SQL to NoSQL Database & Handson with MongoDB

Explore the four categories of NoSQL databases—key-value, column-family, document, and graph—and compare their features, scalability, and common use cases.

Explore NoSQL types—graph, document, and key-value databases—highlighting use cases like recommendations, flexible schemas in MongoDB, and limits in concurrency and standard languages.

Understand why organizations move from rdbms to non-sequel databases, and when not to use rdbms, focusing on scalability, flexible schemas, cloud deployment, and cost advantages for 24/7 availability.

Showcases NoSQL adoption patterns: Facebook with Cassandra, Expedia with MongoDB, Netflix with DynamoDB, and Wal-Mart with a graph database for recommendations.

Identify cases where NoSQL is not suitable and RDBMS is preferable, such as applications needing sql, complex transactions, and traditional non-real-time data warehousing; note NoSQL limitations.

Explore MongoDB as a schema-less document store, map tables to collections and rows to documents, install MongoDB, and learn to create and drop databases, CRUD operations, and the aggregation pipeline.

Install MongoDB on Windows, macOS, or Linux by downloading from the official site, configuring environment variables, creating a data directory, and starting the server on port 27017.

Learn how to create and switch databases in MongoDB using use, see how databases appear only when they contain data, and create collections to store documents.

Explore MongoDB basics by creating, dropping, and listing collections, and compare the empty-then-insert method with automatic collection creation during insert, with hands-on practice.

Learn how to create a database, create collections and documents, use the primary key underskirt ID, and perform create and delete CRUD operations in MongoDB.

Explore how MongoDB stores data as documents in a collection, where each document is a key-value based, JSON-like object with flexible schema, allowing different fields across documents.

Demonstrates MongoDB insert operations on a collection, including insert, insertOne, and insertMany, with automatic collection creation, flexible schema, nested documents, and error handling via try-catch.

Explore demo of find queries in MongoDB: query a Paradox collection, use find, find_one, range filtering, sorting, limiting, and field projections to display readable documents.

Demonstrates update one, upsert, and update many operations in a MongoDB collection, showing conditional insert or modify of documents based on quantity and other fields.

This demo shows updating documents in the cortex collection using replaceOne and update, highlighting that replacing with partial data can remove fields and set quantity to 500.

Explore how MongoDB uses indexes to speed up queries, including the default _id unique index, single field and compound indexes, partial, text, and hash indexes, plus covered versus uncovered queries.

Explore how to create and evaluate indexes in MongoDB, including single-field, composite, embedded field, text, and geospatial indexes, using live queries and explain plans.

Analyze query performance by examining explain plans, using hints to force index use, and profiling to optimize data retrieval in SQL to NoSQL databases, with hands-on practice in MongoDB.

Explain plan demo shows inspecting a MongoDB query with explain to reveal the winning plan, index usage, and timing; create a products collection and compare indexed versus non-indexed queries.

Explore the database profiler, set profiling levels (0, 1, 2), log operations, and query system tables to monitor activity and diagnose performance issues for proactive optimization.

Discover the aggregate framework in MongoDB, including aggregation pipeline, map-reduce, and single-purpose operations such as count, sum, average, min, max, push, addToSet, first, last, distinct, group, unwind, sort, and limit.

Apply project, match, and group stages on the products collection to filter, project fields, and count groups through a multi-stage pipeline.

Demonstrates the aggregate framework on MongoDB by filtering with match, sorting by quantity, applying limit, skip, and unwind, and showing max and location data.

Explore the aggregate framework on a sales collection to compute average, min, max quantities and item price, then group by year to list distinct items sold.

Learn how sharding distributes data across multiple machines to speed queries and storage, using replica sets, mongos routers, and a config server, with hash-based and range-based strategies.

Explore the differences between RDBMS and NoSQL databases, with hands-on guidance on MongoDB, including CRUD, aggregation, indexing, and sharding.