DataSae 0.5.3

Data Quality Framework provides by Jabar Digital Service

DataSae

Data Quality Framework provides by Jabar Digital Service

• Configuration Files
• Checker for Data Quality
  • Command Line Interface (CLI)
  • Python Code
• Converter from Any Data Source to Pandas's DataFrame
  • Local Computer
  • Google Spreadsheet
  • S3
  • SQL
    • MariaDB or MySQL
    • PostgreSQL

Configuration Files

https://github.com/jabardigitalservice/DataSae/blob/46ef80072b98ca949084b4e1ae50bcf23d07d646/tests/data/config.json#L1-L183

https://github.com/jabardigitalservice/DataSae/blob/46ef80072b98ca949084b4e1ae50bcf23d07d646/tests/data/config.yaml#L1-L120

Checker for Data Quality

[!NOTE]
You can use DataSae Column's Function Based on Data Type for adding column checker function data quality in the config file.

pip install 'DataSae[converter,gsheet,s3,sql]'

Command Line Interface (CLI)

datasae --help
 
 Usage: datasae [OPTIONS] FILE_PATH
 
 Checker command.
 Creates checker result based on the configuration provided in the checker section of the data source's configuration file.
╭─ Arguments ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *    file_path      TEXT  The source path of the .json or .yaml file [default: None] [required]                                    │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --config-name                       TEXT  If the config name is not set, it will create all of the checker results [default: None] │
│ --yaml-display    --json-display          [default: yaml-display]                                                                  │
│ --save-to-file-path                 TEXT  [default: None]                                                                          │
│ --help                                    Show this message and exit.                                                              │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Example commands:

datasae DataSae/tests/data/config.yaml # Check all data qualities on configuration
datasae DataSae/tests/data/config.yaml --config-name test_local # Check data quality by config name

[!TIP] Actually, we have example for DataSae implementation in Apache Airflow, but for now it is for private use only. Internal developer can see it at this git repository.

Example results: https://github.com/jabardigitalservice/DataSae/blob/46ef80072b98ca949084b4e1ae50bcf23d07d646/tests/data/checker.json#L1-L432

Python Code

from datasae.converter import Config

# From JSON
config = Config('DataSae/tests/data/config.json')

# From YAML
config = Config('DataSae/tests/data/config.yaml')

# Check all data qualities on configuration
config.checker  # dict result

# Check data quality by config name
config('test_local').checker  # list of dict result
config('test_gsheet').checker  # list of dict result
config('test_s3').checker  # list of dict result
config('test_mariadb_or_mysql').checker  # list of dict result
config('test_postgresql').checker  # list of dict result

Converter from Any Data Source to Pandas's DataFrame

[!NOTE]
Currently support to convert from CSV, JSON, Parquet, Excel, Google Spreadsheet, and SQL.

pip install 'DataSae[converter]'

Local Computer

from datasae.converter import Config

# From JSON
config = Config('DataSae/tests/data/config.json')

# From YAML
config = Config('DataSae/tests/data/config.yaml')

# Local computer file to DataFrame
local = config('test_local')

df = local('path/file_name.csv', sep=',')
df = local('path/file_name.json')
df = local('path/file_name.parquet')
df = local('path/file_name.xlsx', sheet_name='Sheet1')

df = local('path/file_name.csv')  # Default: sep = ','
df = local('path/file_name.json')
df = local('path/file_name.parquet')
df = local('path/file_name.xlsx')  # Default: sheet_name = 'Sheet1'

Google Spreadsheet

https://github.com/jabardigitalservice/DataSae/blob/4308324d066c6627936773ab2d5b990adaa60100/tests/data/creds.json#L1-L12

pip install 'DataSae[converter,gsheet]'

from datasae.converter import Config

# From JSON
config = Config('DataSae/tests/data/config.json')

# From YAML
config = Config('DataSae/tests/data/config.yaml')

# Google Spreadsheet to DataFrame
gsheet = config('test_gsheet')
df = gsheet('Sheet1')
df = gsheet('Sheet1', 'gsheet_id')

S3

pip install 'DataSae[converter,s3]'

from datasae.converter import Config

# From JSON
config = Config('DataSae/tests/data/config.json')

# From YAML
config = Config('DataSae/tests/data/config.yaml')

# S3 object to DataFrame
s3 = config('test_s3')

df = s3('path/file_name.csv', sep=',')
df = s3('path/file_name.json')
df = s3('path/file_name.parquet')
df = s3('path/file_name.xlsx', sheet_name='Sheet1')

df = s3('path/file_name.csv', 'bucket_name')  # Default: sep = ','
df = s3('path/file_name.json', 'bucket_name')
df = s3('path/file_name.parquet', 'bucket_name')
df = s3('path/file_name.xlsx', 'bucket_name')  # Default: sheet_name = 'Sheet1'

SQL

pip install 'DataSae[converter,sql]'

[!IMPORTANT] For MacOS users, if pip install failed at mysqlclient, please run this and retry to install again after that.

brew install mysql

MariaDB or MySQL

from datasae.converter import Config

# From JSON
config = Config('DataSae/tests/data/config.json')

# From YAML
config = Config('DataSae/tests/data/config.yaml')

# MariaDB or MySQL to DataFrame
mariadb_or_mysql = config('test_mariadb_or_mysql')
df = mariadb_or_mysql('select 1 column_name from schema_name.table_name;')
df = mariadb_or_mysql('path/file_name.sql')

PostgreSQL

from datasae.converter import Config

# From JSON
config = Config('DataSae/tests/data/config.json')

# From YAML
config = Config('DataSae/tests/data/config.yaml')

# PostgreSQL to DataFrame
postgresql = config('test_postgresql')
df = postgresql('select 1 column_name from schema_name.table_name;')
df = postgresql('path/file_name.sql')