The Complete MongoDB Course

Set up a MongoDB Atlas cluster on the free tier by signing up, verifying your email, deploying a shared cluster, and creating an admin user with 0.0.0.0/0 network access.

Install the mongo shell and MongoDB database tools on Mac and Windows, configure your PATH, and verify with --help commands to enable import and export tasks.

Install MongoDB Compass to connect your Atlas cluster via a graphical interface; Mac: download from the official site, drag to applications, open, and allow; Windows: download the MSI and install.

Load sample data with MongoDB Compass, connect to Atlas via the connection string, and import JSON files into the sample_training database and its companies and grades collections.

Navigate the MongoDB Atlas interface, rename projects, access the sandbox cluster, and connect with MongoDB Shell or MongoDB Compass while reviewing monitoring and collections.

Learn to import and export MongoDB collections using Compass, create databases and collections, insert documents, and choose json or csv formats for export and import.

Learn to navigate directories in the terminal or command prompt using pwd (print working directory) and cd, then list contents with ls or dir to match your system (Windows differences).

Export and import data in Atlas clusters using mongoexport and mongoimport from the terminal. Build and use the uri, credentials, database, and collection with an output file to json.

Learn to import and export data with mongo dump and mongo restore, using binary json for speed or json for readability, for databases or clusters.

Create and delete databases and collections in MongoDB using Atlas, Compass, and the shell; learn to use create collection, use, show dbs, and drop commands across interfaces.

Connect to the MongoDB shell and switch to the sample_training database. Use findOne and find with a query and projection, filtering by class_id and student_id in grades.

Learn to run MongoDB shell queries on the inspections collection, using find one and filters in Atlas Filter. Filter by result: violation issued and sector: home improvement contractor-100.

Explore how the dollar symbol prefixes operators in MongoDB query language, use $lt for less than in find queries, and structure salary filters with nested key-value syntax.

Learn MongoDB comparison operators ($eq, $ne, $gt, $gte, $lt, $lte, $in, $nin) and how to apply them to filter documents, with examples on trip duration and start station IDs.

Master the four MongoDB logical operators—$and, $or, $nor, and $not—using arrays and field conditions to build precise queries, with practical examples from the shell.

Learn to construct MongoDB queries in the shell and Atlas, using $in, $or, and range operators to filter documents in inspections and companies collections.

Explore how the $expr operator compares fields within a document and supports conditional logic in the aggregation pipeline, with examples like matching durations and equal source and destination airports.

Explore element operators in MongoDB, including $exists and $type, learn syntax to filter documents by field existence and data type, and see practical examples with strings and null values.

Master cursor methods in MongoDB, including count, sort, limit, skip, and size. See use in a trips collection with examples of filtering by duration and sorting by start station name.

Master projection in MongoDB by using the find method's second parameter to display only selected fields, such as name and founded year, while controlling the _id field.

Query the mongo shell to check null start or end station names with $type 10, then project company names and employee counts, sort by employees, and limit to ten companies.

Learn to query embedded documents in MongoDB using dot notation to access fields like address.zip, with find and find one commands and explanations of field paths.

Learn to query arrays and embedded documents in MongoDB by projecting fields in posts and grades collections, using $all and $size, and applying $match for embedded scores.

Practice querying nested fields and arrays in MongoDB by returning documents from the roots and companies collections using dot notation and $match, and project only the _id and products fields.

Insert documents into a collection using the shell or Atlas, implicitly creating the collection with db.collection.insert and JSON. MongoDB auto-generates _id by default, or specify one.

Learn to delete documents in MongoDB using delete one and delete many with a filter, including dot notation for embedded fields, shown with Tom Hanks and Academy Award examples.

Update documents in the shell using update one or update many with a filter, update parameter, and options; apply $set to modify or create fields across matches.

Explore MongoDB update operators, including $set, $unset, $inc, $rename, and $push, with practical examples of updating multiple fields and arrays.

Explore upsert in MongoDB updates, enabling insertion when no documents match the filter, and learn to use $set to add fields like age on non-existent records.

Explore the MongoDB aggregation framework and pipelines, using stages like $match and $group to filter by founding years 2005–2010, project fields, and group by category code.

Learn how dollar match filters documents in the aggregation pipeline using standard json syntax and query operators; it can appear multiple times and behaves like find, without projection.

Explore the dollar project stage in the aggregation pipeline to project fields, rename fields, reference embedded fields with dot notation, reassign values or create new fields, and control _id output.

Learn to build MongoDB aggregation queries: filter by ISO date with $match, project only start and end station coordinates, and extract Houston zip codes by population.

Explore arithmetic expression operators in MongoDB, including dollar add, subtract, divide, multiply, and round, with practical examples and notes on abs and log usage.

Explore string expression operators in MongoDB, including concat, toUpper, toLower, regex match with options, date from string, trim, and split, demonstrated with aggregation projections and practical examples.

