Hi @israelst 

When working with Kinesis in Databricks, you can effectively handle various data formats including JSON, Avro, or bytes. The key is to appropriately decode the data in your Spark application.

Before reading your stream, define your data schema. For JSON data, this can be done manually using PySpark's StructType and StructField if you are aware of your data structure.

To read the stream, specify the source format as “kinesis” in your Databricks notebook. 

df = spark.readStream.format("kinesis")
.option("streamName", "your_stream_name")
.option("initialPosition", "latest")
.load()

After defining the schema, use it to process the JSON data from Kinesis:

from pyspark.sql.functions import from_json
json_df = df.select(from_json(df.value.cast("string"), schema).alias("data")).select("data.*")

Thanks @Priyanka_Biswas, but as I wrote - I am aiming for automatic inference of the schema. AutoLoader already has this functionality... But it seems kinesis structured streaming doesn't...

I wanted to use Databricks for this. I don't want to depend on AWS Glue. Same way I could do it with AutoLoader...