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.
Related
I was trying to get data from On-prem hive Source to Azure data lake gen 2 using azure data factory.
As I need to get data for multiple tables I have created and file(ex: tnames.txt) with all my table names and stored in data lake gen 2.
In Azure Data Factory created a lookup activity and passed tnames.txt file to it.
Then added a foreach activity to that lookup actvity and in foreach activity added a copy activity.
In copy activity in source, I was giving query to extract data.
Sink is datalake gen 2.
Example code:
select * from tableName
Here table is dynamically passed from tnames.txt.
But after data is copied into data lak,e I am getting headers in copied data are like:
"tablename.columnname".
For example: Table name is Employee and few columns are ID, Name, Gender,....
My resultent file columns are like Employee.ID,Employee.Name,Employee.Gender, but my requirement is just column name.
Basically tabe name is append to column name.
How to solve this issue/Is there any other way to get data for multiple tables in single pipeline/copy activity?
Check the mapping tab of your copy activity . If the mapping is enabled, clear it and use auto-create table . It will auto-generate the schema according to the source schema. No need to explicitly create the table with defined schema. Let it be auto create table. It will generate required mapping automatically.
I have a bunch of JSON files coming into Azure data lake gen 2, the JSON files contains new data as well as updates.
The data needs to be merged into a SQL table so I can start to do some reporting. The way I solved the problem has been to create a Azure Data factory that looks like this
Create and copy to temp table:
First I use the copy data to take the JSON and create a table from the schema and dump the content into the table.
Create delivery table:
Creates a table with the right schema if it doesn't already exsist
Merge temp with delivery:
Here I use a merge clause to cast and merge the data from the table that was created at step 1 with the table from step 2.
Delete temp data:
Deletes the table from step 1
This data factory gets triggered each time there's a new file in the data lake.
The pipeline solves my problem but I feel like there's a lot of unnecessary overhead by creating and dropping a new table each time I process a file.
Is there a way to optimize this flow, maybe by merging the JSON directly to the "Delivery" table?
Thanks in advance
I have two sample files in blob as sample1.csv and sample2.csv as below
data sample
SQL table name sample2, with column Name,id,last name,amount
Created a ADF flow without schema, it results as below
preview data
source settings are allow schema drift checked.
sink setting are auto mapping turned on. allow insert checked. table action none.
I have also tried setting a define schema in dataset, its result are same.
any help here?
my expected outcome would be data in sample1 will inserted null into the column "last name"
If I understand correctly, you said: "my expected outcome would be data in sample1 will inserted null into the column last name", you only need to add a derived column to you sample1.csv file.
You could follow my steps:
I create a sample1.csv file in Blob Storage and a sample2 table in my SQL database:
Using DerivedColumn to create new column last name with null value:
expression: toString(null())
Sink settings:
Run the pipeline and check the data in table:
Hope this helps.
You cannot mix schemas in the same source in the same data flow execution.
Schema Drift will handle changes to the schema on an execution-per-execution basis.
But if you are reading multiple different schemas from a folder, you will get non-deterministic results.
Instead, if you loop through those files in a pipeline ForEach one-by-one, data flow will be able to handle the evolving schema.
I'm working on an SSIS project that pulls data form Excel and loads to Oracle Database every month. I plan to pull data from Excel file and load to Oracle stage table. I will be using a merge statement because the data that gets loaded each month is a rolling 12 month list and the data can change, so need to be able to INSERT when records don't match or UPDATE when they do. My control flow looks like this: Truncate Stage Table (to clear out table from last package run)---> DATA FLOW from Excel to Stage Table---> Merge to Target Table in Oracle.
My problem is that the data in the source Excel file doesn't have any unique columns to select a primary key or a composite key, as it is a possibility (although very unlikely) that a new record could have the exact same information. I am unable to utilize the "generated always as identity" because my SSIS package needs to truncate at the beginning of each job to clear out the Stage Table. This would generate the same ID numbers in the new load and create problems in the Target Table.
Any suggestions as to how I can get around this problem?
