-
Reading the Nested JSON File:
- You don’t need to read the JSON file as plain text (
.txt
). Instead, use Databricks’ built-in capabilities to read JSON directly into a DataFrame.
- Start by passing your sample JSON string to the reader. You can use the
spark.read.json
method to read the nested JSON data.
-
Flattening the Nested Structure:
-
Creating Polygons and Centroids:
- Once you have the flattened DataFrame, you can create polygons and centroids.
- For polygons, you’ll need to group the coordinates appropriately. You can use the
ST_PolygonFromEnvelope
function (if your coordinates represent bounding boxes) or other geometry functions.
- For centroids, calculate the average of the latitude and longitude within each polygon.
-
Grouping Other Columns:
- You can group other columns as needed. Use aggregation functions like
groupBy
and apply relevant operations (e.g., sum
, avg
, etc.) to those columns.
-
Persisting the Results:
- Finally, persist the transformed data into a new DataFrame or write it to a storage location (e.g., Delta table, Parquet files, etc.).
Remember to adjust the column names and data types according to your specific JSON structure. If you’re working with PySpark, similar steps apply, but the syntax will be slightly different.
Feel free to ask if you need further assistance! 🚀
For more detailed examples, you can refer to the official Databricks documentation on nested JSON to DataFrame and flattening nested columns12.