rdblue commented on a change in pull request #1095:
URL: https://github.com/apache/iceberg/pull/1095#discussion_r440488580
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File path: site/docs/api-quickstart.md
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@@ -48,6 +48,36 @@ logsDF.write
The logs [schema](#create-a-schema) and [partition
spec](#create-a-partition-spec) are created below.
+### Using a Hadoop catalog
+
+The Hadoop catalog doesn't need to connects to a Hive MetaStore. To get a
Hadoop catalog see:
+
+```scala
+import org.apache.hadoop.conf.Configuration;
+import org.apache.iceberg.hadoop.HadoopCatalog;
+
+val conf = new Configuration();
+val warehousePath = "hdfs://warehouse_path";
+val catalog = new HadoopCatalog(conf, warehousePath);
+```
+
+Like Hive catalog, Hadoop catalog implements the interface `Catalog`. So it
also contains methods for working with tables, like createTable, loadTable,
renameTable, and dropTable.
+
+This example create a table with Hadoop catalog:
+
+```scala
+val name = TableIdentifier.of("logging", "logs")
+val table = catalog.createTable(name, schema, spec)
+
+// write into the new logs table with Spark 2.4
+logsDF.write
+ .format("iceberg")
+ .mode("append")
+ .save("hdfs://warehouse_path/logging/logs")
Review comment:
This seems like something we might want to solve in configuration
instead of hard-coding support for `HiveCatalog`. This problem might go away
when we get a better API in Spark 3.
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