MongoDB is today's fastest-growing database platform and the leading "NoSQL" database in the world. While MongoDB has long been the established standard for web and mobile application development, it is also increasingly being used in broader contexts such as genomics, real-time analytics and IoT.
Analyzing data in MongoDB presents unique challenges because of MongoDB's non-SQL interfaces and non-relational data models. However, MongoDB includes native analytical capabilities, and there are connectors available to familiar analytic tools.
In this course, you'll learn how to interact with MongoDB, use the built-in MongoDB aggregation framework, and analyze MongoDB data with familiar analytics tools such as Python, Excel, Tableau, and Spark.
- About the instructor
- Scope of the course
- Prerequisites
- What is MongoDB
- Document model vs relational model
- Advantages of MongoDB
- JSON document structure
- Challenges for data analysts and data scientists
- Connecting using the shell
- Inserting and changing documents
- Find() commands
- Introduction to the Aggregation framework
- Using the Compass UI
- Connecting from languages and tools: Python, R
- Exercise1
- Manipulating documents: $match, $unwind, $project
- Grouping and sorting
- Joining collections
- Creating aggregations in Compass
- Exercise 2
- Graph operations
- Bucketing and facets
- Windowing functions – moving averages, time series support
- Configuring the BI connector
- Connecting to MongoDB Atlas BI connector
- Using the BI connector with analytic tools: Tableau, Excel
- Configuring the spark connector
- Using Spark SQL
- Using Spark
- Using the Spark
- Summary
- Further resources