I have a file format from my customer to load.
File is pivoted format and 2 level of column. (PointName & Size ). Also columns may vary time to time as more points may get added later. This should be handled dynamically.
I know the SSIS UnPivot transformations may not work due to dynamic column nature(File Meta-data may vary with new file) also 2 level of columns.
Request your help in case if anyone has come across this kind of file load requirements.
Adding the expected output file/table format below.
Related
I have a CSV file that I am using as a source in SSIS. The file contains additional blank columns in the file
There is an additional column in S, U, V; is there a way I can remove the column through SSIS Script Task before using it as a source file.
Perhaps I misunderstand the problem, but you have a CSV with blank columns that you'd like to import into SQL Server but you do not want to import the blank columns into your table.
So, don't create the target columns in your table. That's it. There's no problem with having "extra" data in your data pipeline, just don't land it.
The Flat File Connection manager must* have a definition for all the columns. As it appears you do not headers for the blank columns and a column name is required, you will need to set up the flat file connection manager as my file has no header columns but then skip 1 row to avoid the header row. It might be a data starts on line setting - doing this from memory. By specifying no header row, that means you need to manually provide your column names. I favor naming them something obvious like IgnoreBlankColumn_S, IgnoreBlankColumn_U, IgnoreBlankColumn_V that way future maintainers will know this is an intentional design decision since the source data has no data.
*You can write a query against a text file which would allow you to only pull in specific columns but this is not going to be worth the effort.
I am looking to refresh a data set in quicksight, this is in Spice. The data set comes from a csv file that has been updated and now has more data than the original file I uploaded.
I can't seem to find a way to simply repoint to the same file with same format. I know how to replace the file but whenever i do this it states that it can't create some of my calculated fields and so drops multiple rows of data!
I assume I'm missing something obvious but I can't seem to find the right method or any help on the issue.
Thanks
Unfortunately, QuickSight doesn't support refreshing file data-sets to my knowledge. One solution, however, is to put your CSV in S3 and refresh from there.
The one gotcha with this approach is that you'll need to create a manifest file pointing to your CSV. This isn't too difficult and the QuickSight documentation is pretty helpful.
You can replace the datasource by going into the Analysis and clicking on the pencil icon as highlighted in Step 1. By replacing dataset, you will not lose any new calculated fields that might have been calculated already on the old dataset.
If you try to replace the data source by going into the Datasets as highlighted below, you'll lose all calculated fields and modifications etc
I don't know when this was introduced but you can now do this exact thing through the "Edit Dataset", starting either from the Dataset page or from the 'pencil' -> Edit dataset inside an Analysis. It is called "update file" and will result in an updated dataset (additional or different data) without losing anything from your analysis including calculated fields, filters etc.
The normal caveat applies in that the newer uploaded file MUST contain the same column names and datatypes as the original - although it can also contain additional columns if needed.
Screenshots:
I have a file like as seen below: Just Ex:
kwqif h;wehf uhfeqi f ef
fekjfnkenfekfh ijferihfq eiuh qfe iwhuq fbweq
fjqlbflkjqfh iufhquwhfe hued liuwfe
jewbkfb flkeb l jdqj jvfqjwv yjwfvjyvdfe
enjkfne khef kurehf2 kuh fkuwh lwefglu
gjghjgyuhhh jhkvv vytvgyvyv vygvyvv
gldw nbb ouyyu buyuy bjbuy
ID Name Address
1 Andrew UK
2 John US
3 Kate AUS
I want to dynamically skip header information and load flatfile to DB
Like below:
ID Name Address
1 Andrew UK
2 John US
3 Kate AUS
The header information may vary (not fixed no. of rows) from file to file.
Any help..Thanks in advance.
The generic SSIS components cannot meet this requirement. You need to code for this e.g. in an SSIS Script task.
I would code that script to read through the file looking for that header row ID Name Address, and then write that line and the rest of the file out to a new file.
Then I would load that new file using the SSIS Flat File Source component.
You might be able to avoid a script task if you'd prefer not to use one. I'll offer a few ideas here as it's not entirely clear which will be best from your example data. To some extent it's down to personal preference anyway, and also the different ideas might help other people in future:
Convert ID and ignore failures: Set the file source so that it expects however many columns you're forced into having by the header rows, and simply pull everything in as string data. In the data flow - immediately after the source component - add a data conversion component or conditional split component. Try to convert the first column (with the ID) into a number. Add a row count component and set the error output of the data conversion or conditional split to be redirected to that row count rather than causing a failure. Send the rest of the data on its way through the rest of your data flow.
This should mean you only get the rows which have a numeric value in the ID column - but if there's any chance you might get real failures (i.e. the file comes in with invalid ID values on rows you otherwise would want to load), then this might be a bad idea. You could drop your failed rows into a table where you can check for anything unexpected going on.
Check for known header values/header value attributes: If your header rows have other identifying features then you could avoid relying on the error output by simply setting up the conditional split to check for various different things: exact string matches if the header rows always start with certain values, strings over a certain length if you know they're always much longer than the ID column can ever be, etc.
