Power Query and the Abyss

I stared into the abyss of this business challenge. The old method consisted of 200+ odd ball queries in an Access database. No documentation. You know how it happened. Years ago some hack fixed a small problem with a few queries when the business challenge was small. The business grew in size and complexity, more hacks by other hackers and now: 200+ odd ball queries limp along doing a barely adequate job of what is now a potentially costly business process. By the way the old process took about 5 man days and was fraught with error. Auditors nightmare & a beast to support. There had to be a better way.

My gut feel was that Power Query had potential to “help” but I can honestly say I had no clear vision of the solution when I dove in. ***Spoiler alert*** Power Query not only helped, it turned out to be THE solution. Such a slick solution I felt motivated to blog about it. I’ll dumb down the actual story, but to appreciate the “power” in Power Query one must appreciate the challenge.

Think of a large distributor of hard goods that had negotiated special rebate terms with its best customers. CustomerA gets 8% rebate on hand tools purchased in the Midwest, CustomerB gets 7% rebate on all products purchased in the West, CustomerB also gets 6% rebate on Plumbing parts purchased in the Southeast which were manufactured by CompanyZ… and the list goes on. All these unique terms are captured & updated monthly by the Contracts department in a single spreadsheet. Of course this is a sacred spreadsheet that can only be viewed – thus these terms must be extracted “as is”. Here is a dumbed down sample of the controlling table “RebateMaster” found in the spreadsheet:Rebate Master Table Sample

The more I looked at this table the darker the abyss became. How to apply these rebate rules to the Sales data to derive the records that meet this crazy criteria? I’ll skip the details of the dead ends explored. The ray of hope started to shine when I thought of this as more of a “layered filtering” or “funnel” challenge. Looking at the columns from left to right, each column further filters the overall sales dataset by some criteria. If for every row in this table I could start with all the sales data and filter by [Customer], then filter that result by [Sales Region], then filter that result by [Product Group], then [Product List], then [Manufacturer]… the result would be all the sales records that meet the criteria for that rebate record. Eureka!!! That’s the data we seek!!!

How can Power Query do this “funnel”? With a paradigm shift – well it was a paradigm shift for me. My experience with Power Query to date was limited to traditional data cleansing and shaping at the detail record level. This challenge was far from traditional. My paradigm shift: Stay at the rebate table level and process whole tables worth of Sales data with M code. This is really much easier than it sounds.

Following best practices recommended by Ken Puls I knew to create a connection only queries to my data sources. For brevity… trust me that I created

  • srcRebateMaster_tblCustomers – table of customers that get rebates
  • srcRebateMaster_tblMfg – table of manufacturers that participate in the rebates
  • srcSales – table of Sales data pulled from SQL server. Since this query uses some Ken Puls’ trickery (but a bit off topic) I include it below

Time to bring in the Rebate table shown above. No tricks here, just pull it in like any ole Excel table. Now the paradigm shift. Add a custom column [SalesThisCustMfg] that filters srcSales by [Customer] & [Manufacturer]. Then add a custom column [SalesThisRegion] that filters [SalesThisCustMfg] by [Sales Region]. Likewise for [SalesThisProductGrp]:Power Query with Funnel Filters

Later I will show the M code for these Added Columns. First a little refresher on the Power Query interface. If I were to click on “Table” circled in red I would see the sales records for CustomerA. If instead I were to click on the funky arrows circled in blue I would see the sales records for all the customers in the table. Now that is powerful!!! Love this feature!!! This is very handy place to prove out the formulas and troubleshoot issues ongoing. Thus I will not add any more steps to this query, but rather save it as is (connection only) so I can reference it for the final processing steps in another query and also have it readily available for debugging down the road.

To continue, from the list of queries, right click on the query tblRebateMaster and select “Reference”. This creates a new query where the source is defined as tblRebateMaster and we are right where we left off. Good news is most of the work is done. The quick finish is to remove all but [Rebate%] and [SalesThisProdList] (remember it was the last filter applied in our funnel), then click those funky arrows on [SalesThisProdList] to get the result set of Sales records we were after. Add another Column which multiplies [Rebate%] by [Sales Amount] and you have now calculated the [Rebate Amount]. Close and load this to your data model and pivot away on your newly defined Rebate data.