Welcome to SO and ETL. Instead of using a staging table, in SSIS use two sources: Excel file and existing production table. Sort both inputs and then perform a merge join on the unique identifier. From there, use a derived column transformation to add a new column called 'Action' which will mark a row as either an INSERT/UPDATE/DELETE based on whether the join key is NULL. So:
NULL from file means DELETE (not in file, in database)
NULL from database means INSERT (in file, not in database)
Not NULL for both means UPDATE (in file, in database)
From there, use a conditional split to split rows to either a OLE DB Destination (INSERT), or SQL Command (UPDATE or DELETE). You can now remove the stage environment and MERGE command from your process. This has the added benefit of removing the ETL load from the SQL Server, assuming SSIS is running on a separate server.
Note: The sort transformation has the option to remove duplicates.
SQL Server 2012: using a SSIS package, how to validate the source records for duplicate before inserting?
Our source file is a .csv. We are facing duplicate records loaded in the staging table.
At present , we are following manual process of loading data.
How to validate the source file data against the destination table before loading and load only the valid records? Possibility of loading duplicate records not only because of the source file having duplicate records in it but also reloading the same file to the staging table.
We are not Truncate the staging table. We are keeping records as is.
Second question : How to pick the name of the source file and pass it in the loading ? Possibly having a derived column as "FileName" which will get loaded along with raw data to the staging table.
The typical load pattern I use in this case is:
Prepare a staging table that matches the source file
In SSIS run a SQL Task with TRUNCATE StagingTable; (which clears it out)
Then, run a data flow task that loads the entire data file into the staging table
Lastly, merge the staging table into the final table.
I prefer to do this last step in a SQL Task also:
INSERT INTO FinalTable
(PrimaryKey,Column1,Column2,Column3)
SELECT
PrimaryKey,Column1,Column2,Column3
FROM StagingTable SRC
WHERE NOT EXISTS (
SELECT * FROM FinalTable TGT WHERE TGT.PrimaryKey=SRC.PrimaryKey
);
If you prefer a graphical UI, and you don't mind the extra network traffic, and slower processing time, you can do the same type of merge operation using lookups. You can even use the SCD component but I strongly discourage it's use.
Whether you do it in T-SQL or the UI, you need a key that can be used to uniquely identify the records (referred to as PrimaryKey in my example). If you don't have this key, there is no way to 'deduplicate'
Note in this example you have a 'real' staging table whose only purpose is to get the data file into the database. Then you have a final table that contains the final consistent result
Also note that this pattern only adds new rows - it will not update existing rows if they change in the data file.
Given your exact scenario (of loading the same file again), I would first check if the data is even loaded to the staging table. If you do that, you don't have to worry about checking the duplicates at record level.
How are you setting the connection to the file? Most of the data loads I have dealt with, I designed for-each-loop-container where the file name/path would be populated in a user variable. As you said, you could just use a derived column transform to add a new column which gets the value from a variable. If you don't have the file name in a user variable, you could use expression task in the control flow to populate it.
To cover your exact requirement, I would use the above step to populate the file name in the table. You could even normalize to a different table instead of storing long file name for every data record. Once you have all the file names in the database, you could just have an "Execute SQL" at the beginning to see if that file name is already in the database.
Two years back I have faced the same problem with importing TSV files.
I tried many other solutions but best I could design is C# code script for such validation at its best.
What I did as a solution
Create one C# DataTable object in memory with Primary Key constraints,
like:-
DataColumn[] keyColumn = new DataColumn[30];
keyColumn[intJ] = dtFilterdPK.Columns["Column name"];
Then try to add one by one row from your CSV to this DataTables.
Whenever your data will get Duplication based on Primary Key will have an error
Handle this error code in (TRY)..CATCH block and make this duplication error as per your logging requirement.
Avoid those error records importing in DataTable object.
Atlast import your CSV file into your table as BulkImport
Like:
using (SqlBulkCopy bulkCopy = new SqlBulkCopy(myConnection))
{
bulkCopy.DestinationTableName = "Your DB Table Name"; //Assign table name
bulkCopy.WriteToServer(dtToBeImport); //Write into Actual table.
}
Hope this will help you.