What is the process of integrating API data into MongoDB using Spark/Python?

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The process of integrating API data into MongoDB using Spark/Python involves the following steps:

1. Install the necessary dependencies: Install the PyMongo and PySpark libraries. These libraries will be used to connect to MongoDB and handle data using Spark.

2. Import the necessary libraries: The necessary libraries to be imported are: pyspark.sql, pymongo, requests, and json.

3. Connect to the API: Use the requests library to establish a connection to the API endpoint.

4. Retrieve the data: Use the data from the API endpoint and retrieve the data using requests.get.

5. Convert data to JSON format: Convert the API data to JSON format using the json library.

6. Create a Spark DataFrame: Use the SparkSession to create a DataFrame from the JSON data.

7. Connect to MongoDB: Use the PyMongo library to connect to MongoDB.

8. Write data to MongoDB: Write the data to MongoDB using the PyMongo library.

9. Close connections: Always close the connections after you're done with the program to avoid any memory leaks.

Overall, the process involves connecting to the API, retrieving data, converting it into a Spark DataFrame format, connecting to MongoDB, and writing data to it. These steps should be followed sequentially to effectively integrate API data into MongoDB using Spark/Python.