datalake-utils 2.0.5

Manipulate data on Amazon S3 using Apache Hadoop filesystem compability

Data Lake Utility

Package to manipulate data from/into Amazon S3 using partitions compatible with Apache Hadoop filesystem. At this moment, this package was conceived to handle JSON and Parquet formats. That being said, it expects a Pandas DataFrame.

Data will be written into Amazon S3 as a multi-line JSON string, compressed as GZIP.

Features

Convert list of dictionaries...

[
    {"brand": "Ford","model": "Mustang","year": 1965},
    {"brand": "Pontiac","model": "GTO","year": 1964},
    {"brand": "Lamborghini","model": "Miura","year": 1966}
]

...to multi-line JSON string

{"brand": "Ford","model": "Mustang","year": 1965}
{"brand": "Pontiac","model": "GTO","year": 1964}
{"brand": "Lamborghini","model": "Miura","year": 1966}

Manipulate data on Amazon S3 bucket based on schema, table and partitions

motor_vehicles/
`-- cars
    |-- brand=Ford
    |   `-- year=1965
    |       `-- 34e40fce-444e-11ed-8e00-acde48001122.json
    |-- brand=Pontiac
    |   `-- year=1964
    |       `-- a4eb3018-4458-11ed-b0e9-acde48001122.json
    `-- brand=Lamborghini
        `-- year=1966
            `-- 2c0fea6c-444e-11ed-969f-acde48001122.json

How to use

This package does not slice data into partitions defined. You must handle slicing of data to write into partitions desired. The example below will assume the following file structure:

motor_vehicles/
`-- cars
    `-- decade=1960s
        `-- 2c0fea6c-444e-11ed-969f-acde48001122.json.gzip

from datalake_utils.utils import DataLake
import pandas

data = [
    {
        "brand": "Ford",
        "model": "Mustang",
        "year": 1965
    },
    {
        "brand": "Pontiac",
        "model": "GTO",
        "year": 1964
    },
    {
        "brand": "Lamborghini",
        "model": "Miura",
        "year": 1966
    }
]

datalake = DataLake(
    bucket_name="vehicles",
    schema="motor_vehicles",
    table="cars",
    partitions=[
        {
            "key": "decade",
            "value": "1960s"
        }
    ],
)

datalake.append_to_s3(data=pandas.DataFrame(data), file_format="json")

retrieved_data = datalake.read_from_s3(file_format="json")
print(retrieved_data)

datalake.delete_from_s3()