I am trying to automate SSAS tabular model refresh. The requirement is - depending on the tables chosen, the model will be refreshed only for those tables. I am looking for a way to dynamically build the script to process only the selected tables in the first step of an SQL agent job and pass that dynamically built script to next step which will be SQL Server Analysis Services Command step. Or maybe execute the script built in step 1 itself. But I am not sure how could this be achieved. Please let me know the possible ways.
Have you considered doing this through SSIS and executing the package from SQL Agent? You can use an Analysis Services Processing Task and select the tables that you want to process. If you want to do this in a more dynamic manner, the follow items outline how this can be done.
The table names that you want to process will be stored in an object variable. One option for this is to query an SSAS DMV from an Execute SQL Task for the names of that tables that will be processed and output these names into an object variable. You'll need to set the Result Set to use a full result set and map the object variable in the Result Set pane. The following command will return the unique table names (table_type filter is used to remove results prefixed with $) select table_name from $SYSTEM.DBSCHEMA_TABLES where table_catalog = 'YourTabularModel' and table_schema = 'Model' and table_type = 'SYSTEM TABLE'
If you will be using SSAS DMVs then create an OLE DB connection manager using Microsoft OLE DB Provider for Analysis Services 13.0 as the provider. Make sure to set the initial catalog to the SSAS model with the tables that will be processed.
Add a Foreach ADO Enumerator Loop that will use the object variable as the source variable in the Collection pane. In the Variable Mappings pane, add a variable to store the table name.
Inside the Foreach Loop, add an Analysis Services Execute DDL Task.
Create a string variable with an expression that is the SSAS process command for the table. In the expression replace the table field (assuming you're using TMSL) with the variable holding the table name.
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I want to utilise the ADF Copy function, to carry out incremental table extracts from one Azure DB to another. Every table in the database that I need all have the same 2 relevant fields i.e. date1, date2. For Watermark comparison purposes, I need to use isnull(date1,date2), but unsure how to do this, i.e. I am not sure how I can add this consistent derived value to the Source as an additional field that can perhaps be added via the Query or Stored Procedure Option on the source, to utilise the #item().source.schema and #item().source.table values that have already been generated as parameters..?
You can use the query option in the Copy data activity source and add a new column in the query itself to get the results of isnull(date1,date2) and include the parameter values to get the table name instead of hardcoding them as shown below.
In source, select Query option under Use query and add dynamic content to concat() select statement with parameter values.
#concat('select *, isnull(date1,date2) as final_dt from ',pipeline().parameters.schema,'.',pipeline().parameters.table)
Sink table data output:
I need a data factory that will:
check an Azure blob container for csv files
for each csv file
insert a row into an Azure Sql table, giving filename as a column value
There's just a single csv file in the blob container and this file contains five rows.
So far I have the following actions:
Within the for-each action I have a copy action. I did give this a source of a dynamic dataset which had a filename set as a parameter from #Item().name. However, as a result 5 rows were inserted into the target table whereas I was expecting just one.
The for-each loop executes just once but I don't know to use a data source that is variable(s) holding the filename and timestamp?
You are headed in the right direction, but within the For each you just need a Stored Procedure Activity that will insert the FileName (and whatever other metadata you have available) into Azure DB Table.
Like this:
Here is an example of the stored procedure in the DB:
CREATE Procedure Log.PopulateFileLog (#FileName varchar(100))
INSERT INTO Log.CvsRxFileLog
select
#FileName as FileName,
getdate() as ETL_Timestamp
EDIT:
You could also execute the insert directly with a Lookup Activity within the For Each like so:
EDIT 2
This will show how to do it without a for each
NOTE: This is the most cost effective method, especially when dealing with hundred or thousands of files on a recurring basis!!!
1st, Copy the output Json Array from your lookup/get metadata activity using a Copy Data activity with a Source of Azure SQLDB and Sink of Blob Storage CSV file
-------SOURCE:
-------SINK:
2nd, Create another Copy Data Activity with a Source of Blob Storage Json file, and a Sink of Azure SQLDB
---------SOURCE:
---------SINK:
---------MAPPING:
In essence, you save the entire json Output to a file in Blob, you then copy that file using a json file type to azure db. This way you have 3 activities to run even if you are trying to insert from a dataset that has 500 items in it.
