Data Warehousing and Data Science

26 May 2012

How to Populate a Fact Table using SSIS (part 1)

Filed under: Data Warehousing,SSIS — Vincent Rainardi @ 2:24 pm
Tags: ,

Seems to me that some people are still struggling to populate their fact tables using SSIS. When doing it, they come across issues like these:

  1. Where do I populate my fact table from?
  2. How do I get the dimension keys to put into my fact table?
  3. Where can I get the data for the measures columns?
  4. With what do I populate the snapshot date column?
  5. What is the primary key of my fact table?
  6. The source table doesn’t have a primary key of the source table. What should I do?
  7. I got duplicate rows in the fact table. How do I handle it?
  8. The rows with that snapshot date already exist. What should I do?
  9. Lookup cache: should I choose full cache or partial cache?
  10. I can’t find the row in the dimension table, what should I do?

As always, the best way to explain is by example, which is more effective than answering the above questions. So I’m going to do the following:

  1. Describe the background on the company and the data warehouse
  2. Create the source tables and populate them
  3. Create the dimension tables and populate them
  4. Create the fact table (empty)
  5. Build an SSIS package to populate the fact table, step by step


It’s a van hire company called TopHire. It’s purely fictional of course. Their business system (HireBase) captures the rental information, including the customer information. HireBase has a fleet database where all vans are maintained. HireBase contains only 3 tables:

  1. Customer: contains 100 customers, e.g. name, data of birth, telephone number, etc. A column called CustomerId uniquely identifies a customer.
  2. Van: contains 20 vans that Top Hire operates. It’s their fleet. Contains the registration number, engine size, van size, colour, year of manufacture. The unique identifier for each van is the registration number.
  3. Hire: contains 1000 hire transactions since 1st Jan 2011. Every hire transaction stores: who the customer was, which van was rented out, the date it occurred, number of days and various hire fees/charges (the van, sat nav, insurance, damage waiver and total bill). For each transaction we have Hire ID, which is a unique identifier in this table.

The data warehouse contains 4 tables:

  1. Date dimension: contains every single date from 2006 to 2016.
  2. Customer dimension: contains 100 customers. To be simple we’ll make it type 1 so we don’t create a new row for each change.
  3. Van dimension: contains 20 vans. To be simple we’ll make it type 1 so we don’t create a new row for each change.
  4. Hire fact table: contains 1000 hire transactions since 1st Jan 2011. It is a daily snapshot fact table so that every day we insert 1000 rows into this fact table. So over time we can track the changes of total bill, van charges, satnav income, etc.

Create the source tables and populate them

So now we are going to create the 3 tables in HireBase database: Customer, Van, and Hire. Then we populate them.

First I’ll show you how it looks when it’s done:

Customer table:

Van table:

Hire table:

And here is the script to create and populate them:

-- Create database
create database HireBase
use HireBase

-- Create customer table
if exists (select * from sys.tables where name = 'Customer')
drop table Customer

create table Customer
( CustomerId varchar(20) not null primary key,
 CustomerName varchar(30), DateOfBirth date, Town varchar(50),
 TelephoneNo varchar(30), DrivingLicenceNo varchar(30), Occupation varchar(30)

-- Populate Customer
truncate table Customer

declare @i int, @si varchar(10), @startdate date
set @i = 1
while @i <= 100
 set @si = right('0'+CONVERT(varchar(10), @i),2)
 insert into Customer
 ( CustomerId, CustomerName, DateOfBirth, Town, TelephoneNo, DrivingLicenceNo, Occupation)
 ( 'N'+@si, 'Customer'+@si, DATEADD(d,@i-1,'2000-01-01'), 'Town'+@si, 'Phone'+@si, 'Licence'+@si, 'Occupation'+@si)
 set @i = @i + 1

select * from Customer

-- Create Van table
if exists (select * from sys.tables where name = 'Van')
drop table Van

create table Van
( RegNo varchar(10) not null primary key,
 Make varchar(30), Model varchar(30), [Year] varchar(4),
 Colour varchar(20), CC int, Class varchar(10)

