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

Delta Lake is an open-source storage format that runs on top of existing data lakes. Delta Lake is compatible with processing engines like Apache Spark and provides benefits such as ACID transaction guarantees, schema enforcement, and scalable data handling.

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

Table of contents

• Quick Start
• Get Involved
• Integrations
• Features

Quick Start

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};

#[tokio::main]
async fn main() -> Result<(), DeltaTableError> {
    // open the table written in python
    let table = open_table("./data/delta").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 docker at DockerHub | Docker Repo

Get Involved

We encourage you to reach out, and are committed to provide a welcoming community.

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

Integrations

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

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

Features

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.

Cloud Integrations

Storage	Rust	Python	Comment
Local
S3 - AWS			requires lock for concurrent writes
S3 - MinIO			No lock required when using AmazonS3ConfigKey::ConditionalPut with storage_options = {"conditional_put":"etag"}
S3 - R2			No lock required when using AmazonS3ConfigKey::ConditionalPut with storage_options = {"conditional_put":"etag"}
Azure Blob
Azure ADLS Gen2
Microsoft OneLake
Google Cloud Storage
HDFS

Supported Operations

Operation	Rust	Python	Description
Create			Create a new table
Read			Read data from a table
Vacuum			Remove unused files and log entries
Delete - partitions			Delete a table partition
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
FS check			Remove corrupted files from table

Protocol Support Level

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)