Issue with DP600 Labs instructions

[kaurhaz7895]

My issue is that for most of the DP600 labs, instructions doesn't match with the interfce. For instance for lab number 05: "Create and use Dataflows (Gen2) in Microsoft Fabric", steps like creating a workspace, lakehouse, adding a lakehouse to a dataflow are extremely confusing. Now we don't have experiences in Fabric; we have workloads. But instructions say go to Data Engineering experience. Also, when we are creating dataflows within the lakehouse, lakehouse gets attached automatically as a default destination. But instructions are instructing to add lakehouse, while its disabled since its already attached. So I have to reiterate it multiple times to students.

The issue is not only in lab 05, same issue persists in lab 04. Now copy assisstant doesn't use copy data activity; it uses copy job. Again mismatch with instrcutions and fabric interface.

Same for power bi labs as well. Many instructions are missing. Like for lab 10 and lab 12. Lab 10 assumes, people knows power BI while in Lab 12, it skips instructions on how to login to server while we are opening PBIT file.

[arundba5]

https://microsoftlearning.github.io/mslearn-fabric/Instructions/Labs/02-analyze-spark.html

The code in the instructions are not working for me and for my team. The correct code should be the following.

from pyspark.sql.types import *

Define schema

orderSchema = StructType([
StructField("SalesOrderNumber", StringType()),
StructField("SalesOrderLineNumber", IntegerType()),
StructField("OrderDate", DateType()),
StructField("CustomerName", StringType()),
StructField("Email", StringType()),
StructField("Item", StringType()),
StructField("Quantity", IntegerType()),
StructField("UnitPrice", FloatType()),
StructField("Tax", FloatType())
])

Read CSV with schema

df = (
spark.read
.format("csv")
.option("header", "true") # use this if your CSV has a header
.schema(orderSchema)
.load("Files/Orders/orders/*.csv")
)

display(df)

#---------------------
from pyspark.sql.functions import col, year, month, split

Create Year and Month columns

transformed_df = (
df.withColumn("Year", year(col("OrderDate")))
.withColumn("Month", month(col("OrderDate")))
)

Create FirstName and LastName fields

transformed_df = (
transformed_df
.withColumn("FirstName", split(col("CustomerName"), " ").getItem(0))
.withColumn("LastName", split(col("CustomerName"), " ").getItem(1))
)

Select and reorder columns

transformed_df = transformed_df.select(
"SalesOrderNumber",
"SalesOrderLineNumber",
"OrderDate",
"Year",
"Month",
"FirstName",
"LastName",
"Email",
"Item",
"Quantity",
"UnitPrice",
"Tax"
)

Display first five orders

display(transformed_df.limit(5))
#------------------------

[weslbo]

@arundba5 Thank you for your suggestions. We will review those and make updates where necessary.

[arundba5]

THANKS WESLBO.

t