-- Populate Van table
truncate table Van

declare @i int, @si varchar(10)
set @i = 1
while @i <= 20
 set @si = convert(varchar, @i)
 insert into Van
 ( RegNo, Make, Model, [Year], Colour, CC, Class)
 ( 'Reg'+@si, 'Make'+@si, 'Model'+@si,
 case @i%4 when 0 then 2008 when 1 then 2009 when 2 then 2010 when 3 then 2011 end,
 case when @i%5<3 then 'White' else 'Black' end,
 case @i%3 when 0 then 2000 when 1 then 2500 when 2 then 3000 end,
 case @i%3 when 0 then 'Small' when 1 then 'Medium' when 2 then 'Large' end)
 set @i = @i + 1

select * from Van

-- Create Hire table
if exists (select * from sys.tables where name = 'Hire')
drop table Hire

create table Hire
( HireId varchar(10) not null primary key,
 HireDate date not null,
 CustomerId varchar(20) not null,
 RegNo varchar(10), NoOfDays int, VanHire money, SatNavHire money,
 Insurance money, DamageWaiver money, TotalBill money

-- Populate Hire table
truncate table Hire

declare @i int, @si varchar(10), @DaysFrom1stJan int, @CustomerId int, @RegNo int, @mi int
set @i = 1
while @i <= 1000
 set @si = right('000'+convert(varchar(10), @i),4) -- string of i
 set @DaysFrom1stJan = (@i-1)%200 --The Hire Date is derived from i modulo 200
 set @CustomerId = (@i-1)%100+1 --The CustomerId is derived from i modulo 100
 set @RegNo = (@i-1)%20+1 --The Van RegNo is derived from i modulo 20
 set @mi = (@i-1)%3+1 --i modulo 3
 insert into Hire (HireId, HireDate, CustomerId, RegNo, NoOfDays, VanHire, SatNavHire, Insurance, DamageWaiver, TotalBill)
 values ('H'+@si, DateAdd(d, @DaysFrom1stJan, '2011-01-01'),
 left('N0'+CONVERT(varchar(10),@CustomerId),3), 'Reg'+CONVERT(varchar(10), @RegNo),
 @mi, @mi*100, @mi*10, @mi*20, @mi*40, @mi*170)
 set @i += 1

select * from Hire

Create the Data Warehouse

So now we are going to create the 3 dimension tables and 1 fact table in the data warehouse: DimDate, DimCustomer, DimVan and FactHire. We are going to populate the 3 dimensions but we’ll leave the fact table empty. The purpose of this article is to show how to populate the fact table using SSIS.

First I’ll show you how it looks when it’s done:

Date Dimension:

Customer Dimension:

Van Dimension:

Hire Fact Table:

And then we do it. This is the script to create and populate those dim and fact tables:

-- Create the data warehouse
create database TopHireDW
use TopHireDW

-- Create Date Dimension
if exists (select * from sys.tables where name = 'DimDate')
drop table DimDate

create table DimDate
( DateKey int not null primary key,
 [Year] varchar(7), [Month] varchar(7), [Date] date, DateString varchar(10))

-- Populate Date Dimension
truncate table DimDate

declare @i int, @Date date, @StartDate date, @EndDate date, @DateKey int,
 @DateString varchar(10), @Year varchar(4),
 @Month varchar(7), @Date1 varchar(20)
set @StartDate = '2006-01-01'
set @EndDate = '2016-12-31'
set @Date = @StartDate

insert into DimDate (DateKey, [Year], [Month], [Date], DateString)
 values (0, 'Unknown', 'Unknown', '0001-01-01', 'Unknown') --The unknown row

while @Date <= @EndDate
 set @DateString = convert(varchar(10), @Date, 20)
 set @DateKey = convert(int, replace(@DateString,'-',''))
 set @Year = left(@DateString,4)
 set @Month = left(@DateString, 7)
 insert into DimDate (DateKey, [Year], [Month], [Date], DateString)
 values (@DateKey, @Year, @Month, @Date, @DateString)
 set @Date = dateadd(d, 1, @Date)

select * from DimDate

-- Create Customer dimension
if exists (select * from sys.tables where name = 'DimCustomer')
drop table DimCustomer

create table DimCustomer
( CustomerKey int not null identity(1,1) primary key,
 CustomerId varchar(20) not null,
 CustomerName varchar(30), DateOfBirth date, Town varchar(50),
 TelephoneNo varchar(30), DrivingLicenceNo varchar(30), Occupation varchar(30)

insert into DimCustomer (CustomerId, CustomerName, DateOfBirth, Town, TelephoneNo,
 DrivingLicenceNo, Occupation)
select * from HireBase.dbo.Customer

select * from DimCustomer

-- Create Van dimension
if exists (select * from sys.tables where name = 'DimVan')
drop table DimVan

