Data lake formats reference

Crunchy Data Warehouse supports querying, importing, and exporting in a variety of formats. The following commands can use all supported data lake formats:

• CREATE FOREIGN TABLE ... SERVER crunchy_lake_analytics ... (querying)
• COPY ... FROM (importing)
• CREATE TABLE ... WITH load_from / definition_from option (importing)
• COPY ... TO (exporting)

The format can be specified as an option in the command. Note that PostgreSQL has a slightly different syntax for specifying options in each case:

-- COPY command uses WITH and no =
COPY ... TO/FROM '<url>' WITH (format 'csv');

-- CREATE TABLE ... uses WITH and =
CREATE TABLE ... USING iceberg WITH (load_from = '<url>', format = 'csv');

-- CREATE FOREIGN TABLE ... uses OPTIONS and no =
CREATE FOREIGN TABLE ... SERVER crunchy_lake_analytics OPTIONS (path '<url>', format 'csv');
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When using a common file name extension, the format will be automatically inferred from the extension.

The following formats are supported:

Parquet format

Parquet files are the most versatile format for importing and exporting data in Crunchy Data Warehouse. They are self-describing in terms of column names, types, and compression, and use columnar storage.

The Parquet format supports the following additional options:

CSV format

When querying or importing CSV files, the CSV format properties like the presence of a header, delimiter, and quote character are automatically detected. However, in some cases the detection fails and you can specify it manually.

-- Example: create a table pointing to a list of compressed CSV files
create foreign table your_table_csv () 
server crunchy_lake_analytics 
options (path 's3://your_bucket_name/data/*.csv.gz');
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When writing CSV with COPY ... TO, the defaults are the same as PostgreSQL, but you can use the options to override them.

The CSV format supports the following additional options:

JSON format

Crunchy Data Warehouse can query, import, and export newline-delimited JSON files. For query and import, it can also support JSON arrays, though they may be less efficient.

-- Example: create a table pointing to a single compressed JSON file
create foreign table your_table_json () 
server crunchy_lake_analytics 
options (path 's3://your_bucket_name/values.json.z', compression 'zstd');
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The JSON format supports the following additional options:

GDAL format

Crunchy Data Warehouse can use GDAL to query or import many different file formats. When using format 'gdal' explicitly, some file formats are recognized from the extension.

-- Example: create an Iceberg table from a .zip shapefile
create table forest_fires ()
using iceberg
with (load_from = 'https://data.fs.usda.gov/geodata/edw/edw_resources/shp/S_USA.FireOccurrence.zip');
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Below is an incomplete list:

The GDAL format supports the following additional options:

When using format GDAL, files are downloaded immediately when creating a table, which may therefore take longer than creating other types of tables.

Log format

The log format is intended to make it easy to query and import raw log files based on a pre-defined template. Currently, only S3 access logs are supported.

create foreign table s3log ()
server crunchy_lake_analytics
options (
  path 's3://my-s3-logs/000000000000/eu-central-1/**',
  format 'log', log_format 's3'
);
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The log format supports the following additional options:

External Iceberg format

Crunchy Data Warehouse can create and manage Iceberg tables, but you may already have Iceberg tables managed by other systems. In that case, you can still query or import from those tables by creating a foreign table that points to the metadata JSON file that represents a specific version of the table:

create foreign table external_iceberg()
server crunchy_lake_analytics
options (path 's3://mybucket/table/v14.metadata.json');
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Note that changes to the external Iceberg table will not be reflected in the foreign table unless you update the path to point to the new metadata file.

External Delta format

Similar to external Iceberg tables, you can query or import external Delta Lake tables:

create foreign table external_delta()
server crunchy_lake_analytics
options (format 'delta', path 's3://mybucket/delta_table/');
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Because Delta Lake tables track the latest version in object storage, changes to the table will automatically become visible in the foreign table.

Note: Delta Lake tables are currently only supported in the same region. Otherwise, you will get an error like:

ERROR:  IO Error: Hit DeltaKernel FFI error (from: While trying to read from delta table: '...'): Hit error: 8 (ObjectStoreError) with message (Error interacting with object store: Generic S3 error: Received redirect without LOCATION, this normally indicates an incorrectly configured region)
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Hugging Face

Hugging Face is a widely used platform for sharing machine learning models and training data. You can query files directly using a hf:// prefix instead of s3. Hugging Face file URLs will look something like this:

https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k/blob/main/data/train-00000-of-00001.parquetCopy

The URL for the system will remove the extra /blob/main/ and a final creation to create a foreign table with Hugging Face data will look like this:

CREATE FOREIGN TABLE word_problems ()
SERVER crunchy_lake_analytics OPTIONS
(path 'hf://datasets/microsoft/orca-math-word-problems-200k/data/train-00000-of-00001.parquet');
Copy

You can also use the wildcard path with the user and project name to create a foreign table for a batch of parquet files:

CREATE FOREIGN TABLE word_problems ()
SERVER crunchy_lake_analytics OPTIONS
(path 'hf://datasets/microsoft/orca-math-word-problems-200k@~parquet/**/*.parquet');
Copy

The Hugging Face URLs currently do not use caching. If you access a data set frequently, we recommend moving the data to S3 or loading it into a Postgres table.

Postgres tables

You can create regular (”heap”) tables in PostgreSQL as usual.

The load_from and definition_from options can also be used when creating heap tables:

create table vegitation()
with (load_from = 'https://data.fs.usda.gov/geodata/edw/edw_resources/shp/S_USA.Activity_RngVegImprove.zip');
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Similarly, you can use the COPY command with a URL to import/export data in a heap table.

An advantage of heap tables over Iceberg tables is that you can create indexes and more efficiently handle insert operations, and selective select / update/ delete operations, as well as certain types of joins.

In addition to built-in storage parameters and the format-specific options, heap tables support the following options in the CREATE TABLE statement: