Dask-DeltaTable

Reading and writing to Delta Lake using Dask engine.

Installation

To install the package:

pip install dask-deltatable

Features:

1. Read the parquet files from Delta Lake and parallelize with Dask
2. Write Dask dataframes to Delta Lake (limited support)
3. Supports multiple filesystems (s3, azurefs, gcsfs)
4. Subset of Delta Lake features:
   • Time Travel
   • Schema evolution
   • Parquet filters
     • row filter
     • partition filter

Not supported

1. Writing to Delta Lake is still in development.
2. optimize API to run a bin-packing operation on a Delta Table.

Reading from Delta Lake

import dask_deltatable as ddt

# read delta table
ddt.read_deltalake("delta_path")

# with specific version
ddt.read_deltalake("delta_path", version=3)

# with specific datetime
ddt.read_deltalake("delta_path", datetime="2018-12-19T16:39:57-08:00")

Accessing remote file systems

To be able to read from S3, azure, gcsfs, and other remote filesystems, you ensure the credentials are properly configured in environment variables or config files. For AWS, you may need ~/.aws/credential; for gcsfs, GOOGLE_APPLICATION_CREDENTIALS. Refer to your cloud provider documentation to configure these.

ddt.read_deltalake("s3://bucket_name/delta_path", version=3)

Accessing AWS Glue catalog

dask-deltatable can connect to AWS Glue catalog to read the delta table. The method will look for AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables, and if those are not available, fall back to ~/.aws/credentials.

Example:

ddt.read_deltalake(catalog="glue", database_name="science", table_name="physics")

Writing to Delta Lake

To write a Dask dataframe to Delta Lake, use to_deltalake method.

import dask.dataframe as dd
import dask_deltatable as ddt

df = dd.read_csv("s3://bucket_name/data.csv")
# do some processing on the dataframe...
ddt.to_deltalake(df, "s3://bucket_name/delta_path")

Writing to Delta Lake is still in development, so be aware that some features may not work.