Of course there is always more than one way to do things, but I don't think you need a For Each activity for this task. Activities like Lookup, Get Metadata and Filter output their results as JSON which can be passed around. This JSON can contain one or many items and can be passed to a Stored Procedure. An example pattern:
This is the sort of ELT pattern common with early ADF gen 2 (prior to Mapping Data Flows) which makes use of resources already in use in your architecture. You should remember that you are charged by the activity executions in ADF (eg multiple iteration in an unnecessary For Each loop) and that generally compute in Azure is expensive and storage is cheap, so think about this when implementing patterns in ADF. If you build the pattern above you have two types of compute: the compute behind your Azure SQL DB and the Azure Integration Runtime, so two types of compute. If you add a Data Flow to that, you will have a third type of compute operating concurrently to the other two, so personally I only add these under certain conditions.
An example implementation of the above pattern:
Note the expression I am passing into my example logging proc:
#string(activity('Filter1').output.Value)
Data Flows is perfectly fine if you want a low-code approach and do not have compute resource already available to do this processing. In your case you already have an Azure SQL DB which is quite capable with JSON processing, eg via the OPENJSON, JSON_VALUE and JSON_QUERY functions.
You mention not wanting to deploy additional code which I understand, but then where did your original SQL table come from? If you are absolutely against deploying additional code, you could simply call the sp_executesql stored proc via the Stored Proc activity, use a dynamic SQL statement which inserts your record, something like this:
#concat( 'INSERT INTO dbo.myLog ( logRecord ) SELECT ''', activity('Filter1').output, ''' ')
Shred the JSON either in your stored proc or later, eg
SELECT y.[key] AS name, y.[value] AS [fileName]
FROM dbo.myLog
CROSS APPLY OPENJSON( logRecord ) x
CROSS APPLY OPENJSON( x.[value] ) y
WHERE logId = 16
AND y.[key] = 'name';
I have a handful of data sources that I'd like to apply the same analyses to and eventually load into a larger table database (uniformtable). Different sources contain different columns, and sometimes sources involve crosswalk files that I need to join. I'd like to have one query that converts all sources' data into uniformtable formatting, based on a unique key for each source. Something along the lines of this:
case when source.sourceid = 1 then
create uniformtable as
select column1a as uniforma, column1b as uniformb, sourceid from source
else
when source.sourceid = 2 then
create uniformtable as
select column2a as uniforma, column2b as uniformb, sourceid from source
end;
I've tried using if-then and case-when to accomplish this, but I get syntax errors pointing to the very start of my query. Does Redshift allow you to use if logic for this kind of control?
No, this logic is not permitted.
CASE statements are only valid within a SELECT statement.
You would need to perform this logic external to Amazon Redshift, and then just send the final SQL to create the table.
I am pretty new to Pentaho so my query might sound very novice.
I have written a transformation in which am using CSV file input step and table input step.
Steps I followed:
Initially, I created a parameter in transformation properties. The
parameter birthdate doesn't have any default value set.
I have used this parameter in postgresql query in table input step
in the following manner:
select * from person where EXTRACT(YEAR FROM birthdate) > ${birthdate};
I am reading the CSV file using CSV file input step. How do I assign the birthdate value which is present in my CSV file to the parameter which I created in the transformation?
(OR)
Could you guide me the process of assigning the CSV field value directly to the SQL query used in the table input step without the use of a parameter?
TLDR;
I recommend using a "database join" step like in my third suggestion below.
See the last image for reference
First idea - Using Table Input as originally asked
Well, you don't need any parameter for that, unless you are going to provide the value for that parameter when asking the transformation to run. If you need to read data from a CSV you can do that with this approach.
First, read your CSV and make sure your rows are ok.
After that, use a select values to keep only the columns to be used as parameters.
In the table input, use a placeholder (?) to determine where to place the data and ask it to run for each row that it receives from the source step.
Just keep in ming that the order of columns received by the table input (the columns out of the select values) is the same order that it will be used for the placeholders (?). This should not be a problem with your question that uses only one placeholder, but keep that in mind as you ramp up using Pentaho.
Second idea, using a Database Lookup
This is another approach where you can't personalize the query made to the database and may experience a better performance because you can set a "Enable cache" flag and if you don't need to use a function on your where clause this is really recommended.
