MongoDB Database Developer Course In Python

Set up MongoDB and your coding environment, choose between community edition or Atlas free tier, and use Anaconda to configure the Python environment and install dependencies.

Install MongoDB on Windows 10 using the Community Server Edition MSI and then install MongoDB Compass for a graphical data interface.

install and set up anaconda on Windows, create a conda environment named mongodb_py with Python 3.12 and key packages, install motor via pip, and verify with a jupyter notebook.

Install MongoDB on Ubuntu 20.04 via apt, import the public key, add a sources list, update, and install MongoDB, then install MongoDB Compass and prepare to configure development environment.

Install and configure Anaconda on Ubuntu, create a conda environment named MongoDB Pi with Python 3.8 and packages numpy, pandas, pymongo, jupyter, and motor, then use Jupyter notebooks.

Install MongoDB on macOS Mojave using Homebrew, verify command line tools, and install MongoDB Compass via the OSX DMG. Then configure the development environment and start using Compass.

Install Anaconda on macOS, verify with SHA-256 checksum, then create a MongoDB_pi conda environment with Python 3.8 and libraries NumPy, Pandas, Mongo, Jupyter.

Set up a MongoDB Atlas cluster, configure provider and region, add access controls, load a sample dataset, and connect via Compass or shell to explore databases.

Explore the MongoDB usage overview after installation, learn proper usage, interact via the command line, and see how MongoDB connects to various programming language drivers.

Start and configure a Windows MongoDB server: create the data/db path, run mongod, connect with MongoDB Compass using localhost:27017, view startup logs, and manage the server from PowerShell.

Start and configure a MongoDB server on Ubuntu, resolve data directory not found errors, use mongod with dbpath, and connect via Compass to a local database on localhost:27017.

Start and configure a local MongoDB server on macOS by creating the data directory, fixing permissions, and launching mongod, then connect with Compass on localhost:27017.

Learn to use MongoDB's command line interface and MongoSH to connect with Compass, switch databases, and perform operations like show dbs, show collections, and insert one in a JavaScript-based CLI.

Explore how MongoDB provides language-specific drivers for Python, detailing PyMongo for synchronous code and Motor for asynchronous workloads; learn to navigate PyMongo API documentation and driver resources.

Explore MongoDB basics and CRUD operations, and understand how MongoDB stores data. Practice these commands using a command-line interface and the Compass UI before moving to Python.

Explore MongoDB's document oriented model with collections of JSON based documents, flexible fields, and arrays. Note 16MB BSON limits, 100 levels of embedded documents, and store large files off MongoDB.

Explore crud operations—create, read, update, delete—as the core building blocks of databases, with MongoDB translating them in its framework and enabling data creation without a defined schema.

Master CRUD operations in MongoDB using the Compass app and the Mongo shell, including insert, find, update, and delete on collections.

Learn how to perform create, read, update, and delete operations in the compass app, including creating databases and collections, inserting documents, filtering, sorting, and deleting entries.

Explore MongoDB basics in Python with PyMongo, follow along in Jupyter notebooks using template and solution notebooks.

Explore MongoDB CRUD in Python using PyMongo, creating a MongoClient, connecting to localhost:27017, and performing insert_one, find_one, update_one, delete_one, and delete_many on the myStore.users collection in a Jupyter notebook.

Master insert_one and insert_many in PyMongo to add single or multiple documents, verify with acknowledged and InsertedId, and handle duplicates and partial failures.

Explore how find_one retrieves a single entry and how find returns a cursor for iterating results, with sorting, limit, skip, projection, distinct, and count_documents.

Learn to model Python classes for game characters and items, convert them to dictionaries, and store and retrieve them in MongoDB, reconstructing objects from database data.

Develop clean, readable Python code for MongoDB projects by following naming conventions, descriptive function names, converting MongoDB dictionaries to objects, and using docstrings to document inputs and returns.

Learn to use MongoDB operators in PyMongo to filter queries, including comparison operators, logical operators, type checks, exists, and regex, with practical examples.

Learn how to update MongoDB documents with PyMongo, covering update, update one, update many, replace one, upsert, and update operators like set, current date, rename, mul, and unset.

Explore pymongo delete functionality, from the deprecated remove to delete_one and delete_many, using filters and reviewing delete result attributes like acknowledged and deleted_count.

