Recap and best practices for data product delivery
1. Recap and best practices for data product delivery
You learned several ways of doing data delivery in this module. Let's recap what we covered. High-quality, transformed datasets may oftentimes be the final data product that you're delivering, and the Snowflake Marketplace can help you go beyond sharing those objects in your account to sharing that data with users in Snowflake's Data Cloud. You also learned that data apps are easy to build and share, either directly with other users in your Snowflake account using Streamlit in Snowflake, or with other users in Snowflake's Data Cloud using Snowflake's native applications. How your pipeline delivers your final data product will depend heavily on your use case. For example, your approach may vary based on things like security requirements, consumer personas, and more. What's great is how flexible Snowflake can be in aiding your delivery, from sharing data products within accounts, to sharing them in the Data Cloud and even beyond. By this point in the course, we've built end-to-end data pipelines that can take raw data and produce insights and deliver them via data products. But it wouldn't feel quite right if we stopped here. Remember, we want to learn how to build continuous end-to-end data pipelines so that they can run at scale. So join me in the next module to learn about how to level up your data pipelines with automation.2. Let's practice!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.