Begin by exporting the data from your SQL Server database to a CSV file. You can do this using SQL Server Management Studio (SSMS) by running a SQL query to select the data you need and using the "Export Data" wizard to save the results to a CSV file. Ensure that the CSV file is correctly formatted, with appropriate column headers matching your table structure.

Install and configure Apache Spark, as this will be the primary tool for importing data into Apache Iceberg. Download Spark from the official website and set it up on your local machine or server. Make sure you have the necessary permissions and environment variables (such as `SPARK_HOME` and `PATH`) properly configured.

To work with Apache Iceberg using Spark, you need to include Iceberg’s library in your Spark environment. You can do this by adding the Iceberg package to Spark when you start the Spark shell or submit a job. Use the following command:
```
spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:0.14.1
```
Replace the version with the appropriate one that matches your Spark version.

Define the schema for your Iceberg table based on the structure of the data you exported from SQL Server. You can do this by writing a Spark application or using the Spark shell to define the schema and create the table in Iceberg. Ensure that the data types in the schema match those of the CSV file.

Use Spark to read the CSV file into a DataFrame. This step involves using the `spark.read.csv` method, which allows you to specify options such as the delimiter and whether the first row is a header. For example:
```scala
val df = spark.read.option("header", "true").csv("path/to/your/data.csv")
```

With the DataFrame created, the next step is to write it into the Iceberg table. Use the DataFrame's `write` method to specify Iceberg as the format and provide the necessary table details. For example:
```scala
df.write.format("iceberg").mode("append").save("iceberg_db.your_table_name")
```

Finally, verify that the data has been successfully transferred by querying the Iceberg table. You can do this using Spark SQL by creating a temporary view and running a simple query:
```scala
df.createOrReplaceTempView("tempView")
spark.sql("SELECT FROM iceberg_db.your_table_name").show()
```
Ensure the data appears correctly and matches what was in your original SQL Server database.

By following these steps, you can move data from MS SQL Server to Apache Iceberg without relying on third-party connectors or integrations.