In recent installment of Customer Service Dashboard post, our reader Salmon asked an interesting question,
I am struggling with data size with my dashboards…so many SQL data pulls and formulas to generate the Dashboard, the entire file is massive and sluggish. Perhaps a few tips from Chandoo Master for all us rookie dashboard designers regarding how to minimize file size and maximize calc speeds. #
Dan l & others chipped in and shared their ideas on speeding up Excel. But the topic is wide & has many solutions. So I am dedicating an entire week to discuss this. Welcome to Speedy Spreadsheet Week.
What happens in Speedy Spreadsheet Week?
This week, we will be writing articles explaining various techniques & ideas that you can use to speed up, optimize your Excel workbooks, dashboards & models. This is the posting schedule.
- 20th March: Speeding up Excel Formulas
- 21st March: Speeding up Excel Charts & Formatting
- 22nd March: Speeding up Excel VBA & Macros
- 23rd March: Speeding & Optimization Tips from Excel Experts
- 26th March: Speeding Tips submitted by our readers
Action Required: Tell us how you speed-up your spreadsheets?
Each of us have our own check list when it comes to Speeding up sluggish workbooks. One reason why I am keen to run this speedy spreadsheet week is to learn from you. So go ahead and share your spreadsheet speeding techniques. We will publish a collection of all your tips & ideas on 26th March so that all of us can benefit.
Use below form (or click here) to share your tips.
Thank you
Thank you in advance for contributing to the Speedy Spreadsheet Week at Chandoo.org. Watch out for spreadsheet speeding tips all this week.
One Response to “SQL vs. Power Query – The Ultimate Comparison”
Enjoyed your SQL / Power Query podcast (A LOT). I've used SQL a little longer than Chandoo. Power Query not so much.
Today I still use SQL & VBA for my "go to" applications. While I don't pull billions of rows, I do pull millions. I agree with Chandoo about Power Query (PQ) lack of performance. I've tried to benchmark PQ to SQL and I find that a well written SQL will work much faster. Like mentioned in the podcast, my similar conclusion is that SQL is doing the filtering on the server while PQ is pulling data into the local computer and then filtering the data. I've heard about PQ query folding but I still prefer SQL.
My typical excel application will use SQL to pull data from an Enterprise DB. I load data into Structured Tables and/or Excel Power Pivot (especially if there's lot of data).
I like to have a Control Worksheet to enter parameters, display error messages and have user buttons to execute VBA. I use VBA to build/edit parameters used in the SQL. Sometimes I use parameter-based SQL. Sometimes I create a custom SQL String in a hidden worksheet that I then pull into VBA code (these may build a string of comma separated values that's used with a SQL include). Another SQL trick I like to do is tag my data with a YY-MM, YY-QTR, or YY-Week field constructed form a Transaction Date.
In an application, I like to create a dashboard(s) that may contain hyperlinks that allow the end-user to drill into data. Sometimes the hyperlink will point to worksheet and sometimes to a supporting workbook. In some cases, I use a double click VBA Macro that will pull additional data and direct the user to a supplemental worksheet or pivot table.
In recent years I like Dynamic Formulas & Lambda Functions. I find this preferable to pivot tales and slicers. I like to use a Lambda in conjunction with a cube formula to pull data from a power pivot data model. I.E. a Lambda using a cube formula to aggregate Accounting Data by a general ledger account and financial period. Rather than present info in a power pivot table, you can use this combination to easily build financial reports in a format that's familiar to Accounting Professionals.
One thing that PQ does very well is consolidating data from separate files. In the old days this was always a pain.
I've found that using SQL can be very trying (even for someone with experience). It's largely an iterative process. Start simple then use Xlookup (old days Match/Index). Once you get the relationships correct you can then use SQL joins to construct a well behaved SQL statement.
Most professional enterprise systems offer a schema that's very valuable for constructing SQL statements. For any given enterprise system there's often a community of users that will share SQL. I.E. MS Great Plains was a great source (but I haven't used them in years).
Hope this long reply has value - keep up the good work.