migration consistency and integrity.

The planning stage included drawing up a migration schedule to minimise disruptions in the operation of the e-commerce platform. Migration tests were carried out on a small data sample. In addition, the company developed a data security strategy and a data recovery plan to ensure data security and consistency. They also prepared a preliminary, but highly realistic, cost estimate for the management board.

The entire planning process was thoroughly planned out and based on solid analysis and best data governance practices, which allowed the e-commerce company to migrate their data to Snowflake smoothly and without problems, ensuring business continuity and effective data use in further processes.

Snowflake configuration

The next step involved the integration of Snowflake with existing data sources and its configuration. The data that had earlier been stored in Azure Data Lake Storage was configured for direct access by Snowflake, eliminating the need for migration. Other data, which had to be moved, were cleaned, transformed and transferred to Snowflake. This hybrid migration model allowed the company to avoid having to migrate all its data, tapping the advantages of both platforms.

The company configured account types, computing component types and predicted data warehouse capacity. The platform was deployed in line with the company’s security policies and the new data governance strategy. The plan and schedule drawn up at the earlier stage ensured the smooth deployment of a stable solution.

Staff training

Employees were prepared to work with Snowflake in a process that included:

• dedicated training courses that included the fundamental and advanced aspects of Snowflake;
• practical demonstrations of the features and tools available on the platform;
• interactive training sessions that allowed employees to gain practical skills in areas such as crafting effective and optimal SQL queries;
• individual consultations and technical support for employees who needed extra help understanding and navigating the platform.

Snowflake testing and optimisation

During the testing and optimisation stage, the company performed a series of tasks aimed at ensuring optimum performance and effectiveness, e.g.:

• they carried out performance tests to assess data processing speed, SQL query response and platform scalability. Testing covered different load scenarios so as to assess system behaviour under real-world conditions;
• They performed an SQL query analysis and optimisation to improve data processing time and tap the full potential of the platform, including changes in indexes and data partitioning;
• based on performance test results, the team also adjusted the scaling of computing resources and data warehouse capacity to ensure adequate data processing efficiency as a function of needs;
• they carried out integration tests with core tools and systems, such as data visualisation tools (Power BI) to make sure that Snowflake works as expected and ensures smooth data transfer;
• They performed a usage cost analysis and fine-tuned the configuration of the platform to minimise costs, while ensuring adequate performance and functionality.

Snowflake support and maintenance

For the purposes of the support and maintenance stage, the company created and trained a dedicated support team. For instance, in the event of a data processing delay in Snowflake, the team would perform a system performance analysis, identify the cause of the problem and make the necessary adjustments, such as optimising SQL queries or scaling computing resources.

The team also regularly updated internal data architecture documentation. The documents stored not only technical data but also business information. It became a sort of an in-house data catalogue and was heavily used in daily business.

Snowflake was also regularly updated to tap new features and improvements. The company wanted to ensure continuous platform availability, stability and effectiveness to be able to continue using its data in key business decisions.

Snowflake deployment – more examples

E-commerce, of course, is just one of the many industries that deploy Snowflake. Today, basically all companies work on IT systems full of data that can and should be analysed. Examples of companies from different industries that have already decided to roll out Snowflake are listed on the platform’s official website in the Customers tab. The tab also includes a case study of Pfizer (pharmaceutics), Siemens (technology), MAX Burgers (food), Sainsbury’s (retail) or Scania (logistics) and many, many other examples.

Snowflake – conclusions and takeaways

Snowflake, a modern data processing platform, offers the scalability, flexibility and performance features required for effective data management in the digital era. Thanks to its unique three-tier architecture, Snowflake allows companies to store, process and analyse big data in real time. Its many advantages, such as easy use, reliability, advanced security features, and the pay as you go model make it an ideal choice for all companies looking for an innovative data analytics solution.