create table DimVan
( VanKey int not null identity(1,1) primary key,
 RegNo varchar(10) not null,
 Make varchar(30), Model varchar(30), [Year] varchar(4),
 Colour varchar(20), CC int, Class varchar(10)

insert into DimVan (RegNo, Make, Model, [Year], Colour, CC, Class)
select * from HireBase.dbo.Van

select * from DimVan

-- Create Hire fact table
if exists (select * from sys.tables where name = 'FactHire')
drop table FactHire

create table FactHire
( SnapshotDateKey int not null, --Daily periodic snapshot fact table
 HireDateKey int not null, CustomerKey int not null, VanKey int not null, --Dimension Keys
 HireId varchar(10) not null, --Degenerate Dimension
 NoOfDays int, VanHire money, SatNavHire money,
 Insurance money, DamageWaiver money, TotalBill money

select * from FactHire

Now you can see that the 3 dimensions have been populated. And the fact table is created and empty, ready for us to populate it.

Build the SSIS Package to Populate the Fact Table

First I show you how it looks when it’s done. This is the overall workflow we are going to build:

This is a bit of description on each of the 6 boxes above:

  1. Read Hire table in HireBase: First we get the original OLTP records from the hire table in HireBase.
  2. Get Snapshot Date Key: we get today’s date and convert it into an integer as Snapshot Date Key. FactHire is a snapshot fact table. A periodic snapshot fact table. Which means that every day we populate the fact table with 1000 rows like this:Next week, on Monday 28th May, we insert 1000 rows to the fact table, which is all the rows in the Hire table. Then on Tuesday 29th  May we insert 1010 rows. Because there would be 10 new hire transactions in the Hire table in HireBase system. The 1000 “old” rows might change as well on Tuesday, not all of them will be the same as Monday. Perhaps there are 10 rows which are modified, the amount/fees changed, etc. Whatever the condition of the Hire table on Tuesday, we are taking all rows in put them into our data warehouse. And we do this every day. We every day, put whatever is in the Hire table into FactHire. That’s what Periodic Snapshot fact table means.Hence in a periodic snapshot fact table we have a “Snapshot Date”. Meaning: the date when we captured the source table. Or, the date when these 1000 rows were inserted into this fact table. The value of this snapshot date is today’s date (the date the ETL runs). The date the load happens.
  3. Get Customer Key: from Hire table we get the Customer ID which was involved in the hire transaction. We then go to the Customer Dimension to get the Customer Key for this Customer ID.
  4. Get Van Key: from Hire table we get the Registration Number which was rented out in the transaction. We then go to the Van Dimension to get the Van Key for this Registration Number.
  5. Get Hire Date Key: from Hire table we get the Hire Date, which is the date when the hire transaction happened. We then go to the Date Dimension to get the Date Key for this Hire Date.
  6. Populate FactHire: After we get all the required dimension keys, we insert the rows into the FactHire fact table.

Now that we know what to build, let’s build it. The box numbers below refer to the 6 boxes above.

Box 1. Read Hire table in HireBase

We create the data source in the workflow. It’s OLE DB Data Flow Source, from HireBase. We take all columns, so we don’t specify a query:

Box 2. Get Snapshot Date Key

Create a Derived Column and set the Expression to:


See the complete article here.

Box 3. Get Customer Key

Create a Lookup transformation and set to Full Cache, OLE DB connection manager, Fail component, like this:

Full cache: SSIS reads all rows from DimCustomer into memory, then read the Hire table and SSIS doesn’t need to read DimCustomer any more. All lookup operations are done in memory.
Partial cache: SSIS build the memory cache while reading the rows from Hire table. When SSIS reads the rows from Hire table, it tries to find the Customer Key in memory cache. If it can’t find it, it reads the DimCustomer on disk.
No cache: SSIS doesn’t build memory cache. SSIS joins DimCustomer to Hire table (using RDBMS operation) to find the Customer Key.

Cache Connection Manager (CCM): SSIS reads DimCustomer and put in memory cache. This cache can be reused and shared between several Lookups, and across several dataflows. CCM reduces database workload.

OLE DB connection manager: a table in the warehouse (in this case it’s DimCustomer), see Connection pane below:

In the Columns pane, connect Customer Id from the Hire table to the Customer Id on the DimCustomer table, and bring the CustomerKey as output:

Box 4: Get Van Key

It’s the same as Get Customer Key above, i.e. using Lookup transform. When connecting [Get Customer Key] and [Get Van Key] choose Lookup Match Output. I’ll come back for Lookup No Match Output at the end of the article. I just want to get this up and running first. We should always get a simple one first up and running, then build up from there.

