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Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency.
With a wealth of such cloud solutions available on the modern market, Snowflake stands apart as it offers a new approach to storing information. So, what is Snowflake all about? Its architecture allows companies to perform efficient data management by scaling data storage and making computing tasks separate, saving significant costs. Furthermore, Snowflake’s data sharing capabilities enable users to share and manage secure data quickly in real time.
The solution became recognized worldwide in no time and gained more than 12,000 clients, which equals a market share of almost 19%.
What makes Snowflake so unique, and are there any caveats to it?
In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.
Snowflake is a cloud-based data platform officially introduced in 2014. This data warehouse is offered as a Software as a Service (SaaS) solution powered by a new SQL query engine. Unlike traditional warehouses, Snowflake is arranged for the public cloud and cannot be operated on-premises.
The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Therefore, the tool is referred to as cloud-agnostic.
What does Snowflake do?
Essentially, it enables you to:
To answer the question of what is a Snowflake database and the technology behind it, explore it as a part of the modern data pipeline:
By using the technology, companies can significantly enhance their data management. Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes, data sharing, and engineering.
We will guide you through the fundamental advantages and drawbacks of the Snowflake data cloud to provide you with a closer look at the solution.
Snowflake provides affordability, scalability, and a user-friendly interface. The tool’s high storage capacity is perfect for keeping large information volumes.
You can host Snowflake on numerous popular cloud platforms, including Microsoft Azure, Google Cloud, and Amazon Web Services. Such hosting options make the Snowflake cloud an excellent data warehouse solution for organizations in multiple industries.
Traditional information storage tools typically require significant investment in servers and other related hardware. Snowflake data warehouses deliver greater capacity without the need for any additional equipment. The technology is completely cloud-based, meaning you can implement it to the extent you need with further scaling up or down.
Companies often deal with sensitive information that needs reliable protection. Snowflake data cloud provides IP whitelisting to restrict access to data to authorized users. With techniques such as two-factor authentication, SSO authentication, and AES 256 encryption, Snowflake ensures solid data security.
Snowflake database features a user-friendly design allowing customers to arrange data in the most suitable and convenient manner. Once adjusted to your needs, this responsive platform can perform optimally without human interference. We must stress that the proper approach to setting up crucial aspects of the system ultimately results in the exactly sought-after performance, but only when and if you prioritize that approach.
Traditionally, users may feel safer with physical access to the server where information is stored in case of failure. However, with the Snowflake DB, customers do not need to worry about that. Although the databases are kept in the cloud, the solution carries contingencies for disaster recovery. It establishes numerous data centers that copy your data and guarantee easy access in unforeseen situations.
Every business may have fluctuations in its activities. Thus, there are periods of extensive network use as well as lower workloads. With the help of Snowflake clusters, organizations can effectively deal with both rush times and slowdowns since they ensure scalability upon demand. Therefore, companies can seamlessly accommodate all the changes in user numbers.
The schema of a Snowflake warehouse is a step beyond the traditional star schema design methodology. Both schemas offer their own range of authentic benefits in data warehouse arrangement. A Snowflake schema is multidimensional, and its design resembles a snowflake (hence solution’s name). It introduces an upgrade to the basic star schema. First, it features better optimization for MOLAP modeling tools. Second, although being more complex, the schema delivers better storage savings.
Despite having convincing benefits, Snowflake warehouses do bring about a few downsides. However, this doesn’t prevent the tool from being a primary data warehouse for many users.
So far, Snowflake manages semi-structured and structured data. Unstructured data support is expected to come in the future.
Data migration to Snowflake can be a challenge. The solution provides Snowpipe for extended data loading; however, sometimes, it’s not the best option. There can be alternatives that expedite and automate data flows.
The advantage of high scalability and the opportunity to pay only for what a customer needs has its downsides when it comes to specific bills. Thus, Snowflake applies no data limits to computing and storage. Companies can easily exceed the use of their services and discover it only during billing.
Once we have basically answered the question, “What does Snowflake do?” it’s time to address the question, “How exactly can Snowflake be applied?”
Let’s look at a brief overview of the most widespread database use cases.
Modern companies typically receive data from multiple sources. Therefore, quick data ingestion for instant use can be challenging. Snowflake relieves the issue with the help of Snowpipe: a continuous data ingestion service that allows for quick and convenient data load from external locations, including Azure Blob, GCP bucket, and S3.
All business intelligence operations heavily rely on quality data, making data warehousing a crucial part of the process. Data warehousing is a vital constituent of any business intelligence operation. Companies can build Snowflake databases expeditiously and use them for ad-hoc analysis by making SQL queries. Further, Snowflake enables easy integrations with numerous business intelligence tools, including PowerBI, Looker, and Tableau.
