splitgraph/delta-rs: A native Rust library for Delta Lake, with bindings into Python and Ruby. · GitHub

A native Rust library for Delta Lake, with bindings to Python
Python docs · Rust docs · Report a bug · Request a feature · Roadmap

The Delta Lake project aims to unlock the power of the Deltalake for as many users and projects as possible by providing native low-level APIs aimed at developers and integrators, as well as a high-level operations API that lets you query, inspect, and operate your Delta Lake with ease.

Source	Downloads	Installation Command	Docs
PyPi		pip install deltalake	Docs
Crates.io		cargo add deltalake	Docs

The deltalake library aims to adopt patterns from other libraries in data processing, so getting started should look familiar.

from deltalake import DeltaTable, write_deltalake
import pandas as pd

# write some data into a delta table
df = pd.DataFrame({"id": [1, 2], "value": ["foo", "boo"]})
write_deltalake("./data/delta", df)

# Load data from the delta table
dt = DeltaTable("./data/delta")
df2 = dt.to_pandas()

assert df.equals(df2)

The same table can also be loaded using the core Rust crate:

use deltalake::{open_table, DeltaTableError};
use url::Url;

#[tokio::main]
async fn main() -> Result<(), DeltaTableError> {
    // open the table written in python
    let delta_path = Url::from_directory_path("/abs/data/delta").unwrap();
    let table = open_table(delta_path).await?;

    // show all active files in the table
    let files: Vec<_> = table.get_file_uris()?.collect();
    println!("{files:?}");

    Ok(())
}

You can also try Delta Lake in Docker at DockerHub | Docker Repo

The delta-rs team is committed to providing a welcoming community.

• Join us in our Slack workspace.
• Report an issue.
• Looking to contribute?
  • Read our contributing guide.
  • See our good first issues.

Libraries and frameworks that interoperate with delta-rs - in alphabetical order.

• AWS SDK for Pandas
• ballista
• datafusion
• Daft
• Dask
• datahub
• DuckDB
• polars
• Ray

The following section outlines some core features like supported storage backends and operations that can be performed against tables. The state of implementation of features outlined in the Delta protocol is also tracked.

Storage	Rust	Python	Comment
Local
S3 - AWS
S3 - MinIO
S3 - R2
Azure Blob
Azure ADLS Gen2
Microsoft OneLake
Google Cloud Storage
HDFS
LakeFS

Operation	Rust	Python	Description
Create			Create a new table
Read			Read data from a table
Vacuum			Remove unused files and log entries
Delete - predicates			Delete data based on a predicate
Optimize - compaction			Harmonize the size of data file
Optimize - Z-order			Place similar data into the same file
Merge			Merge a target Delta table with source data
Update			Update values from a table
Add Column			Add new columns or nested fields
Add Feature			Enable delta table features
Add Constraints			Set delta constraints, to verify data on write
Drop Constraints			Removes delta constraints
Set Table Properties			Set delta table properties
Convert to Delta			Convert parquet table to delta table
FS check			Remove corrupted files from table
Restore			Restores table to previous version state

Writer Version	Requirement	Status
Version 2	Append Only Tables
Version 2	Column Invariants
Version 3	Enforce delta.checkpoint.writeStatsAsJson
Version 3	Enforce delta.checkpoint.writeStatsAsStruct
Version 3	CHECK constraints
Version 4	Change Data Feed
Version 4	Generated Columns
Version 5	Column Mapping
Version 6	Identity Columns
Version 7	Table Features

Reader Version	Requirement	Status
Version 2	Column Mapping
Version 3	Table Features (requires reader V7)