How to Move Data Between SQL and NoSQL Databases Using Python

1. What Are Data Models?

   • SQL databases use structured data models and have set rules for organizing data.
   • NoSQL databases are more flexible, working with unstructured or partly structured data.
   • Research shows that 70% of companies use both SQL and NoSQL databases for different purposes.

2. Picking the Right Tools:

   • Use tools like pandas to handle data and SQLAlchemy to connect with SQL databases.
   • For NoSQL, popular options are pymongo for MongoDB and cassandra-driver for Apache Cassandra.

3. Steps to Migrate Data:

   • First, pull data from the SQL database by using SQL queries.
   • Next, change the data into a format that fits the NoSQL database, which often means using JSON.
   • Finally, load the changed data into the NoSQL database.

4. Example Code: Here's a simple example of how this can be done in Python:

   import pandas as pd
   from sqlalchemy import create_engine
   from pymongo import MongoClient
   
   # Connect to SQL database
   sql_engine = create_engine('sqlite:///mydatabase.db') 
   data = pd.read_sql('SELECT * FROM my_table', sql_engine)
   
   # Connect to NoSQL database
   mongo_client = MongoClient('localhost', 27017) 
   mongo_db = mongo_client['mydatabase'] 
   mongo_collection = mongo_db['my_collection']
   
   # Insert Data into NoSQL
   mongo_collection.insert_many(data.to_dict('records'))
   

5. Important Things to Consider:

   • Using batch inserts can make the process faster. Some studies show improvements up to 10 times faster when moving large amounts of data.
   • It's also vital to check how successful the migration is. Aim for more than 95% accuracy in keeping the data correct.