The data lake has come a long way.
It is a well-established design pattern and data architecture for profound applications in data warehousing, reporting, data science, and advanced analytics.
Over the years, users’ expectations, best practices, and business use cases for the data lake have evolved, as have the available data platforms upon which a data lake may be deployed. This evolution is forcing changes in how data lakes are designed, architected, and deployed.
This checklist by TDWI (Transforming Data With Intelligence) and sponsored by Snowflake covers:
- The many issues, design patterns, and best practices of data architectures with a focus on modernization
- Practical use cases—in analytics and elsewhere—that a well-constructed data lake architecture can support and nurture
- The types of data platforms and tools that commonly go into such architectures
- Why you should expect your data lake to evolve to the cloud
