Usage

This page provides a detailed explanation of hamana's core functionalities and an overview of its available connectors. By the end, you'll have a clear understanding of how to utilize hamana effectively in your data analysis workflows.

SQLite Database Management

hamana is designed to facilitate data analysis using an internal SQLite database. Here's how it works:

Connecting to the Database

Use the connect function to create or connect to an SQLite database. By default, the database is loaded in memory, making it ideal for quick analysis and small datasets. For more complex analyses or larger datasets, you can specify a .db file path, which will either be created or loaded automatically.

• In-memory example:

import hamana as hm

# connect to an in-memory SQLite database
hm.connect()

• File-based example:

# connect to an SQLite database stored in a file
hm.connect("data_analysis.db")

Disconnecting from the Database

Once you're done with your analysis, you can disconnect from the database using the disconnect function:

# disconnect from the database
hm.disconnect()

Note: hamana ensures consistency by allowing only one active database instance at a time. This design is particularly suited for workflows in Jupyter Notebooks.

Data Connectors

The power of hamana lies in its ability to load data into the SQLite database from various sources using connectors. These connectors are categorized based on the type of data source:

Available Connectors

1. Relational Database Connectors

hamana supports several relational databases, including Oracle, Teradata, SQLite (external databases) and many others planned for future releases.

Example:

from hamana.connector.db import Oracle

# extract data from an Oracle database
oracle_db = Oracle.new(
    host = "localhost",
    port = 1521,
    user = "user",
    password = "password"
)
employees = oracle_db.execute("SELECT * FROM employees")

2. File Connectors

hamana can load data from various file types, such as CSV, JSON, Fixed-width positional files.

Example:

from hamana.connector.file import CSV

# load data from a CSV file
csv_file = CSV("data/orders.csv")
orders = csv_file.execute()

Modular Design

Connectors are modularly organized under hamana.connector with extensions for specific types of sources:

• .db: For database connectors.
• .file: For file connectors.

This structure ensures scalability, allowing more connectors to be added over time.

Connectors Overview

The following table provides a detailed summary of the available connectors:

Type	Sources	Path	Connector
Relational Database	Oracle	hamana.connector.db	Oracle
Relational Database	Teradata	hamana.connector.db	Teradata
Relational Database	SQLite	hamana.connector.db	SQLite
File Connector	CSV	hamana.connector.file	CSV
File Connector	JSON	hamana.connector.file	JSON
File Connector	Positional Files	hamana.connector.file	PositionalFile

Each connector's page provides detailed usage instructions and examples.