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On the left side, we have a veteran warrior with 37 years of data battle scars and redundant six pack. They call him SQL.
On the right side, there is a young challenger with transformative powers and “never say undo” attitude. He goes by the moniker Power Query.
Who is going to win this battle?!?
I have been using SQL for 25 years and Power Query since it came out in early 2012. And in this article, let me share my views on how SQL compares with Power Query. If you prefer to listen, check out the podcast episode – SQL vs. Power Query.
Listen: SQL vs. Power Query Podcast Episode
SQL vs. Power Query - The Comparison
SQL
Power Query
What can you do?
All CRUD operations (Create, Read, Update, Delete)
Only Read the data
What kind of data?
Usually single source from a database or warehouse
(ex: SQL Server)
Can access data from anywhere and combine data from multiple sources too.
How do you use it?
You need to “WRITE” queries to use SQL.
You “BUILD” Power Queries using the UI buttons and menu options.
Where can you use it?
Works almost universally. You can use SQL with most database systems and programming languages.
Only with Microsoft stack of products, primarily with Power BI, Excel and Fabric.
Who can use it?
By default, you need permissions / special software to use SQL.
Almost anyone can use Power Query as it comes packaged with Excel and Power BI.
How fast is it?
Built for performance and scalability. You can use SQL to access data quite efficiently.
Can become slow and tedious as your data grows.
Resources for Learning SQL
Resources for Learning Power Query
What do you think?
Have you used both or either of these technologies? What do you think? Leave a comment with your thoughts.
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.