Revise data logging section for AWS Lambda and PyMongo

Updated the section title and content to reflect the use of AWS Lambda and PyMongo for data logging instead of the deprecated MongoDB Data API.

Author: sgbaird

docs/courses/hello-world/1.5-data-logging.md

@@ -20,7 +20,7 @@ Chemistry and materials research data is precious. By making your data **F**inda
 
 *Defining FAIR Data in materials science. Reproduced from https://doi.org/10.1557/s43577-023-00498-4*
 
-### Writing to MongoDB Using the Data API
+### Writing to MongoDB Using AWS Lambda and PyMongo
 
 For storing our data, we will be using [MongoDB](https://www.mongodb.com/), a popular ["NoSQL"](https://www.mongodb.com/nosql-explained) database. It's important to note that MongoDB is one of many choices for databases (in contrast to MongoDB as a document-based database, e.g., [PostgreSQL](https://www.postgresql.org/) for relational databases, which can be [hosted via AWS](https://aws.amazon.com/rds/postgresql/), and [Neo4j](https://neo4j.com/) for graph-based databases). MongoDB is a document-oriented database, which means it stores data in JSON-like documents. MongoDB is a popular choice for internet-of-things (IoT) applications due to its ease of setup, use, and scalability. Unfortunately, MongoDB Atlas suddenly deprecated their [Data API](https://docs.atlas.mongodb.com/data-api/) that allowed for direct reading and writing of data from devices like microcontrollers. Instead, we'll use something called AWS Lambda as an intermediary. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. It's a nice choice for creating lightweight APIs and handling intermittent requests, which works well for our microcontroller application.