raster-loader 0.11.3

Python library for loading GIS raster data to standard cloud-based data warehouses that don't natively support raster data.

raster-loader

Python library for loading GIS raster data to standard cloud-based data warehouses that don't natively support raster data.

Raster Loader is currently tested on Python 3.9, 3.10, 3.11 and 3.12.

Documentation

The Raster Loader documentation is available at raster-loader.readthedocs.io.

Install

pip install -U raster-loader

To install from source:

git clone https://github.com/cartodb/raster-loader
cd raster-loader
pip install -U .

Tip: In most cases, it is recommended to install Raster Loader in a virtual environment. Use venv to create and manage your virtual environment.

The above will install the dependencies required to work with all cloud providers (BigQuery, Snowflake, Databricks). If you only want to work with one of them, you can install the dependencies for each separately:

pip install -U raster-loader[bigquery]
pip install -U raster-loader[snowflake]
pip install -U raster-loader[databricks]

For Databricks, you will also need to install the databricks-connect package corresponding to your Databricks Runtime Version. For example, if your cluster uses DBR 15.1, install:

pip install databricks-connect==15.1

You can find your cluster's DBR version in the Databricks UI under Compute > Your Cluster > Configuration > Databricks Runtime version. Or you can run the following SQL query from your cluster:

   SELECT current_version();

To verify the installation was successful, run:

carto info

This command will display system information including the installed Raster Loader version.

Prerequisites

Before using Raster Loader with each platform, you need to have the following set up:

BigQuery:

• A GCP project
• A BigQuery dataset
• The GOOGLE_APPLICATION_CREDENTIALS environment variable set to the path of a JSON file containing your BigQuery credentials. See the GCP documentation for more information.

Snowflake:

• A Snowflake account
• A Snowflake database
• A Snowflake schema

Databricks:

• A Databricks server hostname
• A Databricks cluster id
• A Databricks token

Raster files

The input raster must be a GoogleMapsCompatible raster. You can make your raster compatible by converting it with the following GDAL command:

gdalwarp -of COG -co TILING_SCHEME=GoogleMapsCompatible -co COMPRESS=DEFLATE -co OVERVIEWS=IGNORE_EXISTING -co ADD_ALPHA=NO -co RESAMPLING=NEAREST -co BLOCKSIZE=512 <input_raster>.tif <output_raster>.tif

Your raster file must be in a format that can be read by GDAL and processed with rasterio.

Usage

There are two ways you can use Raster Loader:

• Using the CLI by running carto in your terminal
• Using Raster Loader as a Python library (import raster_loader)

CLI

After installing Raster Loader, you can run the CLI by typing carto in your terminal.

Currently, Raster Loader allows you to upload a local raster file to BigQuery, Snowflake, or Databricks tables. You can also download and inspect raster files from these platforms.

Uploading Raster Data

Examples for each platform:

BigQuery:

carto bigquery upload \
    --file_path /path/to/my/raster/file.tif \
    --project my-gcp-project \
    --dataset my-bigquery-dataset \
    --table my-bigquery-table \
    --overwrite

Snowflake:

carto snowflake upload \
    --file_path /path/to/my/raster/file.tif \
    --database my-snowflake-database \
    --schema my-snowflake-schema \
    --table my-snowflake-table \
    --account my-snowflake-account \
    --username my-snowflake-user \
    --password my-snowflake-password \
    --overwrite

Note that authentication parameters are explicitly required since they are not set up in the environment.

Databricks:

carto databricks upload \
    --file_path /path/to/my/raster/file.tif \
    --catalog my-databricks-catalog \
    --schema my-databricks-schema \
    --table my-databricks-table \
    --server-hostname my-databricks-server-hostname \
    --cluster-id my-databricks-cluster-id \
    --token my-databricks-token \
    --overwrite

Note that authentication parameters are explicitly required since they are not set up in the environment.

Additional features include:

• Specifying bands with --band and --band_name
• Enabling compression with --compress and --compression-level
• Chunking large uploads with --chunk_size

Inspecting Raster Data

To inspect a raster file stored in any platform, use the describe command:

BigQuery:

carto bigquery describe \
    --project my-gcp-project \
    --dataset my-bigquery-dataset \
    --table my-bigquery-table

Snowflake:

carto snowflake describe \
    --database my-snowflake-database \
    --schema my-snowflake-schema \
    --table my-snowflake-table \
    --account my-snowflake-account \
    --username my-snowflake-user \
    --password my-snowflake-password

Note that authentication parameters are explicitly required since they are not set up in the environment.

Databricks:

carto databricks describe \
    --catalog my-databricks-catalog \
    --schema my-databricks-schema \
    --table my-databricks-table \
    --server-hostname my-databricks-server-hostname \
    --cluster-id my-databricks-cluster-id \
    --token my-databricks-token

Note that authentication parameters are explicitly required since they are not set up in the environment.

For a complete list of options and commands, run carto --help or see the full documentation.

Using Raster Loader as a Python library

After installing Raster Loader, you can use it in your Python project.

First, import the corresponding connection class for your platform:

# For BigQuery
from raster_loader import BigQueryConnection

# For Snowflake
from raster_loader import SnowflakeConnection

# For Databricks
from raster_loader import DatabricksConnection

Then, create a connection object with the appropriate parameters:

# For BigQuery
connection = BigQueryConnection('my-project')

# For Snowflake
connection = SnowflakeConnection('my-user', 'my-password', 'my-account', 'my-database', 'my-schema')

# For Databricks
connection = DatabricksConnection('my-server-hostname', 'my-token', 'my-cluster-id')

Uploading a raster file

To upload a raster file, use the upload_raster function:

connection.upload_raster(
    file_path = 'path/to/raster.tif',
    fqn = 'database.schema.tablename'
)

This function returns True if the upload was successful.

You can enable compression of the band data to reduce storage size:

connection.upload_raster(
    file_path = 'path/to/raster.tif',
    fqn = 'database.schema.tablename',
    compress = True,  # Enable gzip compression of band data
    compression_level = 3  # Optional: Set compression level (1-9, default=6)
)

Inspecting a raster file

To access and inspect a raster file stored in any platform, use the get_records function:

records = connection.get_records(
    fqn = 'database.schema.tablename'
)

This function returns a DataFrame with some samples from the raster table (10 rows by default).

For more details, see the full documentation.

Development

See CONTRIBUTING.md for information on how to contribute to this project.

ROADMAP.md contains a list of features and improvements planned for future versions of Raster Loader.

Releasing

1. Create and merge a release PR updating the CHANGELOG

• Branch: release/X.Y.Z
• Title: Release vX.Y.Z
• Description: CHANGELOG release notes

Example:

## [0.7.0] - 2024-06-02

### Added
- Support raster overviews (#140)

### Enhancements
- increase chunk-size to 10000 (#142)

### Bug Fixes
- fix: make the gdalwarp examples consistent (#143)

2. Create and push a tag vX.Y.Z

This will trigger an automatic workflow that will publish the package at https://pypi.org/project/raster-loader.

3. Create the GitHub release

Go to the tags page (https://github.com/CartoDB/raster-loader/tags), select the release tag and click on "Create a new release"

• Title: vX.Y.Z
• Description: CHANGELOG release notes

Example:

### Added
- Support raster overviews (#140)

### Enhancements
- increase chunk-size to 10000 (#142)

### Bug Fixes
- fix: make the gdalwarp examples consistent (#143)