Learn to store and manipulate dates and times in MongoDB with Python, using datetime objects, timestamps, UTC handling, time zones, and basic insert, find, and sort operations.

Extend a Python video game with MongoDB by managing players and NPC data, performing updates, inserts, and deletes, and logging battles with health, energy, and gold changes in real time.

Master arrays in MongoDB with Python: insert items with versions, query by array content and length, and update using set, push, pop, pull, and add to set.

Master the aggregation framework in pymongo to build complex queries that relate and format data from MongoDB, translating Python data types. Use a template notebook to explore all aggregation stages.

Explore the MongoDB aggregation framework to filter and restructure data across collections using a multi-stage pipeline. Learn to apply match, lookup, addFields, group, and sort stages to produce JSON results.

Learn how to use the match and unset aggregation stages in MongoDB, with projections and multi-stage pipelines, implemented in Python notebooks and demonstrated in the Mongo shell.

Master limit, skip, sort, and count in MongoDB's aggregation pipeline using Python, showing how a command cursor works and how stage order affects results.

Explore array handling in MongoDB aggregation pipelines, using size to count array elements, expression operators and field paths, and unwind to flatten arrays into separate documents.

Learn how the group stage clusters distinct entries by a field, using accumulators like count, sum, first, last, push, addToSet, and root, with unwind and tag-based grouping.

Execute three mini challenges to reinforce aggregation pipelines in Python using PyMongo: filter by the beauty tag, unwind tags, and count unique sellers per tag, then sort by tag.

Learn how the addFields stage injects a new field into pipeline data without altering original documents, then use the sample stage to select a random subset for testing.

Learn to join data across MongoDB collections with lookup and unionWith, linking products to sellers and filtering results with regex match options.

Explore out and merge aggregation stages in MongoDB to save results to a collection and merge or overwrite data by id, with insert, replace, keep, discard, or fail options.

Learn to use the conditional operator for if-else logic in aggregation pipelines, including EQ checks like name equals Bob, and apply date operators now, date diff, and expressions operator.

Explore the if null, type, and switch operators in MongoDB to handle missing fields, reveal data types, and apply conditional logic in aggregation pipelines.

Build MongoDB aggregation pipelines to identify top sellers by summing orders and joining seller data, then apply date add and conditional logic to merge premium expiration dates into users.

Learn MongoDB database tools for import and export, and dump and restore, using JSON, CSV, or BSON to move data between databases and boost performance.

Create and optimize indexes in MongoDB to speed up read queries, using single and compound indexes, while considering overhead and verifying performance with explain plans.

Explore how sharding and replication in Atlas split data across multiple machines, improve redundancy, and boost read performance with primary and secondary nodes.

Discover how MongoDB's flexible schema works with data type analysis in Compass, and implement robust validation rules to enforce data integrity using strict, moderate, and warning or error actions.

Explore MongoDB security basics by enabling authentication to control access for database admins, backend developers, and applications. Learn to enforce proper permissions so only authorized entities interact with the database.

Assign users and roles in MongoDB to control permissions, granting read or read-write access to the product collection and using admin roles for secure management of sub applications and API.

Create admin and application users in MongoDB, assign roles with specific privileges, and define custom roles using PyMongo and the mongo shell for secure authentication.

Explore advanced features of MongoDB, covering miscellaneous use cases and common functionality for Python MongoDB applications. Jump around to lectures to gain practical knowledge.

Explore running MongoDB in Docker, attach a host volume for data persistence, connect with Compass, and set up a Python development environment using Docker for pymongo and Jupyter.

Learn to convert pandas dataframes to dictionaries for inserting into MongoDB, and to read MongoDB documents into dataframes using records format and reset index.

Learn to store and manage stock price time series in MongoDB with Python, using time series collections, metadata like ticker, and data expiration for efficient querying.

Learn to design a REST-based API in Python to access MongoDB using FastAPI and Uvicorn, implementing get, post, put, patch, and delete operations with Pymongo.

Learn local networking for a MongoDB instance by binding its IP and port 27017, connecting across the same subnet, and performing CRUD via Compass with a MongoDB URI.

Explore the motor async MongoDB driver, using coroutines for non-blocking access via tornado or async IO, with async insert and find one and event loop usage.

Build MongoDB and Python expertise by mastering CRUD, the aggregation pipeline, and security with roles and permissions, then showcase projects like stock market data and games in a portfolio.