Why is Snowflake Good for Data Engineers?

by Rockborne Consultant

11 May '22

If you’re working in the data industry, I’m sure you’ve heard of Snowflake. But for those of you who are relatively new to the data world, Snowflake is an upcoming cloud computing-based platform. It operates as a data warehouse built on top of Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), making it an ideal and easy tool to integrate within your company. Snowflake’s versatility with cloud platform integration is just one of the many reasons why it is a great tool for Data Engineering. Let’s delve into the benefits of Snowflake in detail.


As discussed before, Snowflake is relatively easy to implement. In fact, it doesn’t require any hardware configuration at all. Snowflake is offered as a Service (SaaS), meaning you can access it anywhere in the world, as long as you have an internet connection. It is designed to really harness the power of cloud computing technology – so no matter what cloud platform your company/clients use, you can easily adapt and partner it with Snowflake.


Furthermore, its advanced architecture and infinite scalability feature allow users to run huge volumes of queries. With Snowflake, you can manage workloads independently without affecting the system’s overall performance – in other words, you can multitask efficiently and meet those important deadlines! You can even import both structured and semi-structured data without the need for any transformations or conversions which saves a lot of time, allowing you to increase productivity elsewhere.


Snowflake is also equipped with advanced data sharing capabilities. It streamlines the process of sharing data amongst its users, so if you’re working on a large dataset with a team of data engineers, you can all work on different parts of the data simultaneously. You can even share your end result with clients via a reader account which allows clients to view your work but doesn’t grant permissions for them to change anything. Snowflake also supports automatic data loading, and it can easily scale along with any new requirements which makes it ideal for handling multiple operations on its own.


Last, but by no means least, Snowflake is designed to deliver constant services and shows little phasing, even when there are component or network failures. This is due to its advanced security measures which are influenced by SOC2 – a criteria for managing customer data based on five ‘trust service principles.’ The five principles are: Security, Availability, Processing Integrity, Confidentiality and Privacy – all of which are essential components to consider when working in the data industry.


Where to next? Find out about the security of Snowflake


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