Bask in the glory of replacing 200+ SQL queries!!! Now those folks in Contracts can add/remove customers as needed. Yes, I might need to update some of the “funnel filters” if a rouge salesman negotiates a completely new rebate term – but with Power Query I’m not scared…

Oh yeah, there is that matter of the M code that does the “funnel filters”. Here tis:

[SalesThisCustMfg] – needs to filter srcSales by [Customer] and [Manufacturer]. Assumes srcSales fields of [Cust] and [Mfg]. Note – There is a limitation that the field [Customer] cannot be referenced inside Table.SelectRows(). Solution is to first assign the value of [Customer] to the variable ThisCustomer, which CAN BE referenced inside the function. Same for [Manufacturer].SalesThisCustMfg M Code

[SalesThisRegion] – needs to filter [SalesThisCustMfg] by [Sales Region]. Assumes srcSales field of [Sales_Region]SalesThisRegion M Code

[SalesThisProdGrp] – needs to filter [SalesThisRegion] by [Product Group]. Assumes srcSales field of [Prod_Grp]SalesThisProdGrp M Code

srcSales: Instead of just pointing the query at the whole Sales table, for performance I pulled only the Sales records for the Customers and Manufacturers that participate in the rebate program.  Here is the M code (paraphrased):

FiscalStart = fnGetParameter(“FiscalStart”),
FiscalEnd = fnGetParameter(“FiscalEnd”),
CustList = Table.Column(srcRebateMaster_tblCustomers,”Customer Number”),
CustListSQL = “‘” & List.Accumulate(CustList, “”, (state, current) => if state = “” then current else state & “‘,'” & current) & “‘”,
MfgList = Table.Column(srcRebateMaster_tblMfg,”Manufacturer”),
MfgListSQL = “‘” & List.Accumulate(MfgList, “”, (state, current) => if state = “” then current else state & “‘,'” & current) & “‘”,
SQL=”Select * from Sales
where Customer in (” & CustListSQL & “)
and Manufacturer in (” & MfgListSQL & “)
and Fiscal_Period between ” & FiscalStart & ” and ” & FiscalEnd,
Source = Sql.Database(“server1.myco.com”, “Sales”, [Query=SQL]),
srcSales Notes:
  • FiscalStart/FiscalEnd are pulled from a parameter table like Ken blogged
  • CustList/MfgList are tables in the Excel file referenced by srcRebateMaster
  • CustListSQL/MfgListSQL – same data as above, but reformatted from a table to a delimited string compatible with SQL
  • SQL – string of executable SQL built using above parms, which is then used in Sql.Database()

As a final note I will add that I was concerned about performance during development. In my real rebate dataset I have roughly 10 “funnel filters”, hundreds of customers that get rebates, and 10 million Sales records of which 10% get some kind of rebate. I toyed a bit with Table.Buffer() and I pondered query folding… Truth is performance never was a problem. My data model loads in less than 5 minutes which serves the need well.

I hope you find this info useful. Perhaps next time you find yourself staring into the abyss, Power Query will be your ray of hope…


PowerPivot – CSVs “The SQL Way”

PowerPivot – CSVs “The SQL Way”

Despite all the technology at our disposal today, when it comes to exchanging data between systems – text files are still the king. The venerable CSV file format is still the least common denominator in the import/export of data. The good news is the Microsoft Excel/PowerPivot/PowerBI stack is a very efficient tool for crunching CSV data into state of the art Charts/Tables/KPIs/Dashboards/etc. However, real world challenges (like dirty data) often turn what should be a straight forward process into a series of speed bumps, hurdles and sometimes dead ends. Described here are some of the tricks/techniques I use on a daily basis to process CSV data with the Microsoft BI tools.

There are many ways to process CSV files. If you are looking to import just a few simple files on an ad-hoc basis there are several methods more simple than The SQL Way I share here. However, if you are tasked with building a process that is robust, repeatable and easily supported… then read on…

Why I do it The SQL Way:

  • Dynamic! With SQL in my ETL path I can quickly make changes to meet the dynamic demands of my data consumers
  • Documentation! Sometimes I push out very important data sets (think litigation) and by providing the SQL used in the ETL it goes a long way to explain where the data came from and how it was compiled. (Same for DAX by the way)
  • Performance! By doing some of the ETL work in the SQL on import, I can keep my tables smaller, perhaps summarized or preprocessed. Keeps the load time and memory requirements down for my data consumers.


