I'm going to prepare some export functionality from blob storage. For this I'd like to use azure data factory (ADF) where I will use copy activity with target data set blob storage using zip compression. I just can't find some information on how big that target zip file could be. In my case it's something between few hundreds of MB to few hundreds of GB. Is there some documentation or does someone have experiences with creating huge (>100GB) zip files with ADF?
Data Factory is designed to scale to handle petabytes of data.
However, Limit for payload size doesn't relate to the amount of data you can move and process with Azure Data Factory.
To know more about Azure Data Factory limits you can refer documentation
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I need to merge more than 800 CSV files that have an identical structure into 1 file on Azure Data Lake Storage GEN2 using a ADF pipeline. Up to how many files can I merge into one and what is the recommended maximum size of the output file?
Up to how many files can I merge into one?
There might not be no limit on the number of files that can be merged into a single file. The following is a demonstration where I have merged up to 10,000 files into one single file.
The copy data activity merge:
The output:
what is the recommended maximum size of the output file?
Since you are merging your files to store it in Azure Data Lake gen2 storage, this data lake storage allows you to store up to 5TB for a single file with few performance limitations.
According to this official Microsoft documentation for Best practices for using Azure Data Lake Storage Gen2, storing many small files reduce performance (which is not the case here). A file with size greater than 100GB also reduces efficiency. Therefore, it is recommended that the optimal output file size would be something less than 100GB.
Here is how my ADF Pipeline looks like. In Data Flow, I read some data from a source, perform filter & join and store data to a sink. My plan was to use Azure Table Storage as the sink. However, according to https://github.com/MicrosoftDocs/azure-docs/issues/34981, ADF Data Flow does not support Azure Table Storage as a sink. Is there an alternative to use Azure Table Storage as the sink in Data Flow?
No, it is impossible. Azure Table Storage can not be the sink of data flow.
Only these six dataset is allowed:
Not only these limits. When as the sink of the dataflow, Azure Blob Storage and Azure Data Lake Storage Gen1&Gen2 only support four format: JSON, Avro, Text, Parquet.'
At least for now, your idea is not a viable solution.
For more information, have a look of this offcial doc:
https://learn.microsoft.com/en-us/azure/data-factory/data-flow-sink#supported-sink-connectors-in-mapping-data-flow
Even today it isn't possible. One option could be (we are solving a similar case like this currently) to use a Blob Storage as a temporary destination.
The data flow will store the result in the Blob Storage. The source data is processed by all these different transformations in the data flow and prepared well for table storage, e.g. PartitionKey, RowKey, and all other columns are there.
A subsequent Copy Activity will move the data from Blob Storage into Table Storage easily.
The marked part of the pipeline is doing exactly this:
Full Orders runs the data flow
to Table Storage copy activity moves data into the Table Storage
I'm working on a requirement to reduce the cost of data storage. It includes the following tasks:
Being able to remove files from File Share and blobs from Blob Storage, based on their last modified date.
Being able to change the tier of individual blobs, based on their last modified date.
Does Azure Data Factory has built-in activities to take care of these tasks? What's the best approach for automating the clean-up process?
1.Being able to remove files from File Share and blobs from Blob Storage, based on their last modified date.
This requirement could be implemented by ADF built-in method: Delete Activity.
Please create a blob storage dataset and just refer to this example and configure the range of last modify date :https://learn.microsoft.com/en-us/azure/data-factory/delete-activity#clean-up-the-expired-files-that-were-last-modified-before-201811
Please consider some back up strategy for some accidents because:
2.Being able to change the tier of individual blobs, based on their last modified date.
No built-in feature to complete this in ADF. However,while i notice that your profile shows you are .net maker, so follow this case:Azure Java SDK - set block blob to cool storage tier on upload so that you could know the Tier could be changed in sdk code. That's easy to create an Azure Function to do such simple task. Moreover,ADF supports Azure Function Activity.
I am accessing Databricks Delta tables from Azure Data Factory, which does not have a native connector to Databricks tables. So, as a workaround, I create the tables with the LOCATION keyword to store them in Azure Data Lake. Then, since I know the table file location, I just read the underlying Parquet files from Data Factory. This works fine.
But... what if there is cached information in the Delta transaction log that has not yet been written to disk? Say, an application updated a row in the table, and the disk does not yet reflect this fact. Then my read from Data Factory will be wrong.
So, two questions...
Could this happen? Are changes held in the log for a while before being written out?
Can I force a transaction log flush, so I know the disk copy is updated?
Azure Data Factory has built in delta lake support (this was not the case at the time the question was raised).
Delta is available as an inline dataset in a Azure Data Factory data flow activity. To get column metadata, click the Import schema button in the Projection tab. This will allow you to reference the column names and data types specified by the corpus (see also the docs here).
ADF supports Delta Lake format as of July 2020:
https://techcommunity.microsoft.com/t5/azure-data-factory/adf-adds-connectors-for-delta-lake-and-excel/ba-p/1515793
The Microsoft Azure Data Factory team is enabling .. and a data flow connector for data transformation using Delta Lake
Delta is currently available in ADF as a public preview in data flows as an inline dataset.
I am Coping files from Azure blob storage to azure data lake store, I need to pick files from year(folder)\month(folder)\day(txt files are on day bases).I am able to do one file with hadrcoded path but i am not able to pick file per day and process to copy in azure data lake store. Can anyone please help me.
I am using ADF V2 and using UI designer to create my connections,datasets and pipeline my steps are which i is working fine
copy file from blob storage to data lake store
picking that file from data lake store and processing through usql for transform data.
that transform data i am saving in Azure SQL DB
Please give me answer i am not able to get any help b/c all help is in JSON i am looking how i will define and pass parameters in UI designer.
Thanks
For the partitioned file path part, you could take a look at this post.
You could use copy data tool to handle it.