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Connect Snowflake to Excel in Minutes

Data “Self-Serve” is a buzzword that’s managed to stick around for a long time without a solution. However, I’m convinced that we can get partway there with simple data products rooted in familiar tools. One ubiquitious tool? Excel. Nearly everyone uses spreadsheets or similar productivity tools at work.

That leads me to meet stakeholders where they’re at: in Excel. And modern data warehouses like Snowflake make it really easy to do so. It’s an easy win if you’ve invested in Analytics Engineering to create clean datasets in your database. Let’s bring those datasets to your users.

Here’s how to connect Snowflake into Excel and enable live connections pivot tables in minutes. These are instructions for Windows specifically.

Step by Step Instructions

(1) Install the ODBC Driver

Click on the “Help” button in the Snowflake UI, go to “Download…” and select “ODBC Driver” and “Snowflake Repository”. Install from the file that downloads.

(2) Configure ODBC Driver

Go to your start menu and type in “ODBC” and click on ODBC Data Sources (64 bit)

Under User DSN, select Add…

Select SnowflakeDSIIDriver from the menu

Fill in the boxes as follows – though your individual situation may vary. My example uses SSO when an organization doesn’t allow direct usernames/passwords for Snowflake. Lots of options here and Snowflake has full documentation of options here.

Note: I found that lots of databases & schemas are available even after choosing some here. Not sure the full limitations, so you can play with options. I put all options in for the primary database I cared about and it worked fine.

Click on Test… to confirm it worked. Here’s the dialog if it did:

(3) Connect to database in Excel

Open Excel and go to the Data tab, click on Get Data and choose From Other Sources and pick From ODBC

From the window that pops up, pick the Snowflake connection and select OK

If successful, you’ll see a window with a dropdown showing your available databases. Use that dropdown to pick the database you want.

IMPORTANT: There is an easy way to load data directly into a Pivot Table at this point (thanks Jacob for this tip!) which will save you and teams time.

Once you select the database / schema / table you want, go to that “Load” button on the bottom and click the little down arrow next to it. Choose “Load to…”

The next menu that pops up will give you various options – pick the second one down saying PivotTable Report

DONE. You’re there. The data is now connected live to Snowflake and is available to pivot. I used Snowflake’s sample “Weather” table which I just learned has basically nothing in it, but that’s besides the point.

Parting notes

There are a couple interesting tidbits to pass both to your stakeholders as well as anyone concerned about Snowflake compute cost & data security.

(1) Stakeholders can refresh data live from Snowflake any time. By right-clicking the pivot table and selecting “Refresh”. No more stakeholders asking you for the latest data – they can just get it anytime.

(2) Data is cached on the local machine, reducing compute costs & keeping things snappy for stakeholders. This satisfies worries from both stakeholders on performance (it’s REALLY snappy, even for huge tables) as well as those concerned on cost (compute only happens on refresh).

That’s it! Just a few installations and clicks and you’ve connected Snowflake live into Excel for any stakeholder. Happy self-serving.

analytics bi data viz

Write Code Last – 4 steps to better dashboards

I gave a talk last week about “Data to Dashboard” and I wanted to share it here, too. There is a lot of discussion in the analytics space about dashboards and how to make them look good but less about how to get to that point. This is my take on the subject – I hope you enjoy it.

Toronto Data Workshop – 6/18/2021
bi businessintelligence everythinganalytics

Medium Data: MS edition

This video is for your data that is too big for an excel spreadsheet and too small for a data warehouse. I like to refer to this as “Medium Data”.

I can think of many times I needed this during my career. Typically, the “medium data” scenarios were related to snapshotting historical data weekly and showing changes in trends over time. One good trick I learned in one of my first jobs was to snapshot my CRM order book every week and save it in a CSV format. Eventually, that got too large for my meager tools, and I started aggregating, losing data, or other hacks (i.e., multiple excel files). Linking excel files together was basically enough to motivate me to learn SQL. With Azure, you can easily scale into the next size of data and keep your analytics rolling. Check the video below for a 15 min walk through.

Going from CSV to SQL in 16 minutes

I’ve just shown the basics – but there are some awesome articles out there that can go more in-depth, including some great automation.

The core tutorial in this video can be found here:

To really amp it up with automatic import, check out this: