Community Spring Cleaning week is here! Join your fellow Maveryx in digging through your old posts and marking comments on them as solved. Learn more here!

Analytics

News, events, thought leadership and more.
RamanK
Alteryx Alumni (Retired)

With the release of Alteryx Analytics 2018.2, we continue to help enterprises uncover hidden analytic possibilities and centralize the discovery of all their data sources.  Now, you may be asking yourself, “Yes, but what does that mean? And more importantly, what does it mean in relation to my use of Alteryx with Snowflake?” Well, let’s find out! This release builds upon our previously announced functionality for Alteryx and Snowflake with the debut of a bulk data connector and metadata loader for Snowflake.

 

New Snowflake Bulk Loader for Alteryx Designer 

With the 2018.2 release, we’ve introduced the ability to bulk load data into Snowflake. Why is this exciting? Because it means your workflows will run even faster than they do now when writing data from Alteryx into Snowflake! Instead of writing data row by row, we’re now moving data to Snowflake in chunks, greatly increasing performance.

 

This functionality can be accessed in two ways. The first is via the Output Tool in Alteryx Designer. There is now an explicit option to choose “Snowflake Bulk Loader” when connecting to the Output Tool:

 

image001.png

 

The second place to access the Snowflake Bulk Load functionality is In-DB. Now, not only can you increase the performance of your workflows by using In-DB tools, you can also utilize the new Snowflake bulk load functionality to increase performance even more. This option can be accessed via the Write tab of your Connect In-DB configuration:

 

image003.png

 

New Snowflake Metadata Loader for Alteryx Connect

We’ve also added a Snowflake Metadata Loader with 2018.2. Now you can harvest your Snowflake instance and populate Alteryx Connect with information about your Snowflake tables, views, procedures and columns – making it easy for data users to search and find analytic assets by name, description or tags. Once you find  what you need, easily review details and annotations or comments from others in the organization for tribal knowledge that may otherwise go un-leveraged. Even better, the harvesting of metadata from Snowflake can be scheduled, so users always have up-to-date documentation of their Snowflake instance for use in their analytics process.

 

image005.png

 

Next Steps

Experience the latest Alteryx + Snowflake buzz for yourself when you download Alteryx 2018.2.  Existing customers can upgrade to the latest version of Alteryx, or if you are not a customer yet, you can get started by downloading our 14-day free trial.

Raman Kaler
Sr. Manager, Alliance Marketing

Raman is responsible for alliance marketing at Alteryx, where she focuses on defining strategy and executing joint marketing programs with key strategic technolog partners. She currently focuses on alliance marketing efforts with, among others, Amazon Web Services, Microsoft, Salesforce, and Tableau. Raman has proven success in creating strategic marketing and business development programs that drive and support pipeline growth.

Raman is responsible for alliance marketing at Alteryx, where she focuses on defining strategy and executing joint marketing programs with key strategic technolog partners. She currently focuses on alliance marketing efforts with, among others, Amazon Web Services, Microsoft, Salesforce, and Tableau. Raman has proven success in creating strategic marketing and business development programs that drive and support pipeline growth.

Comments
DanielUpton
9 - Comet

Love Snowflake.  Good to hear of the new features.

danielkresina
9 - Comet
It appears this new bulk loading tool only works with AWS-based snowflake accounts. Are you planning to add a version that will support the new Azure-based snowflake accounts?
danielkresina
9 - Comet
I tried to test this bulk insert feature with a trial AWS-based Snowflake instance. The alteryx appears to successfully create the table in the database but then the output fails stating that the table does not exist. I tried several different output options (Create, Append, Drop, etc.) and I get the same result. Has anyone else had this issue? At first I wondered if it was due to the endpoint settings in the output connector, so I tried specifying the US-EAST-1 endpoint that I'm using for Snowflake and my s3 bucket, then it gave a different AWS error stating that it was unable to remove the files from the S3 bucket because you had to use the same endpoint. Leaving the endpoint setting as default seemed to be best, but now I run into this SQL error about the table not existing. Any help is appreciated!
ARich
Alteryx Alumni (Retired)

Hi @danielkresina,

 

We don't have plans to support bulk load for Azure-based Snowflake accounts at this time. Please add an Idea to the ideas forum for future consideration,

 

We do have plans to add bulk loading for Snowflake's built-in staging.

 

Best,

Alex 

fpinchon
8 - Asteroid
@danielkresina oh and make sure your ODBC connection to Snowflake has the role populated matching the user... this would explain the no database found error...
jason_scarlett
10 - Fireball
cmcclellan
13 - Pulsar

@ARich can you share the timeframe for the release of the internal staging piece please ?