AWS metadata as dataframes
Project description
Submodules
awsdf.aws module
This module enables connecting to AWS and extracting metadata in pandas dataframes.
Installing from PyPI: pip install -U awsdf
USAGE:
import awsdf
aws_account = awsdf.Account(profile_name=”<PROFILE_NAME>”)
glue_databases_df = aws_account.glue_get_databases()
class awsdf.aws.Account(aws_access_key_id=None, aws_secret_access_key=None, aws_session_token=None, region_name=None, profile_name=None)
Instantiate class object for connecting to AWS and retriving metadata from AWS
__init__(aws_access_key_id=None, aws_secret_access_key=None, aws_session_token=None, region_name=None, profile_name=None)
Provide access keys OR Profile name to connect to AWS account. Keys take preceedence
Parameters:
aws_access_key_id (string) – AWS access key ID
aws_secret_access_key (string) – AWS secret access key
aws_session_token (string) – AWS temporary session token
region_name (string) – AWS region
profile_name (string) – AWS profile name
glue_get_jobs() -> DataFrame
Get AWS Glue jobs
- Returns:
dataframe
glue_get_job_history(job_name, no_of_runs=1) -> DataFrame
Retrieve glue job history
- Arguments:
job_name – Name of job to retrive history
- Keyword Arguments:
no_of_runs – No of runs to retrive in descending order (default: {1})
- Returns:
dataframe
glue_get_databases() -> DataFrame
Get AWS Glue jobs
- Returns:
dataframe
glue_get_tables(dbname=None) -> DataFrame
Get AWS Glue tables
- Keyword Arguments:
dbname – Database Name for which to retrive tables (default: {None})
- Returns:
dataframe
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