Recently, I wrote a tutorial on tax burden in USA chart.
Jared, One of our readers liked this chart very much. Jared works as a workforce scheduler and has data similar to our chart. So he applied the same technique to analyze monthly service levels for last 7 years & sent me the file so that I can share it with all of you.
Monthly service levels in last 7 years – Demo:
First take a look at the demo of Jared’s chart.
Recipe of this chart
This chart construction is similar to our Tax burden chart. Only addition is the cool scroll bar at bottom to see any month’s service level across years.
How does the scroll bar work?
- If you have never used scroll bar or any other form controls, read our introduction to form controls page.
- The chart has one extra series that shows selected month’s value and a bunch of #N/As.
- Scroll bar is setup to have minimum 1, maximum 12 and is linked to a cell.
- Based on scroll-bar selection, we turn on one of the months and make the rest of values NA()
- Using a simple IF formula
- For this extra series, Jared added 100% negative error bar so that a nice drop line is shown when you select a month.
That is all.
Download Jared’s Example and get inspired
Click here to download this workbook. Play with it to learn more. Use this idea in your work and impress someone. Become awesome.
Do you like this example? Say thanks to Jared…
I really loved Jared’s creativity and simple solution. Not to mention his kindness to share this with me and all of you. This shows that by using easy features like scroll bars, slicers, regular charts we can create something that is stunning, meaningful and powerful – right inside Excel.
What about you? Do you like this example? If you learned something new, say thanks to Jared for sharing this with us.
PS: If you want to share your story of how you use Excel to do something awesome, please email me. I am eager to learn from your examples and share your stories on Chandoo.org.
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.