I am trying to import a small table of data from Azure SQL into Snowflake using Azure Data Factory.
Normally I do not have any issues using this approach:
https://learn.microsoft.com/en-us/azure/data-factory/connector-snowflake?tabs=data-factory#staged-copy-to-snowflake
But now I have an issue, with a source table that looks like this:
There is two columns SLA_Processing_start_time and SLA_Processing_end_time that have the datatype TIME
Somehow, while writing the data to the staged area, the data is changed to something like 0:08:00:00.0000000,0:17:00:00.0000000 and that causes for an error like:
Time '0:08:00:00.0000000' is not recognized File
The mapping looks like this:
I have tried adding a TIME_FORMAT property like 'HH24:MI:SS.FF' but that did not help.
Any ideas to why 08:00:00 becomes 0:08:00:00.0000000 and how to avoid it?
Finally, I was able to recreate your case in my environment.
I have the same error, a leading zero appears ahead of time (0: 08:00:00.0000000).
I even grabbed the files it creates on BlobStorage and the zeros are already there.
This activity creates CSV text files without any error handling (double quotes, escape characters etc.).
And on the Snowflake side, it creates a temporary Stage and loads these files.
Unfortunately, it does not clean up after itself and leaves empty directories on BlobStorage. Additionally, you can't use ADLS Gen2. :(
This connector in ADF is not very good, I even had problems to use it for AWS environment, I had to set up a Snowflake account in Azure.
I've tried a few workarounds, and it seems you have two options:
Simple solution:
Change the data type on both sides to DateTime and then transform this attribute on the Snowflake side. If you cannot change the type on the source side, you can just use the "query" option and write SELECT using the CAST / CONVERT function.
Recommended solution:
Use the Copy data activity to insert your data on BlobStorage / ADLS (this activity did it anyway) preferably in the parquet file format and a self-designed structure (Best practices for using Azure Data Lake Storage).
Create a permanent Snowflake Stage for your BlobStorage / ADLS.
Add a Lookup activity and do the loading of data into a table from files there, you can use a regular query or write a stored procedure and call it.
Thanks to this, you will have more control over what is happening and you will build a DataLake solution for your organization.
My own solution is pretty close to the accepted answer, but I still believe that there is a bug in the build-in direct to Snowflake copy feature.
Since I could not figure out, how to control that intermediate blob file, that is created on a direct to Snowflake copy, I ended up writing a plain file into the blob storage, and reading it again, to load into Snowflake
So instead having it all in one step, I manually split it up in two actions
One action that takes the data from the AzureSQL and saves it as a plain text file on the blob storage
And then the second action, that reads the file, and loads it into Snowflake.
This works, and is supposed to be basically the same thing the direct copy to Snowflake does, hence the bug assumption.
Related
In source csv file the data contains white spaces. How to remove those without using any transformation tool and just using Azure Data Factory. I tried "For each" activity on copy activity but the For each #items is of JSON array and string functions doesn't apply on it. Also, Data factory does not support custom functions and expressions. Is there any way to remove the white spaces from the source or during the copy process to the sink? Source and Sink are "Azure Files".
If not all the csv data contains white spaces, as I know about DF and per my experience, it's impossible to achieve that data conversion only with Copy active! Using data flow or others tools is very easy.
There isn't a way to achieve this using ADF only or directly.
HTH.
The most performant way to achieve this would be to temporarily stage the data in Azure SQL or Cosmos DB and then trim each column with an explicit SELECT statement as the source of the subsequent Copy activity moving the data to your sink file.
I Googled for a solution to create a table, using Databticks and Azure SQL Server, and load data into this same table. I found some sample code online, which seems pretty straightforward, but apparently there is an issue somewhere. Here is my code.
CREATE TABLE MyTable
USING org.apache.spark.sql.jdbc
OPTIONS (
url "jdbc:sqlserver://server_name_here.database.windows.net:1433;database = db_name_here",
user "u_name",
password "p_wd",
dbtable "MyTable"
);
Now, here is my error.
Error in SQL statement: SQLServerException: Invalid object name 'MyTable'.
My password, unfortunately, has spaces in it. That could be the problem, perhaps, but I don't think so.
