Files are initially ingested into an Azure Data Lake Storage Gen2 account as 10 small JSON files. Navigate to the Azure ADF portal by clicking on the Author & Monitor button in the Overview blade of Azure Data Factory Service.. In this example, I am using Parquet. Hope . Step 4 shows how it will look when the dynamic content is set. Step 1: Copy Data from Relational Sources to ADLS Gen 2. Interestingly the same behaviour can be observed for JSON files, but it seems like that this is not a problem for Databricks and it is able to process the data. Keep your Data Factory clean with generic datasets In Azure, when it comes to data movement, that tends to be Azure Data Factory (ADF). The first step uses Azure Data Factory (ADF) Copy activity to copy the data from its original . ORC, Parquet and Avro focus on compression, so they have different compression algorithms and that's how they gain that performance. Please select the name of the Azure Data Factory managed identity, adf4tips2021, and give it full access to secrets. How to do a Dynamic Column mapping in Copy Activity This section is the part that you need to . Q&A for work. Tags: Azure Data Factory. Data Factory Pipeline JSON to SQL Table | Azure - Freelancer In the left menu, go to Create a resource -> Data + Analytics -> Data Factory. Now for the bit of the pipeline that will define how the JSON is flattened. Azure Data Factory vs Databricks: 4 Critical Key Differences The solution involves three parts: Dynamically generate your list of mapped column names. This data set can be easily partitioned by time since it's a time series stream by nature. Expend "External tables". Add an Azure Data Lake Storage Gen1 Dataset to the pipeline. JSON Source Dataset. The difference I notice between the 'blob_json_prop' you provide, and a dataset generated in the UI, is Foreach activity is the activity used in the Azure Data Factory for iterating over the items. How to Put the Azure Data Factory (ADF) Snowflake Connector to Use Also. You can also sink data in CDM format using CDM entity references that will land your data in CSV or Parquet format in partitioned folders. Although both are capable of performing scalable data transformation, data aggregation, and data movement tasks, there are some underlying key differences between ADF and Databricks, as mentioned below: