The Origin of Snowflake. Simply load and query data

Simply load and query data


  • Instantly Clone Database (eg. Q&A and DEV)
  • Secure Data Share — Create, as a provider, a secure view on your data for consumers to select data from. Therefore Snowflakes announcement about the Data Exchange.
  • Severless capabilities like SnowPipe
  • Micro-Partitioning — Automatically created at runtime (background re-clustering service). Small, Columnar, Structured/semi-Structured, Partition Map Index ( find that partition relevant to your query). Challenges; Blob Storage is immutable and performance

The Snowflake Approach

  • infrastructure (setup, upgrades, patching, availability, backups, etc.)
  • physical design (index, partitioning, etc)
  • query tuning (statistics, workload management, etc.).

What’s next?

  • Global — One single system connected for the whole world. Which means, one virtual datacenter, cross cloud and cross area. No boundries.
  • All the data — Including non-structured data and all types of access
  • Real-Time — The time from when the data is born to the time you can see the data in Snowflake. End-to-End latency. As little as possible.
  • Beyond SQL — To overcome the challenges SQL cannot solve. Having an extensible framework.
  • Data Services — Data Sharing + add value. Data enrichment and potentially share back.



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Daan Bakboord

Daan Bakboord


Cloud ☁️ Data & Analytics 📊 Engineer @ DaAnalytics | Manager Data & Analytics @ Pong | Snowflake ❄️ Data Superhero | Modern Cloud ☁️ Data Stack enthusiast