Third idea, using a Database Join
That is my recommended approach if you need a function on your where clause. It looks a lot like the Table Input approach but you can skip the select values step and select what columns to use, repeat the same column a bunch of times and enable a "outer join" flag that returns the rows without result from the query
ProTip: If you feel the transformation running too slow, try to use multiple copies from the step (documentation here) and obviously make sure the table have the appropriate indexes in place.
Yes there's a way of assigning directly without the use of parameter. Do as follows.
Use Block this step until steps finish to halt the table input step till csv input step completes.
Following is how you configure each step.
Note:
Postgres query should be select * from person where EXTRACT(YEAR
FROM birthdate) > ?::integer
Check Execute for each row and Replace variables in in Table input step.
Select only the birthday column in CSV input step.
I need some help with a SSIS Script Task (SQL 2008 R2) that dynamically creates a package. I am refining a package that copies data from a Sage Timberline (Now rebranded to Sage 300) Pervasive SQL environment to a SQL server data warehouse. I can create a package that opens the connection to Timberline and copies the data to a table in SQL Server. The problem is, for each company in timberline and each table in SQL, I need to create a separate data flow task. Given the three Timberline company folders and the number of tables in each folder, this would take a lot of time to create and be cumbersome to maintain and troubleshoot.
I am trying to create a package that uses a Foreach Loop to create a package that creates a ADO/ODBC source (Timberline), a OLE destination (SQL) and dynamically handles the column mapping. I found code here that almost does what I need.
I tested this code and it works great using OLE SQL source and destinations. What makes this script work is that it dynamically handles the column mapping. So, it you placed it into a Foreach Loop of the 100 or so tables, with each loop it could dynamically create the data flow and map the columns, then execute the new package.
My problem is that I can only connect to Timberline using ODBC. So, I need to modify the script to create the source connection with ADO NET (ODBC) instead of OLE. I’m having a lot of trouble trying to figure this out. Could someone please help me out with this?
Here the other couple of things I tried first, other than this approach:
Solution: Setup a Linked server to Timberline Pervasive SQL
Problem: SQL server is 64-bit and the Timberline driver is 32-bit. Using a linked server returns a architecture mismatch error. I called Sage and they said they have no plans to release a 64-bit drive.
Solution: Use one of the SQL Transfer tasks
Problem: Only works with SQL databases. This source is a Pervasive SQL database
Solution: Use a “INSERT … INTO …” type script
Problem: This requires a linked server. See the problem above
Here’s the section of the original VB .NET code I need help with:
'To Create a package named [Sample Package]
Dim package As New Package()
package.Name = "Sample Package"
package.PackageType = DTSPackageType.DTSDesigner100
package.VersionBuild = 1
'To add Connection Manager to the package
'For source database (OLTP)
Dim OLTP As ConnectionManager = package.Connections.Add("OLEDB")
OLTP.ConnectionString = "Data Source=.;Initial Catalog=OLTP;Provider=SQLNCLI10;Integrated Security=SSPI;Auto Translate=False;"
OLTP.Name = "LocalHost.OLTP"
'To add Load Employee Dim to the package [Data Flow Task]
Dim dataFlowTaskHost As TaskHost = DirectCast(package.Executables.Add("SSIS.Pipeline.2"), TaskHost)
dataFlowTaskHost.Name = "Load Employee Dim"
dataFlowTaskHost.FailPackageOnFailure = True
dataFlowTaskHost.FailParentOnFailure = True
dataFlowTaskHost.DelayValidation = False
dataFlowTaskHost.Description = "Data Flow Task"
'-----------Data Flow Inner component starts----------------
Dim dataFlowTask As MainPipe = TryCast(dataFlowTaskHost.InnerObject, MainPipe)
' Source OLE DB connection manager to the package.
Dim SconMgr As ConnectionManager = package.Connections("LocalHost.OLTP")
' Create and configure an OLE DB source component.
Dim source As IDTSComponentMetaData100 = dataFlowTask.ComponentMetaDataCollection.[New]()
source.ComponentClassID = "DTSAdapter.OLEDBSource.2"
' Create the design-time instance of the source.
