Azure Data Factory

By Ashwin Venugopal -

 




Index Data From External Data Sources Using Azure Data Factory

An organization's search solution frequently has to include external data that isn't stored in Azure. Because it offers numerous methods for creating and pushing data into indexes, Azure AI Search is adaptable.

Push Data Into Search Index Using Azure Data Factory (ADF)

One method is to use ADF to push data into an index using a zero-code option. Almost 100 distinct data stores are connected to ADF. You can link an infinite number of data stores using connectors like HTTP and REST.

These data stores are utilized in pipelines as either a source or a target (referred to as sinks). In a copy activity, the Azure AI Search index connector can be utilized as a sink.

Create ADF Pipeline To Push Data Into Search Index

The steps you need to take to use and ADF pipeline to push data into a search index are:

  1. Create an Azure AI Search index with all the fields you want to store data in.
  2. Create a pipeline with a copy data step.
  3. Create a data source connection to where your data resides.
  4. Create a sink to connect to your search index.
  5. Map the fields from your source data to your search index.
  6. Run the pipeline to push the data into the index.

Following these steps you can know how you can push data into an index. The pipeline you have created by default merges updates into the index. If you amended the JSON data and rerun the pipeline, the search index would be updated. You can change the write behavior to upload only if you want the data to be replaced each time you run your pipeline.

Limitations of Using Built-in Azure AI Search as Linked Service

At the moment, the Azure AI Search linked service as a sink only supports these fields- String, Int32, Int64, Double Boolean, DataTimeOffset. This indicates that arrays and ComplexTypes are not supported at this time.

Conclusion

We have successfully learnt about Azure Data Factory.






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