I need to get the data from data lake to dataverse database using dataflow.
Related
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 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.
Hive stores its metadata I'm external database like SQL server. Similar to that where does the databricks delta stores its metadata Information?
Databricks Delta stores its metadata on the file system. They are just files in either json (for each transaction) or parquet format (for a snapshot of the table metadata at some version).
There are couple of compute environments that can do transformations for me. I have a REST source from where I am getting responses every day and I have to perform some transformations.
https://learn.microsoft.com/en-us/azure/data-factory/compute-linked-services
I am confused as to what could be the best way to do it? Or in other words whats the different between all the compute environments as in when should I use Azure Batch, stored procedures, HDInsight, etc?
It really depends on where you have the data. If you are storing the data in a data lake, you won't use a stored procedure. If you are storing the data in an Azure Sql, you won't use Data Lake Analytics.
Basically its like this:
Data lake -> data lake analytics with u-sql
Azure SQL (warehouse or just sql) -> stored procedure
HDInsight hadoop -> Pig, hive, etc
None of the above -> custom activity with Azure Batch
Hope this helped!
I am new to Scala and spark i would like to load the files from azure data lake store.
I want to load all the files which start from test.
I tried as follows:
folder/test[0-10000]*.csv"
Can some suggest any solution?