MongoDB 101: Beginner's Guide to MongoDB

Explore how MongoDB organizes data into databases, collections, and documents, clarifying the differences between them and JSON-like representations. Understand collections as schemaless groups of documents with nested and varying fields.

Compare json and bson to understand their use cases and performance trade-offs. Choose json for human readable data interchange, and bson for compact binary data in databases like MongoDB.

Learn to create databases and collections in MongoDB using the shell, using the use command and inserting a document to create a collection.

Learn to insert a single document into a MongoDB collection using insertOne within the university database and student collection, noting the schema-less design allows varying fields and data types.

Learn how to insert multiple documents at once in MongoDB using the insert many function, manage a collection, and view inserted data with find.

Update a MongoDB document with a single filter using the $set operator, ensuring case-sensitive field matching, and learn how upsert creates a new document when no match exists.

Learn to update a MongoDB document using multiple filters to distinguish documents with the same value. Use multi-criteria, like name and age, to update the correct record.

Learn how to read all documents in a MongoDB collection and retrieve specific records using filters, such as name and age, with the find function.

Learn how to delete documents from a MongoDB collection using the remove function, including conditional deletes by fields or id, and deleting all documents with verification via find.

Import a json file into MongoDB using the mongo shell with the json array flag, specifying database and collection, then verify by listing dbs and querying with find.

Import a CSV file into MongoDB using the MongoDB shell, configuring the import command with type csv, db, collection, file, and headerline flags, then verify data with a shell query.

Learn how the EQ operator filters MongoDB documents by field value, including nested fields and lists, with case-sensitive and order-sensitive matching.

Discover how the gt operator in MongoDB finds documents with quantities greater than a value using a filter in db.inventory.find, and learn about the greater than or equal to operator.

Discover how the $lt operator finds documents with a field value under a threshold, and how the lte operator handles under-or-equal values, with an inventory example filtering by quantity.

Learn how the in operator selects documents where a field value matches any value in a specified array, enabling a single query to fetch multiple quantities.

Explore the not equal operator ($ne) in MongoDB, which selects documents whose field value differs from a value and includes missing fields, with examples like quantity not equal to 20.

Learn how the not in operator in MongoDB filters documents whose field values are not in a specified list, with practical examples using quantity not in [15, 20].

Explore how the $or operator in MongoDB enables a logical or query over multiple expressions, fetching documents that match at least one condition, demonstrated with an inventory example.

Explore the MongoDB and operator, a logical tool that filters documents only when all conditions are met, such as quantity not equal to 20 and tags contain B.

Learn how to use the not operator in MongoDB to negate other operators and select documents that do not match an expression, and quantity not equal to 20.

Learn how the MongoDB $exists operator tests whether a field exists in a document, including nulls, and how to combine it with other conditions like quantity equals 20 or tags.

Discover how the type operator in MongoDB selects documents by field data type to reveal anomalies in unstructured data, with examples of string, int, and missing double types.

Explore how the $expr operator enables aggregation expressions inside MongoDB queries, using greater-than and less-than comparisons on a monthly budget collection to filter documents.

Discover how the $mod operator filters documents by modulus on a field, finding even (quantity mod 2 = 0) or odd (quantity mod 2 = 1) quantities in inventory.

Learn how the $currentDate update operator sets fields to the current date or timestamp for documents matching a condition in MongoDB, with practical examples.

Learn how the MongoDB $inc operator increments a field by a specified value, with practical examples like updating cart quantities using updateMany based on a condition.

Explore the decrement operator in MongoDB and update matrix.orders by a specified amount for SKU ABC 1 to 7, then compare with the increment operator.

This video explains how the min operator updates a field to a smaller value in MongoDB, comparing current and new values to keep the minimum.

Discover how the max operator updates a field to a larger value in MongoDB using BSON order, illustrated by increasing id four's high score to 400 with update many.

Apply the $mul update operator to multiply the low_score field by two for documents with id 3, using updateMany, and verify the updated value in the collection.

Use the MongoDB $rename operator to rename fields, including nested ones, to fix typos. Learn to apply update many and verify results with find.

Use the set operator in MongoDB to replace field values, including nested fields, with new values, and see how it updates quantity, details, and tags.

Learn how to use the $unset operator in MongoDB to remove a field from a document, or from all documents with an empty condition, including nested fields.

Learn how the MongoDB $pop operator removes the first or last element from an array, using minus one or plus one, with practical update and find examples.

Learn how the $pull operator removes array elements matching a condition across documents in MongoDB. See practical examples using updateMany and $in to remove 2 or 4 from scores.

Explore the MongoDB $push operator, which appends values to the end of an array, and use update many to add items to all documents or target a single id.

Learn how MongoDB data modeling structures data with collections and documents, compare relational and NoSQL schemas, and explore embedding versus referencing for one-to-one, one-to-many, and many-to-many relationships.

Learn how to model a one-to-one relation in MongoDB. Embed the contact document with email and fax inside each customer to avoid duplicates.

Demonstrate the one-to-many relationship in MongoDB by embedding payment methods inside each customer, showing how a customer can have multiple cards and different customers cannot share a card.

Learn how to model a one-to-many relationship in MongoDB using references, linking customers to orders with ids, updating customers to add orders, and querying with lookups and aggregation.

Explore many to many relationships in MongoDB by using references between orders and products, and learn how to implement joins with the lookup operator, while noting drawbacks of embedded documents.

Set up a shared cluster in MongoDB Atlas by creating a database, selecting the shared tier and region, creating a user, and allowing connections from 0.0.0.0. Connect via MongoDB Compass.

Connect your MongoDB Atlas cluster to MongoDB Compass using the URI and password, then create a new database pet db with a Pets collection and insert documents.

Explore the table view in MongoDB Compass and MongoDB Atlas by connecting clusters, viewing collections, and inserting documents; enable the data api and manage read, write, and network access.

Connect to a local or online server, then create a database and collection in MongoDB Compass. Use the Import Data feature to load files, including CSVs.

Query documents in MongoDB compass using filters to retrieve by fields and conditions. Sort by age in ascending order, project only fields, and use skip and limit to refine results.

Connect a Python application to MongoDB Atlas with PyMongo to perform CRUD operations (insert, read, update, delete) via a PyCharm workflow.

Learn to connect Spark with MongoDB using Databricks, import a csv into a dataframe, and load it into a MongoDB Atlas collection via the Spark MongoDB connector.