MongoDB is a NoSQL database that stores data in a flexible, JSON-like format. Here's an explanation of the MongoDB-related aspects of your project:
MongoDB Basics:
MongoDB is a NoSQL database that is known for its flexibility in storing unstructured or semi-structured data. In your project, you will be working with MongoDB, so it's important to understand its key concepts, such as documents, collections, and databases. Read Operations:
You will be reading data from MongoDB. MongoDB provides various methods for querying data, such as find(), which allows you to retrieve documents that match specific criteria. Insert Operations:
MongoDB supports inserting new documents into collections. You can use the insertOne() or insertMany() methods to add data. Update Operations:
Updating data in MongoDB is done using the updateOne() or updateMany() methods. You can modify specific fields in existing documents. Delete Operations:
MongoDB provides methods like deleteOne() and deleteMany() to remove documents from a collection based on certain conditions. Aggregation Pipelines:
MongoDB's aggregation framework allows you to perform complex data transformations and analysis. Aggregation pipelines consist of a series of stages that process and filter data. Part 2: Apache Airflow DAG for Data Integration
Apache Airflow is an open-source platform for orchestrating complex data workflows. You're using it to create a Directed Acyclic Graph (DAG) that reads data from MongoDB and loads it into PostgreSQL. Here's an explanation of this part:
Apache Airflow:
Apache Airflow is a workflow automation tool that allows you to define, schedule, and monitor data pipelines. Directed Acyclic Graph (DAG):
In Airflow, a DAG represents a workflow, and it defines the sequence of tasks and their dependencies. Your DAG will consist of two main tasks: one for reading data from MongoDB and another for loading it into PostgreSQL. Reading Data from MongoDB:
You will define a task in your DAG that reads data from MongoDB. This task might use libraries like PyMongo to connect to MongoDB and fetch the data. Loading Data into PostgreSQL:
Another task in your DAG will be responsible for loading data into PostgreSQL. You'll need to set up a connection to your PostgreSQL database and insert the data fetched from MongoDB. Scheduling and Monitoring:
Airflow allows you to schedule when your DAG runs and provides a dashboard for monitoring the progress and status of your workflows. By creating an Airflow DAG, you automate the process of regularly moving data from MongoDB to PostgreSQL, ensuring that it's up to date.