Explore date expression operators in MongoDB, such as $dateAdd, $dateDiff, and $month, with syntax examples and a shell-based demo projecting month numbers from start time field.

Master comparison expression operators in Mongo by contrasting query language syntax with expression operator syntax and using $match and $project in aggregation.

Explore MongoDB's array expression operators, such as $isArray, $arrayElemAt, $first, $last, $size, and $map, to query, project, and transform array fields.

Explore conditional expression operators in MongoDB, including $cond, $ifNull, and $switch, with examples classifying companies by employee count and replacing nulls in the project stage.

Explore MongoDB aggregation basics across four examples: compute journey time in minutes with subtract and divide; filter with regex; count offices with size; classify population with switch.

Learn how the $addFields stage in the aggregation pipeline adds new computed fields, passes through existing fields, and can be used with $project for flexible field selection.

Explore MongoDB cursor stages including $sort, $count, $limit, and $skip, with practical examples sorting by trip duration, selecting top N results, counting documents, and filtering by duration.

Add a new field called city_underscore_state by concatenating city and state with a hyphen, using $addFields and $concat. Then sort the documents by city_underscore_state in ascending order.

Explore the dollar group aggregation stage, grouping documents by an underscore id expression and computing fields with accumulators like dollar sum and dollar average, including grouping by color.

Learn how to use the $bucket and $bucketAuto stages to group documents by defined or automatic boundaries, compute counts and averages, and handle defaults.

Explore the dollar facet stage, which runs multiple sub pipelines and outputs an array of documents per sub pipeline. Demonstrates bucket manual and bucket auto on trip duration with boundaries.

Use the dollar sort by count stage to group documents by an expression, return a count, and view results in descending order.

Apply MongoDB aggregation to identify the most common start station by count and the top five by average trip duration using group, sort, limit, and bucket stages.

Use the dollar out stage to save aggregation results into a collection in the database. Ensure the dollar out stage is last and overwrites existing collection with unique _id fields.

Consolidate aggregation pipeline knowledge by learning how to combine stages, use shell variables for query snippets, and build practical queries like total Houston population and homework score statistics in Atlas.

Learn how to use system generated variables like $$NOW and $$CLUSTER_TIME in MongoDB aggregation, project current date and timestamp, and reference the root document with $$ROOT in group operations.

Explore defining user defined variables in the MongoDB shell and in aggregations with $let and $map, including a 20% trip duration increase example.

Explore MongoDB thresholds and limitations, including the 16 MB document size and aggregation pipeline behavior, and learn how 100 MB RAM limits and allow disk use write temporary files.

Explore write concern and ordered inserts in MongoDB, detailing w options (1, 2, majority), j and time limits, and how ordered true stops on error while false skips duplicates.

Enforce consistent document structure in MongoDB by using schema validation with a JSON schema validator, defining required fields like name and age with a 0 to 110 range.

Install the sample_mflix dataset, focusing on movies, users, and comments and their one-to-many relationships. Use Mongo restore with the Uri to load the dump into Atlas.

Learn how dollar lookup performs a left outer join between two collections in a MongoDB aggregation, producing an embedded array and showing matching on name or email.

Use a dollar lookup to join the movies and comments collections, filtering for the movie title 'My Skinny Sister', matching _id fields, and returning movie details with a comments array.

Explore how indexes boost query performance by avoiding collection scans through b-tree indexes on fields such as last name. Create single or multi-field indexes with options including background and unique.

learn how explain method reveals query plans, scan types such as coal scan or ixcan, and execution stats, including the winning plan, fetch, shard merge, and modes like query planner.

Create a single field index on start station ID and on number of employees to compare query performance using explain, illustrating index scan and fetch in MongoDB.

Learn how MongoDB sorts with indexes, avoid in-memory sorts by creating a descending pop index, and read explain plans for index scans vs full scans.

Create compound indexes on multiple fields to boost query performance, noting that field order and direction matter. Only index prefixes support queries, and swapping order can break index usage.

Index arrays and embedded fields in MongoDB, including compound indexes and the cartesian product, with practical examples on scores and tags, and verify index usage with explain and execution stats.

Explore creating and using partial indexes in MongoDB by indexing only trip duration > 100 minutes, with a partial filter expression and explain plan validation.

Master hints in MongoDB to force a query to use a specific index, shown by creating indexes on trip duration and start station ID and applying the hint.

Explore MongoDB drivers and how they connect applications to a database, including officially supported libraries. Focus on Python with PyMongo and perform basic CRUD and aggregation operations.

Connect to a MongoDB Atlas cluster from a Jupyter notebook using PyMongo, create the connection string, and explore databases and collections such as sample_mflix.

Connect to a sample MongoDB using PyMongo, perform find_one and find with cursors, iterate results, print neatly, and implicitly create databases, collections, and basic inserts in Python.

Apply the MongoDB aggregation framework to group movie documents by year, count occurrences per year, and sort by count to reveal yearly trends, demonstrated on the sample_flicks.movies collection.