Do you work with accounting or finance data? You are going to love, no scratch that, adore Power Query. It can save you precious time, make you look like a hero in-front of clients and keep you sane.
In this masterclass, learn all about Power Query for accountants (and other kinds of finance people) & 5 tips.
This is part of Power Mondays series, where every Monday we discuss Power BI / Power Query / Power Pivot.
Why Power Query?
Power Query is a data processing software. Using Power Query, you can create process driven programs (queries) to collect data, clean or reshape it, calculate things and publish final output as a table. Once you create a query in PQ, when the underlying data changes, you just refresh the query and brand new data will be fetched, cleaned and published for you.
For example, you can use Power Query to combine ledger files from various accounts to one master file with Power Query. No more VBA or manual copy pasting. Just one query and you are done.
In this lesson
This masterclass is broken in to 5 topics.
What is Power Query – Quick intro
We start by discussing what Power Query is, how to access it, understanding Power Query UI, how to enable formula bar and what various ribbons are.
Extracting data from bank statement
You will learn how to
- Connect to a file on computer
- Load bank transactions to a new Power Query connection
- Delete transactions of certain type
- How to categorize transactions by amount – small, medium or large txns
- How to duplicate a query
- Splitting transactions in to debits and credits
Combining monthly ledger data files to single table
In this section, learn how to
- Connect to a folder full of similar format files
- Set up sample query to teach Power Query how you want to merge files
- Combine the data and publish
- Create a pivot table report from the data
- Update the folder and refresh
- Look at modified pivot report
Reconciling Sent vs. Paid invoices
In this topic,
- What is reconciliation
- Loading tabular data in to Power Query
- Connecting two tables using “Merge queries” option
- Using left joins
- Removing duplicates
- Tagging invoices as either “Paid in full”, “Under paid”, “Over paid” or “Unpaid” using conditional column feature
- Publishing results
Live exchange rate tracker
In this section, learn how to:
- Connect to public websites to fetch currency data
- Use Reserve Bank New Zealand’s currency rate data as connection
- Reshaping data with “Transpose” and “Use headers as first row” options
- Removing un-necessary columns
- Publishing final exchange rate
Watch Power Query for Accountants – Masterclass
Please watch the lesson video here (or on Chandoo.org YouTube Channel)
Download Sample files for this lesson
Click here to download sample files for this lesson. The example file is crafted with love and care. Check out the queries by going to Power Query screens. Go thru the steps and enable formula bar to learn what is going on.
How to view / edit the queries?
- Go to Data Ribbon
- Click on “Show Queries”
- Right click on any query and “Edit”
How to refresh the queries?
- Go to Data Ribbon
- Click on “Refresh All”
- Done.
I get an error when refreshing…
- The queries are mapped to local folder files
- So you need to re-map them to folders on your computer
- Edit the query with error (see steps above)
- Go to Source step
- Click on gear icon
- Change file address as per your computer
- Save & load the query
AND(You=”Accountant”, Use=”Power Query”)? Let’s talk
Are you an accountant (or finance pro) and use PQ often? Please share your tips, thoughts and ideas in comments section so I can learn from you. Getting on the Power Query bandwagon? Check out the intro to PQ and this lesson to get going. Post your wins in the comments.
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.