Box 5. Get Hire Date Key

It’s the same as Get Customer Key above.

Box 6. Populate FactHire

It’s OLE DB destination. On the Connection Manager pane, specify FactHire as the target table, and leave everything as is:

On the Mapping pane, the source column has the same name as the destination column, except HireDateKey:

That’s it. Run it and all should be green, inserting 1000 rows into the target fact table:

And the FactHire fact table now looks like this:

The 4 dimension key columns are populated with the surrogate key from the respective dimension.

Wrapping Up

The above workflow works but there are 2 things we need to do:

  1. The no match rows
    If in the Hire table we have a CustomerId which does not exist in the Customer Dimension, we need to set the CustomerKey to 0.
  2. Delete fact rows, if exists
    If today’s rows exists in the target fact table, delete them first.

As this is long enough. I’m going to put it as a separate article (link).

Hope this helps,

Vincent Rainardi

26/10/12: Data type error when mapping HireDate column to Date column

On Box 5 Get Hire Date Key, on the Lookup Transformation editor, on the Columns pane (see below), when we try to map HireDate column from Available Input Columns to the Date column on Available Lookup Columns, we get an error message: Cannot map the input column, ‘HireDate’, to the lookup column, ‘Date’, because the data types do not match:

Thank you to Masu for pointing this out.

This is because on the Hire table (the input), the HireDate column is in date data type, whereas in the DimDate table, the Date column is in varchar(10).

To solve this issue, in the Get Hire Date Key task, in the Connection pane, instead of “Use a table or a view”, select “Use results of an SQL query” and type:

select DateKey, Year, Month, convert(date, Date) from DimDate where date <> ‘Unknown’

Because both are now of data type Date, in the column pane you won’t have any issue connecting HireDate to Date:


  1. […] part 1 (link) we have populated a fact table (FactHire) by reading the source table (Hire) and looking up 3 […]

    Pingback by How to Populate a Fact Table using SSIS (part 2) « Data Warehousing and Business Intelligence — 26 May 2012 @ 9:59 pm | Reply

  2. I hope it will help me…
    but, Mr Vincent, I wanna ask you about my problem
    when I built data warehouse, I fill the dimension table by integrate it with the source…
    then, if I wanna populate my fact table, can I use like what you explained above?
    I rather confused about column ‘SnapshotDateKey’ of FactHire fact table…why you create it?
    can you help me?
    thank you…

    Comment by ita — 30 September 2012 @ 7:28 pm | Reply

    • Hi ita, yes you can use the above technique that I explained to populate your fact table.
      The ‘SnapshotDateKey’ column in the FactHire fact table is to differentiate the day of loading. Every day we load all the rows from the source table into this fact table. For yesterday’s load, the SnapshotDateKey column would contain 121001 (1st October 2012) and for today’s load, the SnapshotDateKey column would contain 121002 (2nd October 2012). Please read Kimball & Ross’ Dimensional Modelling book chapter 2 about “periodic snapshot fact tables”.

      Comment by Vincent Rainardi — 2 October 2012 @ 6:18 pm | Reply

      • ok Mr. Vincent…after I read and understanding your tecnique here and on your ebook, I get what you mean.
        thank for your help Mr. Vincent.. 🙂

        Comment by ita — 2 October 2012 @ 6:27 pm

  3. Hi Vincent,
    I would like to say thank you for the nice article. I have some problem with “Get Hire Date Key” I got the message like “Can not map the input column, Hire Date to look up column, Date, because data types do not match”
    I followed below mentioned steps
    1. Selected localhost.TopHireDW at OLE DB Connection Manager
    2. Selected [dbo].[DimDate] at Use a table or view
    3. Tried to map HireDate with Date and got above mentioned message
    Your help is truly appreciate

    Comment by Masu — 26 October 2012 @ 12:52 am | Reply

    • Thank you for pointing it out Masu. I have explained this at the end of the article above.

      Comment by Vincent Rainardi — 26 October 2012 @ 9:16 pm | Reply

    • Thank you Sir for your explanation, I understood it well

      Comment by Masu — 27 October 2012 @ 8:24 pm | Reply

  4. Your write up is very good, I just have one problem with my DW. I am curious why your source is a table from the staging area in your fact table loading sequence (table “Hire”). In my case, I have 3 populated tables in my staging area, 4 populated tables in my DW area and a blank fact table with all proper columns. When I use my “Fact-sales” as the source and destination table, the lookup processes run succesfully but do not populate my fact table. Also, in my destination table, when I look at mappings all of my source mappings are in format “lookupsequencename.columnname”… What am I missing?

