*Data newbie here *
Currently, to run analytics report on data pulled from Dynamics 365, I use Power BI.
Issue with this is, Power BI is quite slow processing large data. I carry out a number of transform steps (e.g. Merge, Join, deleting or renaming columns, etc). So, when I try to run a query in Power BI with said steps, it takes a long time to complete.
So, as a solution, I decided to make use of Azure Data Factory(ADF). The plan is to use ADF to pull the data from CRM (i.e. Dynamics 365), perform transformations and publish the data. Then I'll use Power BI for visual analytics.
My question is:
What azure service will I need in addition to Data Factory? Will I need to store the data I pulled from CRM somewhere - like Azure Data Lake or Blob storage? Or can I do the transformation on the fly, right after the data is ingested?
Initially, I thought I could use the 'copy' activity to ingest data from CRM and start playing with the data. But using the copy activity, I needed to provide a sink (destination for the data. Which has to be a storage of some sort).
I also thought, I could make use of the 'lookup' activity. I tried to use it, but getting errors (no exception message is produced).
I have scoured the internet for a similar process (i.e. Dynamics 365 -> Data Factory -> Power BI), but I've not been able to find any.
Most of the processes I've seen however, utilises some sort of data storage right after data ingest.
All response welcome. Even if you believe I am going about this the wrong way.
Thanks.
Few things here:
The copy activity just moves data from a source, to a sink. It doesnt modify it on the fly.
The lookup activity is just to look for some atributes to use later on the same pipeline.
ADF cannot publish a dataset to power bi (although it may be able to push to a streaming dataset).
You approach is correct, but you need that last step of transforming the data. You have a lot of options here, but since you are already familiar with Power Bi you can use the Wrangling Dataflows, which allows you to take a file from the datalake, apply some power query and save a new file in the lake. You can also use Mapping Dataflows, databricks, or any other data transformation tool.
Lastly, you can pull files from a data lake with Power Bi to make your report with the data on this new file.
Of course, as always in Azure there are a lot of ways to solve problems or architect services, this is the one I consider simpler for you.
Hope this helped!
Related
My assumptions where MDF might be right fit are as follows:
MDF can be used as a Data Wrangling Tool by end-users
MDF is better suited for SQL Server-based Datawarehouse architectures to load the data into staging or data lake in clean format (prepare the data before loading it to SQL Server DWH and then use a proper ETL tool to do transformations)
If MDF has to be used for light ELT / ETL tasks directly on Data Lake or DWH, it needs customization for complex transformations...
My question would be:
A) Did anyone use Mapping Data Flow in production for option 2 and 3 above?
B) If assumption 3 is valid, would you suggest going for Spark-based transformation or an ETL tool rather than patching the MDF with customizations as new versions might not be compatible with, etc..
I disagree with most of your assumptions. Data Flow is a part of a larger ETL environment, either Data Factory (ADF) or Azure Synapse Pipelines and you really can't separate it from it's host. Data Flow is a UI code generator that executes at runtime as a Spark job. If your end user is a data engineer, then yes Data Flow is a good tool for them.
ADF is a great service for orchestrating data operations. ADF supports all the things you mentioned (SSIS, Notebooks, Stored Procedures, and many more). It also supports Data Flow, which is absolutely a "proper" tool for transformations and has a very rich feature set. In fact, if you are NOT doing transformations, Data Flow is likely overkill for your solution.
I do not understand the difference between dataflow and pipeline in Azure Data Factory.
I have read and see DataFlow can Transform Data without writing any line of code.
But I have made a pipeline and this is exactly the same thing.
Thanks
A Pipeline is an orchestrator and does not transform data. It manages a series of one or more activities, such as Copy Data or Execute Stored Procedure. Data Flow is one of these activity types and is very different from a Pipeline.
Data Flow performs row and column level transformations, such as parsing values, calculations, adding/renaming/deleting columns, even adding or removing rows. At runtime a Data Flow is executed in a Spark environment, not the Data Factory execution runtime.
A Pipeline can run without a Data Flow, but a Data Flow cannot run without a Pipeline.
Firstly, dataflow activity need to be executed in the pipeline. So I suspect that you are talking about the copy activity and dataflow activity as both of them are used for transferring data from source to sink.
I have read and see DataFlow can Transform Data without writing any
line of code.
Your could see the overview of Data Flow. Data flow allows data engineers to develop graphical data transformation logic without writing code. All data transfer steps are based on visual interfaces.
I have made a pipeline and this is exactly the same thing.
Copy activity could be used for data transmission. However, it has many limitations with column mapping. So,if you just need simple and pure data transmission, Copy Activity could be used. In order to further meet the personalized needs, you could find many built-in features in the Data Flow Activity. For example, Derived column, Aggregate,Sort etc.
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.
We are implementing solution to achieve similar functionality as of ssis packages to copy data from one database to another (on-premise to azure SQL). In SSIS we have option to setup parallel processing in different ways. We can also transfer data in chunks.
Similarly, which is the best way to achieve parallelisms in Azure Data Factory version 2? Please consider scenario of transferring data for only 1 table.
Have a look at the Copy Activity Performance and Tuning Guide for ways to optimize transferring data into the Cloud with ADF: https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-performance