I am implementing a data flow in which I need to insert rows into two different sink and both of the sink are Azure SQL database tables.
The first sink i.e. the first Azure SQL database table has an auto generated ID column. After inserting values in the first table, we need to insert the values in the second sink which is an audit table.
Now the problem is that the identity column values generated in the first sink needs to be inserted in the second sink also. How can I get these auto incremented id column values to be inserted in the audit table.
By default, data is written to multiple sinks in a nondeterministic order. The execution engine writes data in parallel as the transformation logic is completed, and the sink ordering might vary each run. To specify an exact sink ordering, enable Custom sink ordering on the General tab of the data flow. When enabled, sinks are written sequentially in increasing order.
Refer below image for sink order.
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I would like to load incremental data from data lake into on premise SQL, so that i created data flow do the necessary data transformation and cleaning the data.
after that i copied all the final data sink to staging data lake to stored CSV format.
I am facing two kind of issues here.
when ever i am trigger / debug to loading my dataset(data flow full activity ), the first time data loaded in CSV, if I load second time similar pipeline, the target data lake, the CSV file loaded empty data, which means, the column header loaded but i could not see the any value inside file.
coming to copy activity, which is connected to on premise SQL server, i am trying to load the data but if we trigger this pipeline again and again, the duplicate data loaded, i want to load only incremental or if updated data comes from data lake CSV file. how do we handle this.
Kindly suggest.
When we want to incrementally load our data to a database table, we need to use the Upsert option in copy data tool.
Upsert helps you to incrementally load the source data based on a key column (or columns). If the key column is already present in target table, it will update the rest of the column values, else it will insert the new key column with other values.
Look at following demonstration to understand how upsert works. I used azure SQL database as an example.
My initial table data:
create table player(id int, gname varchar(20), team varchar(10))
My source csv data (data I want to incrementally load):
I have taken an id which already exists in target table (id=1) and another which is new (id=4).
My copy data sink configuration:
Create/select dataset for the target table. Check the Upsert option as your write behavior and select a key column based on which upsert should happen.
Table after upsert using Copy data:
Now, after the upsert using copy data, the id=1 row should be updated and id=4 row should be inserted. The following is the final output achieved which is inline with expected output.
You can use the primary key in your target table (which is also present in your source csv) as the key column in Copy data sink configuration. Any other configuration (like source filter by last modified configuration) should not effect the process.
The problem I'm trying to tackle is inserting and/or updating dynamic tables in a sink within an Azure Data Factory data flow. I've managed to get the source data, transform it how I want it and then send it to a sink. The pipeline ran successfully and it said it copied 37 rows (as expected) but investigation showed that no data was actually deposited in the target table. This was because the Table Action on the sink was set to 'None'. So in trying to fix this last part, it seems I don't have the 'Create' option but do have the 'Recreate' option (see screenshot of the sink below) which is not what I want as the datasource will eventually only have changed data. I need the process to create the table if it doesn't exist and then Upsert data. (Recreate drops the table and then creates it).
If I change the sink type from Inline to Dataset, then I can select Insert and Upsert, etc options but this is then not dynamic as I need to select a specific dataset.
So has anyone come across the same issue and have you managed to have dynamic sinks in your data flow where the table is created if it doesn't exist, then upsert data.
I guess I can add a Pre SQL script which takes care of the 'create the table if it doesn't exist' but I still can't select the Upsert option with inline tables.
For the CREATE TABLE IF NOT EXISTS issue, I would recommend a Stored Procedure that is executed in the pipeline prior to the Data Flow.
For Inline vs Dataset, you can make the Dataset very flexible:
So still based on your runtime table name and no schema, so no need to target a specific table.
For the UPSERT issue, make sure you have an AlterRow activity before the Sink:
I have about 100 tables to which we replicate data, e.g. from the Oracle database.
I would like to quickly check that the data replicated to the tables in db2 is the same as in the source system.
Does anyone have a way to do this? I can create 100 transformations, but that's monotonous and time consuming. I would prefer to process this in a loop.
I thought I would keep the queries in a table and reach into it for records.
I read the data from Table input (sql_db2, sql_source, table_name) and write do copy rows to result. Next I read single record and I read a single record and put it into a loop.
But here came a problem because I don't know how to dynamically compare the data for the tables. Each table has different columns and here I have a problem.
I don't know if this is also possible?
You can inject metadata (in this case your metadata would be the column and table names) to a lot of steps in Pentaho, you create a transformation to collect the metadata to inject to another transformation that has only the steps and some basic information, but the bulk of the information of the columns affected by the different steps is in the transformation injecting the metadata.
Check Pentaho official documentation about Metadata Injection (MDI) and the sample with a basic example of metadata injection available in your PDI installation.
I'm testing the data flow on my Azure Data Factory. I created Data Flow with the following details:
Source dataset linked service - from CSV files dataset from Blob storage
Sink linked service - Azure SQL database with pre-created table
My CSV files are quite simple as they contain only 2 columns (PARENT, CHILD). So, my table in SQL DB also have only 2 columns.
For the sink setting of my data flow, I have allowed insert data and leaving other options as default.
I have also mapped the 2 columns for input and output columns as per screenshot.
The pipeline with data flow ran successfully when I checked the result, I could see thqat 5732 rows were processed. Is this the correct way to check? As this is the first time I try this functionality in Azure Data Factory.
But, when I click on Data preview tab, they are all NULL value.
And; when I checked my Azure SQL DB in the table where I tried to insert the data from CSV files from Blob storage with selecting top 1000 rows from this table, I don't see any data.
Could you please let me know what I configured incorrectly on my Data Flow? Thank you very much in advance.
Here is the screenshot of ADF data flow source data, it does see the data on the right side as they are not NULL, but on the left side are all NULLs. I imagine that the right side are the data from the CSV from the source on the blob right? And the left side is the sink destination as the table is empty for now?
And here is the screenshot for the sink inspect input, I think this is correct as it reads the 2 columns correctly (Parent, Child), is it?
After adding Map drifted, to map "Parent" => "parent" and "Child" => "child"
I get this error message after running the pipeline.
When checking on sink data preview, I get this error message. It seems like there is incorrect mapping?
I rename the MapDrifted1 expression to "toString(byName('Parent1))" and Child1 as suggested.
The data flow executed successfully, however I still get NULL data in the sink SQL table.
Can you copy/paste the script behind your data flow design graph? Go to the ADF UI, open the data flow, then click the Script button on top right.
In your Source transformation, click on Data Preview to see the data. Make sure you are seeing your data, not NULLs. Also, look at the Inspect on the INPUT for your Sink, to see if ADF is reading additional columns.
I am trying to have a setup in Pentaho where :
My source data is in MySQL DB and target database is Amazon redshift.
I want to have incremental loads on Redshift database table, based on the last updated timestamp from MySQL DB table.
Primary key is student ID.
Can I implement this using update/insert in Pentaho ?
Insert/Update step in Pentaho Data Integration serves the purpose of inserting the row if it doesn't exist in the destination table or updating it if it's already there. It has nothing to do with incremental loads, but if your loads should be inserting or updating the record based on some Change Data Capture mechanism then this is the right step at the end of the process.
For example you could go one of two ways:
If you have a CDC then limit the data at Table Input for MySQL since you already know the last time a record has been modified (last load)
If you don't have a CDC and you are comparing entire tables then go for joining the sets to produce rows that has changed and then perform a load (slower solution)