Organizations harness machine learning (ML) algorithms to make forecasts on the data. ML models, in turn, require significant volumes of adequate data to ensure accuracy. Moreover, each experiment must be supported with copies of entire data sets. Snowflake cloud platform has a zero-copy cloning feature to conduct this operation seamlessly. Besides, the platform provides all the required integrations to help engineers prepare data and build ML models.
Maintaining data security is crucial for any company. With traditional data warehouses, organizations may find it challenging to prevent data breaches. Snowflake is easy to connect with data governance tools like Informatica and Immuta for maximum data protection and data access under complete control.
Snowflake architecture represents a fusion of conventional shared disk architecture and shared-nothing database architecture with massively parallel processing (MPP).
In a nutshell, this technology manages to get the best of both worlds. It keeps all the information in a central data repository attributed to a shared disk architecture. At the same time, it applies MPP, which is part of a shared-nothing architecture, to enhance computing power.
Furthermore, a shared-data approach stems from this efficient combination. The background for the Snowflake architecture is metadata management, so customers can enjoy an additional opportunity to share cloud data among users or accounts.
As it was mentioned earlier, Snowflake separates computation and storage. This delivers considerable benefits to organizations with vast storage and low CPU. Snowflake architecture comprises three fundamental layers:
It’s worth considering the existing experiences to make the maximum benefits out of the Snowflake cloud platform. Here are some key recommendations for Snowflake use according to customer reviews and available options.
It’s better to adhere to a multi-stage process of landing the files in a cloud storage and loading them to a landing table before transforming the data. You will ease orchestration and testing by splitting the whole process into predefined steps.
It makes sense to retain the raw data history, which you can store with the VARIANT data type to enable automatic schema evolution. Therefore, you’ll be empowered to truncate and reprocess data if bugs are detected and provide an excellent raw data source for data scientists.
With on-premise data warehouses, storing multiple copies of data can be too expensive. You can use Snowflake cloud computing to store raw data in structured or variant format, using various data models to meet the needs. Each model carries its specific benefits and allows for reloading and reprocessing of data in the event of errors.
Both of these features guarantee the quickest and most efficient way to fulfill data loading.
When ingesting data with the COPY command, it’s better to utilize partitioned staged data files. This is the way to reduce the work of scanning excessive numbers of data files in cloud storage.
Massive SQL statements that join and process large numbers of tables do not usually guarantee an efficient working process. Such an approach can lead to over-complex code that operates poorly. Conversely, splitting the transformation pipeline into multiple steps and writing results to intermediate tables would simplify the code, ease the testing of intermediate results, and expedite performance.
Snowflake performs its primary roles of ingesting, processing, and analyzing billions of rows at incredible speeds using SQL statements, which operate upon the data set at a time. Row-by-row processing may lead to programming loops that update rows one by one. Therefore, such processing can significantly hamper query performance. Instead, you can utilize SQL statements to process all table entries simultaneously.
Experienced data engineers value simplicity. Simple solutions are easier to work with, understand, and diagnose problems.
You can enjoy the following specific opportunities by using Snowflake cloud data:
If all the remarkable features and advantages of a Snowflake warehouse have convinced you to implement or migrate to this platform, here are several tips on where to start.
Before you start using Snowflake services, familiarize yourself with the respective documents. Explore the information about getting started, creating an account, and organizing your working processes, such as using REST API to access unstructured data.
Snowflake offers an ecosystem of third-party integrations. If you’re already utilizing any software to work with data, you can check which options Snowflake provides. Furthermore, the platform enables connectivity with multiple technologies, including business intelligence tools, machine learning solutions, data science platforms, and more.
Snowflake offers four plans: Standard, Enterprise, Business Critical, and Virtual Private Snowflake. Check the pricing guide and figure out which plan suits your business best.
Although the Snowflake community is not so big yet, it may still help you get the answers to your questions or more detailed information on the topics of interest. You can also visit Snowflake’s YouTube channel, which offers valuable content.
Snowflake has an online university that aims to educate users with all levels of expertise through a variety of courses.
Whether you’re already applying data management solutions or planning to implement Snowflake as your first data platform, the respective transformation can challenge your organization.
Handling the task with professional guidance will help to make your transition successful. NIX United has extensive expertise in cloud-based computing solutions. We specialize in bringing innovative technologies to your service. Our experienced team will help you select the optimal tools for your goals, safeguard your data, and manage your entire transformation journey for maximum outcomes.
Entrust your project to our experts, and we’ll identify your unique path to advanced and profitable data operations.
Artur is passionate about shaping the future of cloud architecture and driving innovation in enterprise solutions. He adeptly empowers businesses to thrive in fast-paced environments, skillfully leveraging the power of serverless technologies to optimize cloud economics.
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