Python Data Persistence - Introduction

Neha Kumawat

a year ago

Data Persistence | Insideaiml
Data Persistence | Insideaiml

Overview of Python - Data Persistence

During the course of using any software application, user provides some data to be processed. The data may be input, using a standard input device (keyboard) or other devices such as disk file, scanner, camera, network cable, WiFi connection, etc.
Data so received, is stored in computer’s main memory (RAM) in the form of various data structures such as, variables and objects until the application is running. Thereafter, memory contents from RAM are erased.
However, more often than not, it is desired that the values of variables and/or objects be stored in such a manner, that it can be retrieved whenever required, instead of again inputting the same data.
The word ‘persistence’ means "the continuance of an effect after its cause is removed". The term data persistence means it continues to exist even after the application has ended. Thus, data stored in a non-volatile storage medium such as, a disk file is a persistent data storage.
In this tutorial, we will explore various built-in and third party Python modules to store and retrieve data to/from various formats such as text file, CSV, JSON and XML files as well as relational and non-relational databases.
Using Python’s built-in File object, it is possible to write string data to a disk file and read from it. Python’s standard library, provides modules to store and retrieve serialized data in various data structures such as JSON and XML.
Python’s DB-API provides a standard way of interacting with relational databases. Other third party Python packages, present interfacing functionality with NOSQL databases such as MongoDB and Cassandra.
This tutorial also introduces the ZODB database which is a persistence API for Python objects. Microsoft Excel format is a very popular data file format. In this tutorial, we will learn how to handle the .xlsx file through Python.
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