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IoT

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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Enterprise Digital Transformation

Big Data is likely to be a key element in the digital transformation taking hold in the enterprise, but there are still questions as to how it will influence future business processes and what steps to take now to ensure it provides optimal support in a fast-changing economy. Legacy IT is largely unprepared to meet the requirements of the new digital business: application cycle times measured in months, if not years; siloed infrastructure that prohibits organizations from viewing their data holistically; performance bottlenecks that impact end-user experience in a world that demands constant availability and response times; rigid architectures that force organizations to make forklift upgrades as requirements change; and traditional provisioning processes in which IT is often seen as a barrier rather than an enabler for the business. 

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