Basically, I would like to get this to recursively loop through files in a folder and sub-folders, and load data from files with a string pattern, like 'ABC*', and load recursively all these files into a table. The blocker, here, is that I need the file name loaded into a field as well. So, I want to load data from MANY files, into 4 fields of actual data, and 1 field that captures the file name. The only way I can distinguish the different data sets is with the file name. Is this possible? Or, is this an exercise in futility?
my suggestion is to use the Azure SQL Spark library, as also mentioned in documentation:
https://docs.databricks.com/spark/latest/data-sources/sql-databases-azure.html#connect-to-spark-using-this-library
The 'Bulk Copy' is what you want to use to have good performances. Just load your file into a DataFrame and bulk copy it to Azure SQL
https://docs.databricks.com/data/data-sources/sql-databases-azure.html#bulk-copy-to-azure-sql-database-or-sql-server
To read files from subfolders, answer is here:
How to import multiple csv files in a single load?
I finally, finally, finally got this working.
val myDFCsv = spark.read.format("csv")
.option("sep","|")
.option("inferSchema","true")
.option("header","false")
.load("mnt/rawdata/2019/01/01/client/ABC*.gz")
myDFCsv.show()
myDFCsv.count()
Thanks for a point in the right direction mauridb!!
Currently I'm importing a CSV file into an Azure SQL database automatically each morning at 3 am, but the file has several blank lines in the csv file that are imported as rows which is cleaned up after the data is ingested.
There isn't a way to correct the file prior to ingestion, so I need to transform the data once it's been ingested and would like to avoid having to do this manually.
Is using something like Azure Data Factory the best approach to doing this? Or is there a less expensive / simpler way to simply remove blank lines via something akin to a stored procedure for Azure SQL Database?
I have Multiple CSV files in Folder
Example :
Member.CSv
Leader.CSv
I need to load them in to Data base tables .
I have worked on it using ForEachLoop Container ,Data FlowTask, Excel Source and OLEDB Destination
we can do if by using Expressions and Precedence Constraints but how can I do using Script task if I have more than 10 files ..I got Stuck with this one
We have a similar issue, our solution is a mixture of the suggestions above.
We have a number of files types sent from our client on a daily basis.
These have a specific filename pattern (e.g. SalesTransaction20160218.csv, Product20160218.csv)
Each of these file types have a staging "landing" table of the structure you expect
We then have a .net script task that takes the filename pattern and loads that data into a landing table.
There are also various checks that are done within the csv parser - matching number of columns, some basic data validation, before loading into the landing table
We are not good enough .net programmers to be able to dynamically parse an unknown file structure, create SQL table and then load the data in. I expect it is feasible, after all, that is what the SSIS Import/Export Wizard does (with some manual intervention)
As an alternative to this (the process is quite delicate), we are experimenting with a HDFS data landing area, then it allows us to use analytic tools like R to parse the data within HDFS. After that utilising PIG to load the data into SQL.
My problem statement is that I have a csv blob and I need to import that blob into a sql table. Is there an utility to do that?
I was thinking of one approach, that first to copy blob to on-premise sql server using AzCopy utility and then import that file in sql table using bcp utility. Is this the right approach? and I am looking for 1-step solution to copy blob to sql table.
Regarding your question about the availability of a utility which will import data from blob storage to a SQL Server, AFAIK there's none. You would need to write one.
Your approach seems OK to me. Though you may want to write a batch file or something like that to automate the whole process. In this batch file, you would first download the file on your computer and the run the BCP utility to import the CSV in SQL Server. Other alternatives to writing batch file are:
Do this thing completely in PowerShell.
Write some C# code which makes use of storage client library to download the blob and once the blob is downloaded, start the BCP process in your code.
To pull a blob file into an Azure SQL Server, you can use this example syntax (this actually works, I use it):
BULK INSERT MyTable
FROM 'container/folder/folder/file'
WITH ( DATA_SOURCE = 'ds_blob',BATCHSIZE=10000,FIRSTROW=2);
MyTable has to have identical columns (or it can be a view against a table that yields identical columns)
In this example, ds_blob is an external data source which needs to be created beforehand (https://learn.microsoft.com/en-us/sql/t-sql/statements/create-external-data-source-transact-sql)
The external data source needs to use a database contained credential, which uses an SAS key which you need to generate beforehand from blob storage https://learn.microsoft.com/en-us/sql/t-sql/statements/create-database-scoped-credential-transact-sql)
The only downside to this mehod is that you have to know the filename beforehand - there's no way to enumerate them from inside SQL Server.
I get around this by running powershell inside Azure Automation that enumerates blobds and writes them into a queue table beforehand