The error you're encountering is related to a compatibility issue between Databricks' GCS implementation and Apache Iceberg when trying to read Iceberg tables from Google Cloud Storage. The specific error is:

```
java.lang.UnsupportedOperationException: Byte-buffer read unsupported by com.databricks.common.filesystem.LokiGCSInputStream
```

This indicates that the Databricks GCS file system implementation (`LokiGCSInputStream`) doesn't support the byte-buffer read operations that Iceberg requires when reading Parquet files.

Potential Solutions

1. Use a Different FileIO Implementation

You need to configure Iceberg to use a different FileIO implementation that's compatible with Databricks' GCS integration. Try setting the following configuration:

```python
spark.conf.set("spark.sql.catalog.your_catalog_name.io-impl", "org.apache.iceberg.gcp.gcs.GCSFileIO")
```

2. Update Catalog Configuration

Ensure your catalog is properly configured with the correct GCS credentials and implementation:

```python
Configure Iceberg catalog
spark.conf.set("spark.sql.catalog.your_catalog_name", "org.apache.iceberg.spark.SparkCatalog")
spark.conf.set("spark.sql.catalog.your_catalog_name.type", "hadoop")
spark.conf.set("spark.sql.catalog.your_catalog_name.warehouse", "gs://your-bucket/path")
spark.conf.set("spark.sql.catalog.your_catalog_name.io-impl", "org.apache.iceberg.gcp.gcs.GCSFileIO")
```

3. Check Iceberg Version Compatibility

The issue might be related to compatibility between Iceberg 1.5.1 and Databricks Runtime 16.3. Try using a different Iceberg version that's known to work with Databricks, such as 1.4.2:

```python
Include in your spark configuration
spark.conf.set("spark.jars.packages", "org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.4.2,org.apache.iceberg:iceberg-gcp-bundle:1.4.2")
```

4. Use Absolute Paths

Iceberg requires absolute paths to locate metadata files and data files. Make sure you're using the full GCS path:

```python
# Instead of using a table name reference
df = spark.sql("SELECT * FROM gs://your-bucket/path/to/table")
```

5. Consider Using Unity Catalog

If possible, consider using Databricks Unity Catalog with Iceberg reads enabled, which provides better integration:

```sql
CREATE TABLE T(c1 INT) TBLPROPERTIES(
'delta.columnMapping.mode' = 'name',
'delta.enableIcebergCompatV2' = 'true',
'delta.universalFormat.enabledFormats' = 'iceberg'
);
```

This is a known issue with Iceberg and certain file system implementations that don't support byte-buffer reads. The error occurs during the reading of Parquet file footers, which Iceberg uses to build its metadata model.