12/11/2023 0 Comments Sqlpro csv import mapping![]() ![]() Seems an oversight to me, poor specifications, and a developer that locked down on specific tasks without thinking how people might use this. My guess is that the wizard is so basic that no one programmed in the logic to deal with an existing table. I'm not sure why there's a restriction here. If I try to re-run this and pick a different flat file and use the same table name, I get this: However, each time I run this, I get a different table name, which means I then need 12 statements to consolidate this data together. This worked well, and I had a strange staging table. In reality, I later realized I didn't care about most of these fields, but to get started, I needed to load them up.Īfter this I get the summary and can click Finish to load the data. The one issue is the ID field, which really needs to be a string type. In my case, the nvarchar() works well for these fields. Here I can change the types, set a PK, nulls, etc. The next step in the wizard is the datatype mapping for the table. There's only 5 rows here, so there isn't a lot to sample. I could have used rich data type detection here, but not sure that would be better. I suspect these are column names as the CSV likely has some keyword clashes. However, you can see there is a warning that some names were changed. I scrolled over and didn't show everything below as some of this is PII data. I can't select more than one file, so this is really a one-off type wizard. It suggests one, and a schema, but I can change these. After starting the wizard, I pick the file and give a table name. This wizard is simple, and it allows you to pick a file and create a table. It's 12 files, and by the time I actually program something, I could easily run through the wizard 12 times. Then I decided to use the Import Flat File Wizard to load my data. This shouldn't be too bad, right? Let's get started. The Paypal data was simpler and more structured, similar to this: "Date","Time","TimeZone","Name","Type","Status","Currency","Amount","Receipt ID","Balance" When I opened this in Sublime Text to get an idea of the structure, I saw this: Username,ID,Datetime,Type,Status,Note,From,To,Amount (total),Amount (fee),Funding Source,Destination,Beginning Balance,Ending Balance,Statement Period Venmo Fees,Terminal Location,Year to Date Venmo Fees,Disclaimer Here's an example of a field inside the CSV.Īpparently I won't be importing things into varchar() fields. There were also some lovely sections in the middle with interesting characters. When I looked at the Venmo stuff, it was a lot of fields, with a header row containing the starting balance and a final row with the ending balance. The first step is often to look at the data. At least I'll only be cursing Paypal once during this process. The Paypal data had monthly PDFs, but one csv for the year. Each lovingly named something like venmo_statement (1).csv, venmo_statement (2).csv, etc. ![]() The Venmo one had 12 files, one for each month. One was from Venmo and the other from Paypal. In this case, my wife emailed me 2 zip files with data. When I get your CSV, I'm usually not pleased. I usually export things in a way that make it easy for my to import them. Like some of you, I don't like getting CSV data from others. This article looks at some of the hassles and frustrations of working with CSVs in SQL Server, some of which I think are quite unnecessary. I offered to help, thinking this would be just a few minutes to load and then export the data. She had a number of exports from various bank and accounting systems that she wanted to consolidate and run reports again. In this case, the request was from my wife, to help her with business taxes. Whether I was a DBA or developer, often someone gets a little data and wants it cleaned up and in a structured format to easily run a report or analyze the information. It's a similar type of request that I've had come across my desk over the years from many sources. This was an ad hoc, semi-one-off request. ![]() ![]() This wasn't a new process, or an application that needed enhancing, or even an ongoing ETL (Extract, Transform, Load) process that needed to be built. Like many of you, I recently had to import some data in a CSV (comma-separated file) into SQL Server. ![]()
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