How do I create a new managed table in Databricks using Unity Catalog and Delta Lake, specifying the schema and loading data from a CSV file?
To create a new managed table in Databricks using Unity Catalog and Delta Lake, you can follow these steps:

1. Define the schema for your table using the `StructType` and `StructField` classes in PySpark or the `StructType` and `StructField` objects in Scala.
2. Load the CSV file into a DataFrame using the `spark.read.format("csv")` method in PySpark or the `spark.read.format("csv")` method in Scala.
3. Create the table using the `df.writeTo("catalog.schema.table").createOrReplace()` method in PySpark or the `df.writeTo("catalog.schema.table").createOrReplace()` method in Scala.

Here's an example of how to create a new managed table in PySpark:

```python
from pyspark.sql.types import StructType, StructField, IntegerType, StringType, TimestampType

schema = StructType([
    StructField("id", IntegerType(), True),
    StructField("firstName", StringType(), True),
    StructField("middleName", StringType(), True),
    StructField("lastName", StringType(), True),
    StructField("gender", StringType(), True),
    StructField("birthDate", TimestampType(), True),
    StructField("ssn", StringType(), True),
    StructField("salary", IntegerType(), True)
])

df = spark.read.format("csv").option("header", True).schema(schema).load("/path/to/csv/file.csv")

df.writeTo("main.default.people_10m").createOrReplace()
```

And here's an example of how to create a new managed table in Scala:

```scala
import org.apache.spark.sql.types._

val schema = StructType(Array(
    StructField("id", IntegerType, nullable = true),
    StructField("firstName", StringType, nullable = true),
    StructField("middleName", StringType, nullable = true),
    StructField("lastName", StringType, nullable = true),
    StructField("gender", StringType, nullable = true),
    StructField("birthDate", TimestampType, nullable = true),
    StructField("ssn", StringType, nullable = true),
    StructField("salary", IntegerType, nullable = true)
))

val df = spark.read.format("csv").option("header", "true").schema(schema).load("/path/to/csv/file.csv")

df.writeTo("main.default.people_10m").createOrReplace()
```

In both examples, replace `/path/to/csv/file.csv` with the path to your CSV file, and `main.default.people_10m` with the desired catalog, schema, and table name in Unity Catalog.