Snowflake AI Data Cloud

As a Data Engineer, I primarily use Snowflake  for data warehousing tasks as well as ETL processing, and sometimes I also use it for data sharing. I personally find Snowflake  better than other tools because one of the biggest benefits is how compute and storage are separated in it, allowing different teams to run workloads independently without affecting each other's performance. In that way, I find Snowflake more useful than other tools.

For example, I am part of an ETL team, which is transitioning from Informatica to GCP  B-Cloud, so there are a lot of transitioning and tech remediation happening presently in our project. We use Snowflake to verify whether the data has been loaded and shared with the business users daily through Informatica, checking how much data is shared. We also verify this using GCP  to see how much data is sent to the users, allowing us to determine whether the transitioning is happening perfectly or if we need to add any more constraints. Additionally, for maintaining data warehouse tasks in our restaurant project, we receive data continuously and must produce sales reports for the business users at the end of the day by the EOD flag, utilizing data warehousing steps to store a huge amount of data from the past 10 to 15 years of sales data, so we use Snowflake for that.

My main use case is for data warehousing, ETL process, and data sharing; these are the main tasks where we use Snowflake.

One of the best features I appreciate is how the computing and storage are separate in Snowflake, so that in our project with multiple teams, around 15 to 20 teams, all of them can use Snowflake without affecting each other's work due to the separation of compute and storage. Another key feature is scaling and performance optimization; based on the amount of data we receive, we can easily scale Snowflake without requiring any special requests to be raised to the team. For instance, during weekdays, the data would be less compared to weekends, so we reduce the storage somewhat during weekdays, saving us a huge amount of money.

This separation of compute and storage allows us to scale compute resources independently based on our requirements while keeping storage costs negligibly low. The automatic scaling and performance optimization are very important for any data engineering tool, and Snowflake offers this, allowing it to scale down when the amount of data is less and to automatically scale up when the data is high. Additionally, I appreciate the special features such as Time Travel and secure data sharing.

Since we are using Snowflake, it has improved the speed and reliability of our analytics processes, which is key to any data engineering or data warehousing project. Prior to using this cloud data warehouse, reporting jobs often competed for resources, causing delays and sometimes making business users wait for more than hours to receive data during peak times. With Snowflake, different teams can separately use the same data without affecting one another, and the data sharing has become more user-friendly. The performance tuning and scalability have positively impacted our organization as well.

Before using Snowflake, business users received data during peak hours at around 9:00 p.m. to 10:00 p.m., causing a three to four hours time wastage, but since we transitioned to Snowflake, that time is easily utilized for other tasks. Now, during both peak hours and normal days, data is available to business users daily at 6:00 p.m., making that three to four hours available for analytics, helping them make better business decisions.

One main area for improvement in Snowflake is cost visibility and optimization; while it's flexible and scalable, costs can increase quickly if warehouses are left running unnecessarily or workloads are not monitored carefully, raising the costs of the tool. The automatic scaling should be more optimized to work well with varying data levels. Another improvement could focus on recommendation capabilities and integrating an AI tool for better user onboarding without extensive documentation.

All the performance is generally excellent, and we have never experienced any crashes. However, query optimization could use improvement, and adding built-in guidance for workload management would be beneficial. If Snowflake integrates with an AI tool, new users can navigate the tool more easily by prompting the AI.

I have been using Snowflake for the past one year.

All the performance is generally excellent, and we have never experienced any crashes.

The automatic scaling should be more optimized to work well with varying data levels.

Anyone with prior knowledge in SQL and data engineering can easily use Snowflake. To understand the tool better, you can go through the documentation provided on Snowflake's website or use tutorials on YouTube before utilizing the tool. The user interface is very user-friendly, making it easy for new users to find it useful. I would rate this product an 8 out of 10.