From the course: Learning SnowflakeDB
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Compare cloud data warehouse solutions - Snowflake Tutorial
From the course: Learning SnowflakeDB
Compare cloud data warehouse solutions
- [Instructor] Let's define some terms as we're understanding the capabilities of Snowflake DB. So for a data warehouse and data lake. Data warehouse accepts structured data which is stored in tables. Again, the underlying storage is stored in columns which we'll look at in the storage section rather than row for aggregate purposes. The schema is used when you read the data rather than when you write it in. When you write it in, that would be for an operational OLTP, or transactional workload, which is not something that Snowflake DB is designed for. And it's designed for a huge volume of data. A data lake can work with unstructured data, usually files. It's stored in object storage. And in our case, 'cause we're cloud native, that would be buckets. Again, it's schema on read. And the idea here is massive volume. So where this comes into play is for customer scenarios that have terabytes and petabytes and even…
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