Dim srcDesignTime As CManagedComponentWrapper = source.Instantiate()
' The ProvideComponentProperties method creates a default output.
srcDesignTime.ProvideComponentProperties()
source.Name = "Employee Dim from OLTP"
' Assign the connection manager.
source.RuntimeConnectionCollection(0).ConnectionManagerID = SconMgr.ID
source.RuntimeConnectionCollection(0).ConnectionManager = DtsConvert.GetExtendedInterface(SconMgr)
' Set the custom properties of the source.
srcDesignTime.SetComponentProperty("AccessMode", 0)
' Mode 0 : OpenRowset / Table - View
srcDesignTime.SetComponentProperty("OpenRowset", "[dbo].[Employee_Dim]")
' Connect to the data source, and then update the metadata for the source.
srcDesignTime.AcquireConnections(Nothing)
srcDesignTime.ReinitializeMetaData()
srcDesignTime.ReleaseConnections()
Thanks in advance!
The C# code here is what you need if you need a Derived Column transform between the Source and Destination...
http://bifuture.blogspot.com/2011/01/ssis-adding-derived-column-to-ssis.html
To get the Source & Destination connections working, there is some secret sauce here to get things working between COM and .Net...
http://blogs.msdn.com/b/mattm/archive/2008/12/30/api-sample-ado-net-source.aspx
There is a similar page showing what to do for OleDB connections too.
Creating the source tables is easy. The available ODBC Metadata collections accessible should be retrieved with GetSchema("MetaDataCollections"). This will return a list of the available schema collections available for that particular ODBC driver.
Next, you'll want to see the data types returned from GetSchema("DataTypes"), so you can correctly interpret the data types for each column retrieved from GetSchema("Columns") to make your SQL Server create table script (which I'm assuming you've done).
To at least figure out which tables have primary keys, you'll need to loop over each table returned from GetSchema("Tables") in order to work with GetSchema("Indexes"). There's a bug that requires you to query the Indexes one table at a time. It is easy to google this - create a string array to pass in as the 3rd parameter: GetSchema("Indexes", tblName, resultArray[])
What I did was got the Tables and Columns collections into object variables in my parent SSIS package. Because Timberline is so fast (not), it seemed more efficient to pull all the columns down and filter them locally...which I do to create the tables in SQL Server, if necessary.
Once that is done, use the local copy of Tables again to manipulate a SSIS package in a Script task in "design mode" (change source and destination target tables, and redo the column mappings), and execute the now-in-memory SSIS package.
For me it took awhile to figure out. Both above URLs were required. I found and copied the .Net 2.0 Dts.PipelineWrap and Dts.RuntimeWrap .dlls to Microsoft.Net\FrameworkV2.0xxxxx folder, then referenced these in each script task wanting to use them, before setting up my "using DtsPW = Microsoft.SqlServer.Dts.Pipeline.Wrapper", etc.
Of note, because Timberline is 32-bit ODBC, I think it's necessary to build the SSIS package to use "X86", and target the script tasks to use .Net 2.0 framework.
I used the Derived Column code because I needed to copy multiple Timberline DBs into one SQL Server DB. Derived Column adds a "CompanyID" value to the output pipeline to SQL Server.
In the end, map the Destination's Virtual Input columns to its External Metadata columns, based off of the pipeline the Destination is attached to:
foreach (DtsPW.IDTSVirtualInputColumn100 vColumn in destVirtInput.VirtualInputColumnCollection)
{
var vCol = destInst.SetUsageType(destInput.ID, destVirtInput, vColumn.LineageID, DtsPW.DTSUsageType.UT_READWRITE);
destInst.MapInputColumn(destInput.ID, vCol.ID, destInput.ExternalMetadataColumnCollection[vColumn.Name].ID);
}
Anyways, that code will make more sense in the context of the bifuture.blogspot.com page.
The EzApi library could help with this too, but the AdoNet connection source for it is coded as a virtual class, so you'd need to implement specific classes to use. My C# kungfu is not strong enough for that in the time I have...
Also, CozyRoc sells a toolset with custom SSIS controls (data flow Source and Destination controls...) that looks like it does this on the fly input-to-output column mapping as well.
My package seems to work good enough now... Oh, and one more, I did not have luck trying to use DSN-less ODBC connections to Timberline, just: Dsn=dsnname;Uid=user;Pwd=pwd;
SSIS packages running in 64-bit land cannot see 32-bit DSNs on 64-bit OS, it seems...at least, it didn't work for me (win7-64, 32-bit Text ODBC DSN).