Data Modeling
A key aspect of working with MongoDB is how you model your data. MongoDB is a schema-less database, but proper data modeling is crucial for performance and scalability. Here are the main topics to cover:
- Embedded vs. Referenced Data: In MongoDB, you have two options for representing relationships between documents: embedding data within documents or referencing other documents. You need to understand when to use each approach.
- Data Duplication: Unlike relational databases, MongoDB allows data duplication. You must learn when it's beneficial to duplicate data and when it's better to reference it.
- Normalization and Denormalization: Understand how to balance between normalization (storing data in multiple collections) and denormalization (embedding data into a single MongoDB Certified Developer document) based on application needs.
Aggregation Framework
MongoDB’s Aggregation Framework is a powerful tool for performing data transformations and calculations. This is a critical topic for the exam and includes the following:
- Pipeline Operations: Understand how to use the aggregation pipeline, which allows you to chain multiple stages to transform the data.
- Common Stages: Learn about the most commonly used stages such as
$match, $group, $sort, $project, $limit, and $skip.
- Operators: Get familiar with various aggregation operators like
$sum, $avg, $min, $max, $push, and $addToSet.
Indexing and Query Optimization
Indexes play a vital role in MongoDB’s performance. The MongoDB C0DEV Exam will test your ability to optimize queries using indexes. Focus on these key areas:
- Types of Indexes: Learn the different types of indexes in MongoDB, such as single field, compound, and geospatial indexes.
- Index Creation: Know how to create indexes for optimal query performance and how to use the
explain() method to analyze query performance.
- Index Usage: Understand when and how indexes are used during query execution and how to optimize queries for better performance.
- Indexing Strategies: Learn how to create indexes that balance read performance with write performance.
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