When Snowflake was created in 2012, it took the company just nine years to establish itself. Despite competing in a well-established market with several significant players, the company is able to innovate and become a leader in the space. Furthermore, it does so in a market dominated by Oracle and other well-known cloud providers like Amazon and Google. A cloud data warehouse solution, Snowflake began as a cloud data warehouse solution but is now more of a business core. Snowflake advertises itself as a comprehensive cloud data platform. You can operate numerous workloads across all three main clouds with the elasticity, performance, and scale required by modern businesses thanks to Snowflake’s platform.
Snowflake, a software startup, had the largest IPO ever. The stock price doubled on the first day of trade, giving Snowflake a market capitalization of almost $60 billion. Snowflakes became the topic of conversation for everyone.
The valuation of $60 billion is irrational at first look. Snowflake is not only losing money (a net loss of $349 million in 2019), but their revenue is also negligible when compared to their market capitalization. They made $403 million in revenue in the previous year. The company’s market capitalization is around 150 times its sales because of this. So, what precisely is driving their stock price to such high levels? Investors look at a variety of factors in addition to sales and profitability. Snowflake has god-like stats when compared to other software companies. My goal is to provide context for the metrics in Snowflake’s S-1, as well as provide an overview of how remarkable they are.
Adaptive Data Platform
However, Snowflake’s product makes a significant impact. It eliminates the customer’s responsibility for installation, configuration, management, and maintenance. On the other hand, it provides a straightforward framework for integrating massive amounts of data into a single database. The data in Snowflake can subsequently be used for a variety of purposes, including data analysis and data linkage and restructuring to feed another application.
Snowflake’s platform is completely Cloud-Native
Snowflake developed the platform entirely on its own. As a result, a cloud-native solution can be deployed on AWS, Google Cloud, or Microsoft Azure. When creating the Snowflake platform, conventional cloud characteristics such as scalability and affordability were taken into account. Additionally, Snowflake can safeguard data up to the raw level and adhere to all regulatory and governance requirements.
Working with Snowflake is a Rewarding Experience
Snowflake is highly reliant on SQL (Structured Query Language), which most developers should be familiar with and in which many data analysts are now educated. SQL is one of the most basic languages for working with data, and anyone can pick it up fast. To work with Snowflake using the online interface, you must additionally utilize certain SQL commands.
Developers using the API can use any programming language in conjunction with SQL. Snowflake has interfaces for Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC), allowing you to connect to a Snowflake database from any programming language.
Structured data, such as CSV files or Excel sheets with tables, rows, and columns, can be handled by Snowflake. Snowflake, on the other hand, has adapted to the cloud era and can now handle XML and JSON datasets. When data from SaaS solutions have to be fetched and processed in Snowflake, this is ideal.
Regardless of how the data appeared in the source, it can be easily fetched in Snowflake using SQL and filtered for specific columns as needed. It is also easy to swiftly merge data from multiple tables, even if the total number of rows is in the hundreds of thousands or millions.
Snowflake has also recently released a new developer experience. Snow park will enable data engineers, data scientists, and developers to create code in their preferred languages, using familiar programming ideas, and then run workloads on Snowflakes such as ETL/ELT, data preparation, and feature engineering.
Snowflake Rules Metadata
Snowflake has made an effort to adopt a cloud-based business model. You have the option of paying only for what you use, but you can also choose when to deploy which workloads. Workload deployments can be more tightly managed when an organization is in charge of how many, when, and how much they cost.
Let us say you want to analyze data from a massive database that contains millions of records. After that, you could have your data analyst begin by working with a smaller, more manageable set of data before moving on to more complex queries and algorithms. These models can then be tested on the entire dataset to see if they produce the desired results.
Snowflake makes this process incredibly simple. However, it is also critical to continue growing over time. Consider developing a query and algorithm, then applying it to a massive dataset. In this case, it makes sense to increase the workload on the servers involved.
Collaborating by Data Exchange
Good data analysts are in high demand, but there are not enough of them to go around. As a result, many businesses hire outside consultants to assist them in analyzing critical business data on a regular basis. Additionally, Snowflake makes it possible for people outside of the company to work with the data. Users and user rights can be tailored for this purpose. It is also possible to see down to the row level which data a particular user has access to. As a result, a smaller data set can be given to an outside data engineer to work with. This means that sensitive information that is necessary to analyze is hidden from the public, but an internal employee can use these analyses on the entire dataset to do just that. Certain compliance requirements, in some cases, can be better met this way.
Finally, knowing that Snowflake makes data interchange easier is comforting. Anyone with valuable Snowflake data sets can make those data sets available to the rest of the community. Either for free or for a price. As an illustration, there exist databases containing data on things like IP addresses and weather forecasts for various cities throughout the world. The Snowflake data exchange can be a helpful resource for application developers because it makes it easier to construct apps faster.
It is clear that Snowflake’s platform is far more than just a data warehouse; it is already a lot more. Using Snowflake’s platform, millions of companies around the world can mobilize their data and break down the silos that keep it locked away. Snowflake already has a large number of high-profile IT companies as users, using it to streamline application development and data science efforts. As more businesses move their data onto the cloud, the solution will continue to expand and become more comprehensive. Because of our initial discussion, it is clear that there are benefits and potential for a wide range of businesses. The cloud-native aspect, speed of innovation, and ease of usage will draw in many businesses.