I have 20 files of type Excel/pdf located in different https server. i need to validate these file and load into azure storage Using Data Factory.I need to do apply some business logic on this data and load into azure SQL Database.I need to if we have to create a pipe line and store this data in azure blob storage and then load into Azure sql Database
I have tried creating copy data in data factory
My idea as below:
No.1
Step 1: Use Copy Activity to transfer data from http connector source into blob storage connector sink.
Step 2: Meanwhile, configure a blob storage trigger to execute your logic code so that the blob data will be processed as soon as it's collected into blob storage.
Step 3: Use Copy Activity to transfer data from blob storage connector source into SQL database connector sink.
No.2:
Step 1:Use Copy Activity to transfer data from http connector source into SQL database connector sink.
Step 2: Meanwhile, you could configure stored procedure to add your logic steps. The data will be executed before inserted into table.
I think both methods are feasible. The No.1, the business logic is freer and more flexible. The No.2, it is more convenient, but it is limited by the syntax of stored procedures. You could pick the solution as you want.
The excel and pdf are supported yet. Based on the link,only below formats are supported by ADF diectly:
i tested for csv file and get the below random characters:
You could refer to this case to read excel files in ADF:How to read files with .xlsx and .xls extension in Azure data factory?
Related
I'm new to both of ADF(Azure Data Factory) and ADX(Azure Data Explorer).
I have multiple Json files in ADLS in different folder level, and I need to ingest all the files into ADX.
ex) UserData/Overground/UsersFolder/project1/main/data/json/demo-02/2021/01/28/03/demo-02-2021-01-28-03-30.json
UserData/Overground/UsersFolder/project1/main/data/json/demo-02/2021/01/28/04/demo-02-2021-01-28-03-30.json
UserData/Overground/UsersFolder/project1/main/data/json/demo-02/2021/01/29/03/demo-02-2021-01-28-03-30.json
UserData/Overground/UsersFolder/project1/main/data/json/demo-02/2021/02/23/03/demo-02-2021-01-28-03-30.json
I'm just wondering if I need to create as many tables in ADX as the number of the Json files in ADLS.. so if I have 1000 Json files in ADLS, should I create 1000 tables in ADX to copy the data from adls to adx?
and how could I copy the data from adls to adx in ADF?
Appreciate your help in advance
To copy from multiple folders, you can use Additional settings of Copy activity Source. For more information follow this official document. You may need to use wildcard for multiple files.
Additional settings:
recursive Indicates whether the data is read recursively from the subfolders or only from the specified folder. Note that when recursive is set to true and the sink is a file-based store, an empty folder or subfolder isn't copied or created at the sink.
Allowed values are true (default) and false.
This property doesn't apply when you configure fileListPath.
Also refer Azure Data Explorer as Sink
Azure Data Explorer is supported as a source, where data is copied from Azure Data Explorer to any supported data store, and a sink, where data is copied from any supported data store to Azure Data Explorer. Integrate Azure Data Explorer with Azure Data Factory
I have a requirement to export data from Azure SQL managed instance to data lake storage as json documents. I have to use SQL Server Integration Services to accomplish this. I tried using the Flexible File Destination Data Flow task but when I see the supported file formats there's no json being supported. What other options do I have to accomplish this.
Azure Data Factory support data movement between Azure Managed Instance and Data lake account, but unfortunately when the destination is Azure Data Lake storage it also doesn't support JSON format using SSIS.
Azure Data Lake Store Destination
The Azure Data Lake Store Destination component enables an SSIS
package to write data to an Azure Data Lake Store. The supported file
formats are: Text, Avro, and ORC.
Workaround: The possible workaround you can try to use Data Flow activity in Azure Data Factory. Load the data from Managed Instance and transform it using Pivot transformation and store the processed data in Data Lake. This approach doesn't involve SSIS. Check this similar kind of request and approach here.
Usecase: I have data files of varying size copied to a specific SFTP folder periodically (Daily/Weekly). All these files needs to be validated and processed. Then write them to related tables in Azure SQL. Files are of CSV format and are actually a flat text file which directly corresponds to a specific Table in Azure SQL.
Implementation:
Planning to use Azure Data Factory. So far, from my reading I could see that I can have a Copy pipeline in-order to copy the data from On-Prem SFTP to Azure Blob storage. As well, we can have SSIS pipeline to copy data from On-Premise SQL Server to Azure SQL.
