#### Code

# CodeImport DataType

from pyspark.sql.types import StructType, StructField, TimestampType, IntegerType, StringType, FloatType, BooleanType, LongType

# Define Custom Schema

call_schema = StructType(

  [

    StructField("RecordType", StringType(), True),

    StructField("SystemIdentity", StringType(), True),

    StructField("FileNum", StringType(), True),

    StructField("SwitchNum", StringType(), True),

    StructField("CallingNum", StringType(), True),

    StructField("CallingIMSI", StringType(), True),

    StructField("CalledNum", StringType(), True),

    StructField("CalledIMSI", StringType(), True),

    StructField("DateS", StringType(), True),

    StructField("TimeS", StringType(), True),

    StructField("TimeType", LongType(), True),

    StructField("CallPeriod", LongType(), True),

    StructField("CallingCellID", StringType(), True),

    StructField("CalledCellID", StringType(), True),

    StructField("ServiceType", StringType(), True),

    StructField("Transfer", LongType(), True),

    StructField("IncomingTrunk", StringType(), True),

    StructField("OutgoingTrunk", StringType(), True),

    StructField("MSRN", StringType(), True),

    StructField("CalledNum2", StringType(), True),

    StructField("FCIFlag", StringType(), True),

    StructField("callrecTime", TimestampType(), True),

    StructField("EventProcessedUtcTime", TimestampType(), True),

    StructField("PartitionId", LongType(), True),

    StructField("EventEnqueuedUtcTime", TimestampType(), True),

  ]

)

# Define Delta Live Table

import dlt

from pyspark.sql.functions import *

from pyspark.sql.types import *

json_path = "/mnt/adlspoc23/stream/"

@dlt.table(

 comment="The raw Telecom callstream dataset, ingested from ADLS.",

 schema=call_schema

)

def callstream_raw():

 return (spark.read.format("json").load(json_path))

# Error

org.apache.spark.sql.AnalysisException: Table 'callstream_raw' has a user-specified schema that is incompatible with the schema inferred from its query.