Snowflake Data Cloud Summit 2020

In the middle of November 2020 there was Snowflake data cloud summit conference. Naturally, it was virtual this year. There was 40+ sessions divided into several tracks covering Migration into Snowflake, Modernization of Data lake, Analytics and ML track, Data Apps Track, Industry solution spotlight, bunch of sessions with Snowflake data heroes about mobilizing your data and last but not least Keynote of the day and couple more „headline“ sessions. All in all it was pretty packed day with lot of interesting sessions.

In this post I would like to provide my summary and view on the recent Snowflake announcements, general feeling how i see the platform and tips for really cool sessions which I liked the most.

I haven’t seen all the sessions yet and probably i won’t see them. I’ve just covered what has been interesting for me — migrations, data lakes, ML, success stories. All in all i have probably seen around 70% of the available content. Let’s start with major announcements and new features.

Announcements

Snowpark

I can imagine this will help many projects to simplified their architecture or use techniques which they are already familiar with. All of that can be done in single platform. Apart from ML use cases I think this will be useful for many others like:

  • Data quality — imagine running your Deequ code directly from Snowflake
  • Data Lakes and Data Apps — build more complex data pipelines directly in platform
  • AI and ML — train and run your models directly from Snowflake

Data governance features — Tagging and Row Level Security

Tagging

Row level security

Other data governance features

Unstructured data support

Performance improvements

Search optimization service

Query acceleration service

You can find all the announcements with more details in following Snowflake post 👉🏻 Data Cloud Summit 2020 Announcements

Worth to check sessions

Migrating Zabka to Snowflake

Continuous Data Pipelines: Foundations and Effective Implementation at Convoy

Building Extensible Data Pipelines with Snowflake

Building a scalable data lake using Amazon S3 and Snowflake

Moving to and Living with Snowflake

Streamlining Data Science with Snowflake

Summary

Lead data engineer @TietoEVRY. Currently obsessed by cloud technologies and solutions in relation to data & analytics. ☁️ ❄️