Lakehouse connector#

The Lakehouse connector provides a unified way to interact with data stored in various table formats across different storage systems and metastore services. This single connector allows you to query and write data seamlessly, regardless of whether itโ€™s in Iceberg, Delta Lake, or Hudi table formats, or traditional Hive tables.

This connector offers flexible connectivity to popular metastore services including AWS Glue and Hive Metastore. For data storage, it supports a wide range of options including cloud storage services such as AWS S3, S3-compatible storage, Google Cloud Storage (GCS), and Azure Blob Storage, as well as HDFS installations.

The connector combines the features of the Hive, Iceberg, Delta Lake, and Hudi connectors into a single connector. The configuration properties, session properties, table properties, and beahvior come from the underlying connectors. Please refer to the documentation for the underlying connectors for the table formats that you are using.

General configuration#

To configure the Lakehouse connector, create a catalog properties file etc/catalog/example.properties with the following content, replacing the properties as appropriate:

connector.name=lakehouse

You must configure a AWS Glue or a Hive metastore. The hive.metastore property will also configure the Iceberg catalog. Do not specify iceberg.catalog.type.

You must select and configure one of the supported file systems.

Configuration properties#

The following configuration properties are available:

Property name

Description

Default

lakehouse.table-type

The default table type for newly created tables when the format table property is not specified. Possible values:

  • HIVE

  • ICEBERG

  • DELTA

ICEBERG

File system access configuration#

The connector supports accessing the following file systems:

You must enable and configure the specific file system access.

Examples#

Create an Iceberg table:

CREATE TABLE iceberg_table (
  c1 INTEGER,
  c2 DATE,
  c3 DOUBLE
)
WITH (
  type = 'ICEBERG'
  format = 'PARQUET',
  partitioning = ARRAY['c1', 'c2'],
  sorted_by = ARRAY['c3']
);

Create a Hive table:

CREATE TABLE hive_page_views (
  view_time TIMESTAMP,
  user_id BIGINT,
  page_url VARCHAR,
  ds DATE,
  country VARCHAR
)
WITH (
  type = 'HIVE',
  format = 'ORC',
  partitioned_by = ARRAY['ds', 'country'],
  bucketed_by = ARRAY['user_id'],
  bucket_count = 50
)