    Comment by Ethan — 7 April 2013 @ 2:14 am | Reply

  5. its really really helpful. thanx alot..

    Comment by wasu — 9 September 2013 @ 12:19 pm | Reply

  6. Hi Vincent, this is a very good tutorial, but I got a problem in Get Hire Date Key. That makes me stuck to this Part 1 and couldn’t get my self to the next part of tutorial.
    So here’s the problem.
    Like u said before, use SQL query not table or a view. I’ve already used your code but theres error message said “Conversion failed when converting date and/or time from character string”.
    Is there something that I can do to fix this problem?


    Comment by Buby — 3 July 2014 @ 9:37 am | Reply

    • The error message appears when I pressed Preview on Lookup Transformation Editor, for the “Get Hire Date Key”.
      Thanks in advance for your help

      Comment by Buby — 3 July 2014 @ 9:39 am | Reply

      • Hi Buby,
        one possible cause of “Conversion failed when converting date and/or time from character string” error message is if in the Lookup Transformation Editor window of the Get Hire Date Key you type the SQL query as:
        select DateKey, Year, Month, convert(date, Date) as Date from DimDate
        One of the row in DimDate contains the word ‘Unknown’ in the Date column so it can not be converted to Date, resulting in the above error message.
        Hence the query should be:
        select DateKey, Year, Month, convert(date, Date) as Date from DimDate where Date ‘Unknown’
        I hope this was the issue, if not please get back to me.
        Kind regards,

        Comment by Vincent Rainardi — 5 July 2014 @ 8:30 am

    • I solved this issue, using the clause NOT LIKE

      select DateKey, [Year], [Month], convert(date, [Date])
      from DimDate
      where [Date] not like ‘Unknown’

      Comment by Rafael Lima — 30 June 2016 @ 2:02 am | Reply

  7. This article is very good, but as a beginner in SSIS and OLAP I got lost just at “Box 1. Read Hire table in HireBase”.
    Can you please give details as to how about you get the OLE DB source Editor screen. I opened SSIS in BIDS, did new project, selected Integration Services project. Click on DataFlow tab, double click on OLE DB Source. I get the following error “The Data Flow object could not be added. Data Flow object can only be added to a Data Flow task.

    Comment by Emma — 15 October 2014 @ 3:02 pm | Reply

    • Hi Emma,
      Open Business Intelligence Development Studio or SQL Server Data Tools, create a new SSIS project or open an existing SSIS project, click Data Flow Task on the left hand panel (SSIS Toolbox) and drag it to the Control Flow tab. Double click this Data Flow Task icon and the tab will automatically switch from Control Flow tab to Data Flow tab. Look at the “Source” section of the SSIS Toolbox and find OLE DB Source. Click it and drag it to the Data Flow tab’s canvas.
      A good resource to start learning SSIS is (Create a simple ETL package)

      Comment by Vincent Rainardi — 16 October 2014 @ 6:26 pm | Reply

      • Thank you Vincent. Much appreciated for your help.

        Comment by Emma — 17 October 2014 @ 10:11 am

  8. Hi Vincent, Excellent Article! Would you recommend any article which at the Step1 of creating Fact tables follows Insert/Update Logic rather than complete snapshot ? ( Insert if exists else Update if any existing records have been changed) . Thanks in advance.

    Comment by Robbie — 16 January 2015 @ 12:38 am | Reply

    • Hi Robbie, no I don’t know any article which explains the details of how to upsert into a transactional fact table, using SSIS or other ETL tool. Transactional fact tables (rather than snapshot fact tables) are inserted but not updated. This is because if we update, it would take a long time. But suppose that your fact table only contains 100k rows, or even 1m this is possible (I’d expect update on 1m rows to be about 5s). I think the best way is to use manual upsert command using Execute SQL Task. I don’t like using Merge command, nor SCD Wizard, because of their performance.
      To support this update SQL command, the fact table needs to have an index on the composite PK. The widely-adopted SQL Server indexing technique of a single-column PK with identity being a clustered index, plus a non-clustered index on the dimkey columns are sufficient to support performance. I believe a more superior performance can be achieved by clustering on the dimkey columns, with negligable performance overhead on the insert operations (the issue with this approach is more of functional one than performance: duplicate PK occurs when when we insert a second row with the same dimkeys — so I would recommend the first approach above).

