The Data Warrior

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Archive for the tag “#SnowflakeDB”

Okta SSO with Snowflake 

Ever wonder how to secure a cloud data warehouse? Well, Vlad from EA (Entertainment Arts) has produced an entire blog just about using Snowflake. This is the 1st in a series he wrote with detailed instructions on how to set up SSO on Snowflake for various tools, including Tableau. Check it out:

Once you made a decision (smart one!) to place you data warehousing and analytics activity onto Snowflake platform the next question would be how to make your data secure. Snowflake is really good …

Read the rest here: Okta SSO with Snowflake – Data Warehousing and Business Intelligence

Thanks Vlad!

Enjoy all!

Kent

The Data Warrior

Chief Technical Evangelist, Snowflake Computing

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TPC-DS at 100TB and 10TB Scale Now Available in Snowflake’s Samples

Here is another great announcement we made at Snowflake that I missed sending out to everyone. This is really cool to have a full set of data to test on you own. If you want to give it a try, sign up for a FREE Snowflake account.

Get you TPC-DS data here

We are happy to announce that a full 100 TB version of TPC-DS data, along with samples of all the benchmark’s 99 queries, are available now to all Snowflake customers for exploration and testing. We also provide a 10TB version if you are interested in smaller scale testing.The STORE_SALES sub-schema from the TPC-DS BenchmarkSource: TPC Benchmark™ DS Specification

For all the details, continue reading here: TPC-DS at 100TB and 10TB Scale Now Available in Snowflake’s Samples

Enjoy!

Kent

New Snowflake features released in Q2’17 

I have been busy lately preparing and delivering quite a few talks so got a bit behind on my blogging and reporting. So in an effort to catch up a bit, here are some details on developments at Snowflake:

Q2 1017 Features

It has been an incredible few months at Snowflake. Along with the introduction of self-service and numerous other features added in the last quarter, we have witnessed:

  • Our customer base has grown exponentially with large numbers of applications in full production.
  • Billions of analytical jobs successfully executed this year alone, with petabytes of data stored in Snowflake today, and without a single failed deployment to-date.
  • A strong interest in pushing the boundaries for data warehousing even further by allowing everyone in organizations to share, access and analyze data.

Continuing to engage closely with our customers during this rapid growth period, we rolled out key new product capabilities throughout the second quarter.

Get the rest of the details here: New Snowflake features released in Q2’17

Cheers

Kent

New Snowflake features released in Q1’17

Snowflake just keeps getting better & better!

We recently celebrated an important milestone in reaching 500+ customers since Snowflake became generally available in June 2015. As companies of all sizes increasingly adopt Snowflake, we wanted to look back and provide an overview of the major new Snowflake features we released during Q1 of this year, and highlight the value these features provide for our customers.

This post provides an overview of the major new Snowflake features we released during Q1 of this year, and highlights the main value they provide.

Read the rest: New Snowflake features released in Q1’17

#LoveMyJob

Kent

The Data Warrior

Snowflake and Spark, Part 2: Pushing Spark Query Processing to Snowflake

Here is the latest post on using Spark and the Snowflake cloud-native data warehouse.

Welcome to the second post in our ongoing blog series describing Snowflake’s integration with Spark. In Part 1, we discussed the value of using Spark and Snowflake together to power an integrated data processing platform, with a particular focus on ETL scenarios.

In this post, we change perspective and focus on performing some of the more resource-intensive processing in Snowflake instead of Spark, which results in significant performance improvements. As part of this, we walk you through the details of Snowflake’s ability to push query processing down from Spark into Snowflake. We also touch on how this pushdown can help you transition from a traditional ETL process to a more flexible and powerful ELT model.

Read the rest: Snowflake and Spark, Part 2: Pushing Spark Query Processing to Snowflake

Enjoy!

Kent

The Data Warrior

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