hip-data-tools 1.6.0

Common utility functions for data engineering usecases

hip-data-tools

© Hipages Group Pty Ltd 2019

Common Python tools and utilities for data engineering, ETL, Exploration, etc. The package is uploaded to PyPi for easy drop and use in various environmnets, such as (but not limited to):

1. Running production workloads
2. ML Training in Jupyter like notebooks
3. Local machine for dev and exploration

Installation

Install from PyPi repo:

pip3 install hip-data-tools

Install from source

pip3 install .

Connect to aws

You will need to instantiate an AWS Connection:

from hip_data_tools.authenticate import AwsConnection

conn = AwsConnection(mode="assume_role", settings={"profile_name": "default"})

# OR if you want to connect using Env Vars:
conn = AwsConnection(mode="standard_env_var", settings={})

# OR if you want custom set of env vars to connect
conn = AwsConnection(mode="custom_env_var", settings={
     "aws_access_key_id_env_var": "aws_access_key_id",
     "aws_secret_access_key_env_var": "aws_secret_access_key"
 })

Using this connection to object you can use the aws utilities, for example aws Athena:

from hip_data_tools.aws.athena import AthenaUtil

au = AthenaUtil(database="default", conn=conn, output_bucket="example", output_key="tmp/scratch/")
result = au.run_query("SELECT * FROM temp limit 10", return_result=True)
print(result)