      Comment by Vincent Rainardi — 16 January 2015 @ 6:18 am | Reply

    • HI Robbie, did you find any suitable article for your question? I am looking for same scenario. If Yes, I would appreciate if you can share it with me. TIA

      Comment by Pre — 30 June 2015 @ 7:26 am | Reply

  9. What an amazingly concise but very effective guide, using straight to the point and meaningful examples! People who write books/papers tend to use examples they have become aquatinted within in a work environment/subject, which often results in the reader being lost. This gives great examples on how the fact table should look (albeit basic), i.e not too different to the OLTP main table. It also conveys how the fact table should interact with the dimensions as opposed to an OLTP environment i.e Star over e-r modelling. Thanks!

    Comment by BenMc — 1 February 2015 @ 2:30 pm | Reply

  10. thanks this article has been of help to me, please I would like to ask if i am making a mistake, because after following the article, i was expecting the database diagram for the warehouse to be in a star form as they are related, but mine does not have that relation, am i to relate them manually or I have missed something along the line

    Comment by leye — 2 March 2015 @ 9:34 pm | Reply

    • Hi, I did not create the foreign keys (FK) hence in the database diagram it is not a star.
      I prefer not to create FKs in the warehouse, but manually control and check it from the ETL & DQ, because FK could cause performance issues. For example when deleting rows from dimensions, or updating the surrogate keys on the dimensions or bridge tables, also if the dim keys on the fact tables are left as null because they are late arriving fact rows.

      Comment by Vincent Rainardi — 3 March 2015 @ 5:09 am | Reply

  11. Excellent article ! Steps are so easy to follow and the concepts have been explained clearly. Thanks for posting.

    Comment by Danny — 2 May 2015 @ 3:10 am | Reply

  12. Vincent,
    Great Material. How do I create a SSAS cube from this data populated with SSIS. Do you have any link

    Comment by Kilambi — 26 August 2015 @ 8:30 pm | Reply

  13. […] this session I will be doing a demo on my article “Populating a Fact Table using SSIS”: by going through it step by step on the screen. Giving the audience plenty of time to understand […]

    Pingback by SQLBits XV Liverpool 4-7th May 2016 | Data Warehousing and Business Intelligence — 18 December 2015 @ 10:17 pm | Reply

  14. This is has really helped a lot in the project i’m working. However, how do I handle null exception

    Comment by seyi — 28 April 2016 @ 3:25 pm | Reply

    • It depends on which column is null Seyi. If an attribute or measure column is null then we let them stay as null in the warehouse. If a natural key is not found in the dimension then we map it to the unknown row by setting the dimension key to 0.
      What do you mean by null exception? (I have a feeling you meant different thing).

      Comment by Vincent Rainardi — 28 April 2016 @ 6:31 pm | Reply

      • I wasn’t expecting a surrogate key in a dimension table to be null but I keep getting an error “Cannot insert the value NULL into column ‘InsuredKey’, table ‘factTable’; column does not allow nulls. INSERT fails.” yet I followed the example of setting to 0 insuredKey in lookup no match output in the part 2 of this series. I’m really kind of stuck at the moment

        Comment by Seyi — 3 May 2016 @ 10:27 pm

      • Hi Seyi, the problem is in the “Mismatch Insured” box. Potential issues are:
        – Specify how to handle rows with no matching entries: it is set to “Redirect rows to no match output”?
        – Is the Mismatch Insured box a Derived Column transform?
        – In the Mismatch Insured box, is the Derived Column Name column set to Insured Key?
        – In the Mismatch Insured box, is the Expression column set to zero?
        The second thing to check is the Merge box. Potential issues are:
        – Is the output of Mismatch Insured Box going to a Sort box?
        – In the Sort box, is the Input Column set to Insured ID?

        Comment by Vincent Rainardi — 4 May 2016 @ 6:21 pm

  15. how to calculate meseur in your fact table using data tools for visual studio2013

    Comment by belkis — 30 April 2017 @ 9:11 am | Reply

    • Hi Belkis/Moktefib, yes it is possible to calculate the measures in the fact table using Data Tools for Visual Studio 2013

      Comment by Vincent Rainardi — 30 April 2017 @ 4:32 pm | Reply

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