  • You have little to no control over the format of the source data. It is given to you in CSV format (which at best is loosely defined standard often misused). It is given to you periodically and your goal is to import the data into PowerPivot so it may be combined with other data and visualized in Pivots and Charts.
  • The project is not well defined, thus you can expect many changes in source data and desired visualizations. Somebody is going to move your cheese. Count on it.
  • Source data files may need to be combined to form a single table in PowerPivot (such as sales from different regions, different reporting periods, etc…)
  • Source data files may need to be joined or filtered to form a single table in PowerPivot (such as importing sales data for only those customers that exist in a separate file)


  • Build a Schema.ini file that describes the CSV source data
  • Define PowerPivot Connection to the source CSV files
  • Create the SQL necessary to extract/filter/transform the source data into a PowerPivot data model
  • Create DAX defined columns and measures as needed (not described here)
  • Create additional dimensions as needed (not described here)
  • Create desired visualizations in the way of Pivot Tables and Charts (not described here)

Build a Schema.ini file:

The bad news is the Schema.ini file format is NOT well defined. I found a few gems on Internet forums that helped me clear hurdles, but I never found a comprehensive resource. The good news is Excel provides a decent Wizard that does most of the heavy lifting. Follow along closely as these steps are not all that obvious.

  1. Open a new spreadsheet in Excel (2010 version for this example). On the Data Ribbon select to Get External Data, From Other Sources, From Microsoft Query
  2. From the Choose Data Source panel, select <New Data Source>
  3. From the Create New Data Source pane, give a bogus name (really does not matter), Select the driver “Microsoft Text Driver(*.txt,*.csv)”, click the Connect button
  4. From the ODBC Text Setup pane select the proper directory for your source CSV file, click the Options button
  5. Still from the ODBC Text Setup pane click the Define Format button
  6. From the Define Text Format pane select the source CSV file, and set the Column Name Header /  Format: / Delimiter:  fields to match your source CSV file
  7. Still from the Define Text Format Pane you now get to enjoy the magic… click the Guess button and then the OK button. Ignore any error message you get at this point and back all the way out of the Wizard and even close the spreadsheet – for at this point you have accomplished your goal… you have created the Schema.ini file!!!
  8. Edit the schema.ini file with your favorite text editor and make adjustments per notes below.

Schema.ini Notes:

  • the schema.ini file must reside in the same directory as the source CSV file
  • if you use the Wizard to create the schema.ini file, it will contain entries for all files found in the directory. To keep the schema.ini clean, I typically keep each report type in its own directory. c:\data\report1 has a schema.ini that defines report1.csv; c:\data\report2 has a schema.ini that defines report2.csv; etc…
  • As a general rule, I define all columns in the schema.ini as “Char Width 255” & let PowerPivot convert the fields to the desired data type. All hail PowerPivot and its ability to change data types on the fly!!!
  • Dates in source CSV data can be problematic. Most times you can define in Schema.ini the date fields as “Char Width 255” and in PowerPivot as “Date”. However depending on the format of the source data you may need to specify something like DateTimeFormat=”d-mmm-yy hh:nn:ss” in the schema.ini file in order to help PowerPivot with the transform. One limitation to note – schema.ini assumes all dates in a given source file are of the same format. If that is not true in your case, define in schema.ini the date fields at “Char Width 255” and in PowerPivot as “Text”, then sharpen up your DAX skills to create a defined column of type “Date”.
  • PowerPivot can handle “dirty” column names most of the time. It will often just drop the offending character. However I found on occasion PowerPivot will lock up on the import if the column name is too weird. In such cases I can often edit the schema.ini file and remove the offending character from the column definition.
  • If the source CSV data contains control characters (other than Carriage Return, Line Feed, End of File, etc) the results can be unpredictable. I have had cases where a text file would only partially load (with no error message thrown) because the 026 control character was somehow embedded in a user input text field. Thus I typically scrub the CSV file before import to remove extraneous junk. On an ad-hoc basis I use NotePad++ to search/replace. For more complex or systematic processes I depend on a PowerShell script that cleanses the files before import. I also use PowerShell to perform many other ETL functions, but that is a whole other topic 🙂
  • Depending on how your source CSV file treats text delimiters, you may find this schema.ini setting to be handy: textdelimiter=none. This is another of those under-documented settings that has saved me much grief.