Check for configurable header values: You could also put a list of unacceptable ID values into a table, and then do a lookup onto this table, throwing out the rows which match the lookup - then if you need to update the list of header values, you just have to update the table and not your whole SSIS package.
Check for acceptable ID values: You could set up a table like the above, but populate this with numbers - not great if you have no idea how many rows might be coming in or if the IDs are actually unique each time, but if you're only loading in a few rows each time and they always start at 1, you could chuck the numbers 1 - 100 into a table and throw away and rows you load which don't match when doing a lookup onto this table.
Staging table: This is probably the way I'd deal with it if I didn't want to use a script component, but in part that's because I tend to implement initial staging tables like this anyway, and I'm comfortable working in SQL - so your mileage may vary.
Pick up the file in a data flow and drop it into a staging table as-is. Set your staging table data types to all be large strings which you know will hold the file data - you can always add a derived column which truncates things or set the destination to ignore truncation if you think there's a risk of sometimes getting abnormally large values. In a separate data flow which runs after that, use SQL to pick up the rows where ID is numeric, and carry on with the rest of your processing.
This has the added bonus that you can just pick up the columns which you know will have data you care about in (i.e. columns 1 through 3), you can do any conversions you need to do in SQL rather than in SSIS, and you can make sure your columns have sensible names to be used in SSIS.
What is the use of Multicast Transformation Task ? With this task, is it possible to send to two destinations from a single source, while each destination has different columns ?
I assume that you are referring to Multicast Transformation inside the Data Flow task. If so, yes it is possible. The purpose of the transformation is to channel data from a single source to n number of Transformation tasks or Destinations.
If source has following columns
Source
Column 1
Column 2
Column 3
and destinations have these columns.
Destination 1 Destination 2
Column 1 Column 2
Column 3
Both destinations will be able to see Columns 1 - 3 that are available in Source. You have to map the columns accordingly in the respective destinations. Refer below example:
Example:
Screenshot #1 shows that Source has two columns Header and Value.
Screenshot #2 shows that Destination 1 has both columns Header and Value. They are mapped accordingly.
Screenshot #3 shows that Destination 2 has only column Header. It is mapped accordingly.
Screenshot #4 shows sample package execution.
Hope that helps.
Screenshot #1:
Screenshot #2:
Screenshot #3:
Screenshot #4:
#Siva did a good job of explaining the how. I'm going to tackle the "What is the use of Multicast Transformation Task?" question.
Let me give you examples of how I have used it or seen it used. First, we like to store the data in a staging table that contains just the raw unchanged data (this makes it easier for us to research data issues to see if the data problem came from a bug in our process or bad data sent by the client.) and at the same time I want to send the same data to another staging table that will be used to transform the data.
Sometimes we use Mulitcast to take denormalized files and send them to normalized data tables. So the names go to the person table, the addresses go to the address table and the phones go to the phone table.
Multicast can be used to do several different transformations on different data fields in the same source at the same time rather than one at a time and then bring all the revised data back together in a Merge join. So one path checks the States to make sure they are valid or converts the long names to the 2 character abbreviations and another checks the zip codes and adds the leading zeros that got lost because the data came from an Excel file. Then the cleaned address data is put back together with the correct values we want for insertion to our database. This can speed up cleaning as data is being scrubbed simultaneously not one step at a time.
I have flat file that structured in a hierarchical format that looks something like this:
Area|AreaCode|AreaDescription
Region|RegionCode|RegionDescriptoin
Zone|ZoneCode|ZoneDescription
District|DistrictCode|DistrictDescription
Route|RouteCode|RouateDescription
Record|Name|Address|Ect
RouteFooter
Route|RouteCode|RouateDescription
Record|Name|Address|Ect
RouteFooter
DistrictFooter
District|DistrictCode|DistrictDescription
Route|RouteCode|RouateDescription
Record|Name|Address|Ect
Record|Name|Address|Ect
RouteFooter
Route|RouteCode|RouateDescription
Record|Name|Address|Ect
RouteFooter
DistrictFooter
ZoneFooter
RegionFooter
AreaFooter
I have to bring this into SSIS and consume information about the Record row and also about the header for the current record row. As well as information from several other sources and output a more simple flat file as a result.
I would like to read the flat file above into a structure that each row contains a record with the appropriate header information included.
My question is, what is the best way to do this if it is even possible?
First how do you tell what type of line you are on if you are on say line 3,987,986? How do you tell what is related to what? Is there apossiblity you could get this in a better format? Before spending lots of time (and don't kid yourself, this will take lots of time to set up and test properly) I would kick the file back to the provider and request it in a different format. You won't always get it, but you should at least try.
When I have done this in the past in DTS, the first characters of each line told me which structure the line referred to. I imported all into a staging table with two columns, one for the recordtype data and one for the rest. Then I parsed the rest into the staging tables for the type of record with the correct column structure for that type of record (and any fileds you might need to do the relationships) and then did clean up and then imported to prod tables. AS you also have differnt number of columns I would try that approach (only you may have to manually populate some columns instead of figuring out directly from the file), also give each record an identity filed in the staging tables. this will help you figure out the realtionships I think.