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SnowFlake

Snowflake for Data Science

Anyone working on a cloud-friendly data science, data engineering, or data warehousing team has surely heard the name Snowflake come up over the past couple of years.

For those that haven’t, Snowflake is a relatively new database solution that is majorly innovative in some ways and yet simultaneously familiar in the ways that matter.

The product is essentially a SaaS database built with cloud-native features that we 21st century data enthusiasts (fine… nerds) have come to expect from off-the-shelf products. Some of these features include:

  • the ability to quickly and automatically scale computing power — responding to highly variable workloads in a way that enables high velocity data to flow in, unimpeded by ingestion bottlenecks
  • the separation of storage and compute, both technically and on billing statements — making cold data retention much more cost-effective
  • the ability to segregate and securely share chunks of data — reducing the amount of maintenance and management that data ops teams need to invest in shared data assets
  • automatic query and data optimization — this one speaks for itself!

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