I am developing a report in SSRS.
My report has around 50 row headers. Data for each row header is the reult of a complex query to the database.
2 row header may/may not have data that relates to another row header.
In this case what would be the best way to create the report?
-- Do we create a procedure that gets all data to a temporary table and then generate the report using this temp table?
-- Do we create multiple datasets for this report.
Please advice on what would be the best way to proceed.
I read somewhere about using Link wherein data is retrieved from the post gre database (project uses postGreSql db) to the local sql server that SSRS provides.
Report then retrieves data from the local sql server to generate the report.
Thoughts?
You are best using a stored procedure as the source. It is easy to optimize the stored procedure so as to get the best performance so the report runs fast.
Assembling all your data so that you can use a single dataset to represent it would be the way to go.
Related
We curate data in the "Dev" Azure SQL Database and then currently use RedGate's Data Compare tool to push up to 6 higher Azure SQL Databases. I am trying to migrate that manual process to ADFv2 and would like to avoid copy/pasting the 10+ copy data actives for each database (x6) to keep it more maintainable for future changes. The static tables have some customization in the copy data activity but the basic idea follows this post to perform an upsert.
How can the implementation described above be done in Azure Data Factory?
I was imagining something like the following:
Using one parameterized link service that has the server name & database name configurable to generate a dynamic connection to Azure SQL Database.
Creating a pipeline for each table's copy data activity.
Creating a master pipeline to then nest each table's pipeline in.
Using variables loop over the different connections an passing those to the sub-pipelines parameters.
Not sure if that is the most efficient plan or even works yet. Other ideas/suggestions?
we can not tell you if that's the most efficient plan. But I think so. Just make it works.
As you said in the comment:
we can use Dynamic Pipelines - Copy multiple tables in Bulk with
'Lookup' & 'ForEach'. we can perform dynamic copies of your data
table lists in bulk within a single pipeline. Lookup returns either
the lists of data or first row of data. ForEach - #activity('Azure
SQL Table lists').output.value ;
#concat(item().TABLE_SCHEMA,'.',item().TABLE_NAME,'.csv') + This is
efficient and cost optimized since we are using less number of
activities and datasets.
In usually, we also will choose same solution with you: dynamic parameter/pipeline, lookup + foreach active to achieve the scenario. In one word, make the pipeline has a strong logic, simple and efficient.
Added the same info mentioned in the Comment as Answer.
Yup, we can use Dynamic Pipelines - Copy multiple tables in Bulk with 'Lookup' & 'ForEach'.
We can perform dynamic copies of your data table lists in bulk within a single pipeline. Lookup returns either the lists of data or first row of data.
ForEach - #activity('Azure SQL Table lists').output.value ;
#concat(item().TABLE_SCHEMA,'.',item().TABLE_NAME,'.csv')
This is efficient and cost optimized since we are using less number of activities and datasets.
Attached pic as ref-
I have a report that I generate on a weekly basis. I have the code written in SQL and I then pull all the data into excel's data model.
I then create pivot tables and dashboards in excel from that particular data.
The SQL code creates new table of the same name everytime and deletes the older version of the table. There isn't any way for me to just append the new data as the report is run from the very start and not just on the new data.
I wish to automate this process of refreshing my dashboard from the data I produce in SQL. Is there a way to do so?
Currently I create a new table in SQL, import data into the excel's data model and then recreate the dashboard.
I am not even sure if this is possible. Any help would be greatly appreciated!
Solved!
After some digging, I was able to find a feature that Excel's data model supports.
Instead of making a connection directly to a SQL Server Table, you can create a connection by writing a SQL Query.
This way, even if you delete the table for updating it, as far as the name remains the same, Excel's data model would be able to pull data from the table just by you hitting refresh!
Our challenge is the following one :
in an Azure SQL database, we have multiple tables with the following table names : table_num where num is just an integer. These tables are created dynamically so the number of tables can vary. (from table_1, table_2 to table_N) All tables have the same columns.
As part of a U-SQL script file, we would like to execute the same query on all of these tables and generate an output csv file with the combined results of all these queries.
We tried several things :
U-SQL does not allow looping so we were thinking creating a View in our Azure SQL database that would combine all the tables using a cursor of some sort. Then, the U-SQL file would query this View (using external source). However, a View in Azure SQL database can only be created via a function and a function cannot execute dynamic SQL or even call a stored procedure...
We did not find a way to call a stored procedure of the external data source directly from U-SQL
we dont want to update our U-SQL job each time a new table is added...
Is there a way to do that in U-SQL through a custom extractor for instance? Any other ideas?
One solution I can think of is to use Azure Data Factory (v2) to assist in this.
You could create a pipeline with the following activities:
Lookup activity configured to execute the stored procedure
For Each activity that uses the output of the lookup activity as a source
As a child item use a U-Sql Activity that executes your U-Sql script which writes the output of a single table (the item of the For Each activity) to blob or datalake
Add a Copy Activity that merges the blobs from step 2.1 to one final blob.
If you have little or no experience working with ADF v2 do mind that it takes some time to get to know it but once you do, you won't regret it. Having a GUI to create the pipeline is a nice bonus.
Edit: as #wBob mentions another (far easier) solution is to somehow create a single table with all rows since all dynamically generated table have the same schema. You can create a stored procedure for populating this table for example.
I'm looking for a way to speed up the following process: I have a SSIS package that loads data from Excel files on a weekly basis to SQL Server. There are 3 fields: Brand, Date, Value.
In the dataflow, I check for existing combinations of Brand+Date, and new combinations go to the table directly, the existing ones go to a RecordSet destination for updates:
The next step is to update the Value of the existing combinations:
As you can see, there are thousands of records to update, and it takes too long. The number of records tend to grow week by week. Please suggest.
The fastest way will be do this inside a Stored procedure using ELT (Extract Load Transform) approach.
Push all data from excel as is into a table(called load to a staging table in theory). Since you do not seem to be concerned with data validation steps, this table can be a replica of final destination table columns.
Next step is to call a stored procedure using Execute SQL task. Inside this procedure you can put all your business logic. Since this steps with native data manipulation on SQL server entities, it is the fastest alternative.
As a last part, please delete all entries from the staging table.
You can use indexes on staging table to make the SP part even faster.
I have a report which uses some tables with large data. We Wrote a stored procedure to get the required data from the tables and prcoess the report output table. Since the data volume is large, we dont want to call the procedure to call entire source tables data whenever any updates happened in them. We need to update only the 'updated/changed rowsin source table into destinationtable.
what is best way to do this in Sql Server?
Thanks for the Help.
You can definetely use table triggers
Please check SQL Server Trigger Example to Log Changes
There is also a solution called CDC (Change Data Capture) in SQL Server
You can also investigate CDC