The Open Data Blend November 2023 update includes updates to the prescribing dataset and support for OneLake in Microsoft Fabric.

Open Data Blend Datasets

English Prescribing Data for September 2023

We updated the Prescribing dataset with the latest available NHS English Prescribing data including activity up until September 2023. This is contained in the 2023 data file and supporting dimension (i.e. lookup/reference) files.

You can download the data from the Open Data Blend Datasets Prescribing page, analyse it directly in supported BI tools through the Open Data Blend Analytics service, or instantly explore insights through the Pharma Market Insights report.

OneLake in Microsoft Fabric Support in Open Data Blend for Python

We released Open Data Blend for Python 1.4.0. This new version introduces support for OneLake in Microsoft Fabric which enables you to copy our datasets into Fabric lakehouse with just a few lines of code.

After preparing your Microsoft Fabric workspace as described here, you can start ingesting our datasets into your lakehouse as shown in the following example:

import opendatablend as odb

dataset_path = 'https://packages.opendatablend.io/v1/open-data-blend-road-safety/datapackage.json'
access_key = '<ACCESS_KEY>' # The access key can be set to an empty string if you are making a public API request

# Specify the resource names of the data files. In this example, a subset of the available data files will be requested in Parquet format.
resource_names = [
    'date-parquet',
    'time-of-day-parquet',
    'geolocation-parquet',
    'road-safety-accident-info-parquet',
    'road-safety-accident-location-parquet',
    'road-safety-accident-2021-parquet'
    ]

# Specify the base path for where the data files should be landed. In this example, we want them to be stored in the root of the 'Files' folder in the Fabric lakehouse that has been attached to the Fabric notebook. 
base_path = '/lakehouse/default/Files/'

output = odb.get_data_files(dataset_path, resource_names, base_path=base_path, access_key=access_key)

# Print the file locations
print(output.data_file_names)
print(output.metadata_file_name)

You can learn more about Open Data Blend for Python and how to start using it here.

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