Define the PowerPivot Connection & Import first Report:

  1. From the PowerPivot Window (Excel 2010 64 bit for this example), Home Ribbon, select to Get External Data From Other Sources
  2. From the Table Import Wizard panel select “Others (OLEDB/ODBC)”
  3. From the Connection String panel, define a useful name for the connection (such as “CSV Files via SQL”) and click the Build button
  4. From the Data Link Properties panel, Provider tab, select “Microsoft OLE DB Provider for ODBC Drivers” and click the Next button
  5. Still from the Data Link Properties panel, Connection tab, select “Use Connection String” and click the related Build button
  6. From the Select Data Source panel, give a DSN Name such as “report64” and click the New button
  7. From the Create New Data Source panel select the “Microsoft Access Text Driver (*.txt, *.csv) and click the Next button
  8. From the Create New Data Source panel browse to the desired location such as c:\data and give a suitable file name such as Report64 and click Next and then click Finish
  9. From the ODBC Text Setup panel select OK (assuming you have already created the schema.ini file described above)
  10. From the Data Link Properties panel (again) select the OK button
  11. From the Table Import Wizard panel (again) verify the connection string looks something like the following and click Next:
    Provider=MSDASQL.1;Persist Security Info=False;Extended Properties=”DBQ=C:\Data\Report1;DefaultDir=C:\Data\Report1;Driver={Microsoft Access Text Driver(*.txt,*.csv)}; DriverId=27; Extensions=txt,csv,tab,asc; FIL=text; FILEDSN=C:\data\report64.dsn; MaxBufferSize=2048; MaxScanRows=25; PageTimeout=5; SafeTransactions=0;Threads=3;UID=admin;UserCommitSync=Yes;”
  12. From the How to Choose the Import Data panel, select to Write a Query and click the Next button.
  13. From the Specify a SQL Query panel, define a useful name and the following as a SQL Statement and click the Finish button:
    select * from c:\data\report1\report1.txt
  14. Data should now be imported to a PowerPivot table. Check/set the Data Type for each column & refresh to ensure PowerPivot can handle the desired transforms.

Import subsequent CSV reports:

  1. From the PowerPivot Window, Design Ribbon, select Existing Connections
  2. From the Existing Connections panel, select to Open the “CSV Files Via SQL” connection just created.
  3. As above, specify the desired table name and SQL query for the next report and click Finish
  4. As above check/set the column data types and refresh…

PowerPivot Connection & Import Notes:

  • Only 1 PowerPivot connection needs to be defined. You can import all your CSV tables via this connection assuming your SQL query fully specifies the file path. Note that you can use UNC style file paths in the SQL query (\\<sharename>\data\report1\report1.txt as an example).
  • I have NOT figured out how to use environment variables or the like to make the SQL queries somewhat dynamic. At this point all my SQL queries are static. I am truly open to suggestions here. Large projects can result in *many* static queries that ultimately need to be modified.

SQL Notes:

  • the SQL functionality is limited by the Jet database (aka Microsoft Access). Tis not as good as T-SQL, but not too bad. Syntax is often the challenge, but most is well defined in various Internet forums.
  • When you need to combine multiple CSV source files into a single table use the Union All command. For example:
    select * from c:\data\SalesReports\2011full.csv
    union all
    select * from c:\data\SalesReports\2012full.csv
    union all
    select * from c:\data\SalesReports\2013ytd.csv
  • When you need to limit the records by another file, use a join such as:
    select *
    from c:\data\SalesReports\2013ytd.csv s, c:\data\Customers\CustListA c
    where s.custno=c.custno


I beg forgiveness for not properly acknowledging those that I have learned from. It has been a long and winding path to this point and alas I have not taken good notes. The best can do at this point is call out those who I sponge from regularly:

Update 1/18/2014 – For a little more detail on the process of creating the schema.ini file, including screen shots, see Teo Lachev’s blog: http://prologika.com/CS/blogs/blog/archive/2014/01/17/auto-generating-schema-ini.aspx

Update 10/3/2014 – Hit a snag today. Tried to import a CSV file that had a dash “-” in the file name. Import routine did NOT like the dash. I tried various bracketing methods including `'[] to no avail. Ended up changing the file name using underscore “_” instead of dash. Just thought I’d share…

Update 3/25/2015 – Again had a challenge with a – in a file name. I was able to fix the issue using the character ` (found on the tilde ~ key on my keyboard). Trick is to tic the start of the path to the last \, then tic after the last \ to the end of the path. Example:
select * from `c:\somedir\anotherdir`\`some-file.txt`

Update 4/2016 – Again I had a challenge with a file name – but this time with an Excel file on a Windows Share where the share name had dashes and dollar signs. Excel file??? Yes, I use this same approach to read Excel files via CSV. Just pick the “Microsoft Excel Driver” instead of the “Microsoft Access Text Driver” described above. Anyway back to this path issue… the solution: square brackets
Example for an Excel file:
select * from [\\servername\sharename\somedir\some-file.xlsx].[sheetname]

Why do I keep making updates to this old blog? For MY future reference of course…