I have data pipeline in Azure Data Factory which copies the files from AWS S3 Bucket to Azure Data lake gen 2. So in order to build this pipeline I have created various resources like Azure Data Lake Gen 2 Storage, File system in ADLS with specific permissions, Data Factory, Source Dataset which connects to S3 Bucket, Target Dataset which connects to ADLS Gen2 folder.
So all these were created in a Dev subscription in Azure but now I want to deploy these resources in Prod subscription with least manual effort. I tried the ARM template approach which does not allow me to selectively choose the pipeline for migration. It copies everything present on the data factory which I don't want considering I may have different pipelines which are still under development phase and I do not want those to be migrated to Prod. I tried the powershell approach too which also has some limitations.
I would need expert advice on what is the best way to migrate the code from one subscription to other.
Related
I want to upload binary files from Windows FileSystem to Azure blob. I achieved it with Azure data factory with the below steps
Installed integration run time on the FileSystem
Created a linked service in ADF as FileSystem
Created a binary dataset with the above linked service
Use CopyData activity in a ADF Pipeline, set the binary dataset as source and Azure Blob as Sink
Post upload, I am performing some ETL activities. So my ADF pipeline has two components,
Copy Data
Databricks Notebook
I am wondering if I could move the Copy Data fragment to Databricks?
Can we upload binary files from Windows FileSystem to Azure blob using Azure Databricks?
I think it is possible, you may have to do network changes
https://learn.microsoft.com/en-us/azure/databricks/administration-guide/cloud-configurations/azure/on-prem-network
In my azure subscription I have a storage account with a lot of tables that contains important data.
As far as I know azure offers a backup point-in-time for the storages and blobs, and geo redundancy in event of a failover. But I couldn't find anything regarding the backup of table storages.
The only way to do so is by using azCopy which is fine and a logic, but I couldn't make it work as I had some issues with permissions even if I set the Azure Blob Data Contributor to my container.
So as an option, I was thinking if there is a way how to implement this using python code to loop throu all the tables in a specific container and make a copy into another container.
Can anyone enlighten me on this matter please?
Did you set the Azure Storage firewall: allow access from all networks?:
Python code is a way but we can't help you design the code. And there isn't an example for you. It doesn't meet Stack Overflow's guideline.
If you still couldn't figure it out with AzCopy, I would suggest you think about use Data Factory to schedule backup the data from table storage to another container.
Create a pipeline with copy active to copy the data from Table
Storage. Ref this tutorial:Copy data to and from Azure Table
storage by using Azure Data Factory.
Create a schedule trigger for the pipeline to make the jobs
automatic.
If the Table storage has many tables, the easiest way is using Copy Data Tool.
Update:
Copy data tool source settings:
Sink settings: auto create the table in sink table storage
HTH.
I have the following ETL requirements for Snowflake on Azure and would like to implement the simplest possible solution because of timeline and technology constraints.
Requirements :
Load CSV data (only a few MBs) from Azure Blob Storage into Snowflake Warehouse daily into a staging table.
Transform the loaded data above within Snowflake itself where transformation is limited to just a few joins and aggregations to obtain a few measures. And finally, park this data into our final tables in a Datamart within the same Snowflake DB.
Lastly, automate the above pipeline using a schedule OR using an event based trigger (i.e. steps to kick in as soon as file lands in Blob Store).
Constraints :
We cannot use use Azure Data Factory to achieve this simplest design.
We cannot use Azure Functions to deploy Python Transformation scripts and schedule them either.
And, I found that Transformation using Snowflake SQL is a limited feature where it only allows certain things as part of COPY INTO command but does not support JOINS and GROUP BY. Furthermore, although the following THREAD suggests that scheduling SQL is possible, but that doesn't address my Transformation requirement.
Regards,
Roy
Attaching the following Idea diagram for more clarity.
https://community.snowflake.com/s/question/0D50Z00009Z3O7hSAF/how-to-schedule-jobs-from-azure-cloud-for-loading-data-from-blobscheduling-snowflake-scripts-since-dont-have-cost-for-etl-tool-purchase-for-scheduling
https://docs.snowflake.com/en/user-guide/data-load-transform.html#:~:text=Snowflake%20supports%20transforming%20data%20while,columns%20during%20a%20data%20load.
You can create snowpipe on Azure blob storage, Once snowpipe created on top of your azure blob storage, It will monitor bucket and file will be loaded into your stage table as soon as new file comes in. After copied the data into stage table you can schedule transformation SQL using snowflake task.
You can refer snowpipe creation step for azure blob storage in below link:
Snowpipe on microsoft Azure blob storage
I have a copy data pipeline in the Azure Data Factory. I need to deploy the same Data Factory instance in multiple environments like DEV, QA, PROD using Release Pipeline.
The pipeline transfer data from Customer Storage Account (Blob Container) to Centralized Data Lake. So, we can say - its a Many to One flow. (Many customers > One Data Lake)
Now, suppose I am in DEV environment & I have 1 demo customer there. I have defined an ADF pipeline for Copy Data. But in prod environment, the number of customers will grow. So, I don't want to create multiple copies of the same pipeline in production Data Factory.
I am looking out for a solution so that I can keep one copy pipeline in Data Factory and deploy/promote the same Data Factory from one environment to the other environment. And this should work even if the number of customers is varying from one to another.
I am also doing CI/CD in Azure Data Factory using Git integration with Azure Repos.
You will have to create additional linked services and datasets which do not exist in a non-production environment to ensure any new "customer" storage account is mapped to the pipeline instance.
With CI/CD routines, you can deliver this in an incremental manner i.e. parameterize you release pipeline with variable groups and update the data factory instance with newer pipelines with new datasets/linked services.
My company has two Azure environments. The first one was a temporary environment and is being re-purposed / decommissioned / I'm not sure. All I know is I need to get files from one Data Lake on one environment, to a DataLake on another. I've looked at adlcopy and azcopy and neither seem like they will do what I need done. Has anyone encountered this before and if so, what did you use to solve it?
Maybe you can think about Azure Data Factory, it can helps you transfer files or data from one Azure Data Lake to Another Data Lake.
You can reference Copy data to or from Azure Data Lake Storage Gen2 using Azure Data Factory.
This article outlines how to use Copy Activity in Azure Data Factory to copy data to and from Data Lake Storage Gen2. It builds on the Copy Activity overview article that presents a general overview of Copy Activity.
For example, you can learn from this tutorial: Quickstart: Use the Copy Data tool to copy data.
In this quickstart, you use the Azure portal to create a data factory. Then, you use the Copy Data tool to create a pipeline that copies data from a folder in Azure Blob storage to another folder.
Hope this helps.