But I don't see a existing solution to achieve what I am looking for. can someone provide some insight on how can I achieve the same?
I would try to use Data Factory with a Data Flow to validate/process the files (if possible for your case). If the validation is too complex/depends on other components, then I would use functions and put the resulting files to blob. The copy activity is also able to import the resulting CSV files to SQL server.
You can create a pipeline that does the following:
Copy data - Copy Files from SFTP to Blob Storage
Do Data processing/validation via Data Flow
and sink them directly to SQL table (via Data Flow sink)
Of course, you need an integration runtime, that can access the on-prem server - either by using VNet integration or by using the self hosted IR. (If it is not publicly accessible)
I am just going through some Microsoft Document and doing handOn for Data engineering related things.
I have couple of queries for a scenrerio - "copy CSV file(s) from Blob storage to Synapse analytics (stage table(s)):
I read that we can do direct data pull in Synapse with the process of creating external tables. (https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/load-data-wideworldimportersdw)
If above is possible, then in what cases we do use Azure Data factory Copy or data flow method?
While working with Azure data factory, is it a good idea to use Polybase, because it will use Blob storage again as staging in this scenrerio (i.e. I am copying file from Blob only and again using blob for staging)?
I searched for answers to my queries but haven't found any satisfactory answer yet.
If you're just straight loading data from CSV into DW, use Copy. Polybase is recommended, but not always needed for small files.
If you need to transform that data or perform updates, then use data flows.
I am new to Azure and hence trying to understand what services to use when and how.
At the moment, I have one excel file that has couple of tabs that require some transformation to create one excel file tab (inside the source file itself - say Tab "x"). The final tab "x" created is then being useful for creating one final excel file that is shared to various team.
At present, everything is done manually.
This needs to change and the excel file shared to team has to be automated. The source of the file is the excel file that has various tabs (excluding tab "x") and the reporting tool will be SSRS with excel data being stored in cloud.
Keeping this scenario in mind, what is the best way to store excel data into cloud? The excel data will be stored in cloud on a monthly basis. I am confused as to whether to store data in Azure-SQL, Azure Data Lake Gen 2 or Azure Data Lake Analytics or Azure SQL VM?
Every month data can be fetched from Excel file and populate into Azure using azure data factory. But I am not sure what is the best way to store data in the cloud considering the fact that some ETL process is needed to generate data in format similar to tab "X".
I think you can think about to using Azure SQL database.
Azure SQL database or SQL server support you import data from the excel( or csv) files. For more details and limits, please see: Import data from Excel to SQL Server or Azure SQL Database.
If your data have stored in Azure SQL database, you also can using EXCEL to get the data from Azure SQL database:
Connect Excel to a single database in Azure SQL Database and import data and create tables and charts based on values in the database. In this tutorial you will set up the connection between Excel and a database table, save the file that stores data and the connection information for Excel, and then create a pivot chart from the database values.
Reference: Import data from Excel to SQL Server or Azure SQL Database.
I think you don't need to store these excel files in Azure Data Lake.Azure Data Lake Storage Gen2 is a set of capabilities dedicated to big data analytics, built on Azure Blob storage. It's still a storage.
The more Azure resource you use, the more cost you need to pay.
If your excel file stored in you local computer, you can using Azure Data Factory to access these local files or with self host integration runtime.
Please referenceļ¼ Copy data to or from a file system by using Azure Data Factory.
Hope this helps.
Your storage requirements are very minimal, so I would select Data Lake to store your documents. The alternative is Blob Storage, but I always prefer Data Lake because it works with Azure Active Directory.
In your scenario, drop it in the ADL, and use the ADL as the source in Azure Data Factory.
Edit:
Honestly, your original post is a little confusing. You have a RAW Excel document, you do some transformations on the RAW document, to generate an Excel Source document. This source document holds the final dataset that the dev team will use to build out SSRS reports. You need to make this dataset available to the teams so that they can connect to it to build the reports? My suggestion is to keep it simple and drop the final source dataset in Excel format, into blob or data lake storage and then ask the dev guys to pick it up from the location. If you are going the route of designing and maintaining a data pipeline (Blob > Data Factory > SQL, or CSV, TSV - then you are introducing unnecessary complications.