In the modern age, there’s a lot of data that needs to be collected, structured, and analyzed in order to deliver transformational insights. A data warehouse is typically used to store and maintain data that helps guide decision-making. However, there are many pain points and issues with data warehouses, even though there have been great technological strides and inventions made in the last few decades.
Many data warehouses have hit the mark with delivering solutions for specific purposes. For example, there are data warehouses that have been specifically engineered to handle large amounts of data (basically big data lakes). However, these warehouses may not do an excellent job of structuring, organizing, or querying the data.
On the other hand, if you have a warehouse that does an excellent job of structuring, organizing, or querying data, that warehouse may not be able to handle big data, and then as a result, the system falls apart when it comes to scalability.
Infrastructure may also pose a major problem with how you are storing your data. Some warehouses may require you to buy a lot of storage and servers, which is not the best option, but there are other warehouses that offer cloud storage. Even with some cloud-based warehouses, storage and processing are tightly coupled so the system has to be scaled in unison.
Over time, there have been different data warehouses that have been hitting the mark with certain pieces of the puzzle. But, solving all of the pieces is rare. This is why Snowflake did something different. Snowflake examined these different factors and built a data warehouse from the ground up. They have created a system that solves for a lot of problems that you see today with data warehousing.
So how did Snowflake do it? Snowflake engineered a tiered system that separates the storage of the data from the processing and querying of the data. It is also cloud native, which means the database runs on the cloud and started off as a cloud-based database. Snowflake uses a pay-as-you-go model as well, which doesn’t involve licensing plans or a need for software installation of any kind.
Snowflake solves the problem of having large amounts of data by scaling your storage independently from the actual processing of that data. You can also split the organization and processing of the data into different groups, which allows for it to handle a situation where some groups dump a lot of data vs. querying a lot of data or vice versa. Snowflake supports that whole mechanism with ease!
The ultimate benefit of a data warehouse like Snowflake is that it allows for a cloud-based option where you simply sign up for an account, and you are not going to be billed until you use it for the storage and query aspects. This decouples the infrastructure aspect and helps the company manage their cost structure because cost is based on actual usage of the system.
Some addition benefits regarding the performance of Snowflake include:
Say you are a mid-sized retailer and you want to start getting value out of your data. Since you may not have a huge infrastructure to store your data, you can simply sign up for Snowflake, start funneling your data into it, and then be able to analyze that data immediately as a result. Initially you may not know exactly what to analyze and how to get the best value out of your existing data, but as you grow and evolve your analytical capabilities, you can simply scale just that aspect. Over time, as you grow and have more data to analyze, you can simply add more of this data to Snowflake. The good thing is that you’ll only get charged for what you use.
Snowflake is a modern data warehouse that takes a fresh look at solving for a lot of issues that a traditional data warehouse may have. A lot of other warehouses may solve for specific issues, but Snowflake started from the ground up to solve for not just one piece of the puzzle, but for the entire puzzle itself. To learn more about what Snowflake can do for your data, check out this short introductory video.