MongoDB

Part $1$: Preparing the Large Dataset

Task $1$: Dataset Acquisition and Import

Obtain a large e$-$commerce dataset. This can be a publicly available dataset or one generated using tools like Mockaroo. Ensure the dataset includes at least $50,000$ products, $20,000$ customers, and $100,000$ orders.

Import this dataset into your MongoDB database, creating new collections: bigProducts, bigCustomers, and bigOrders.

Deliverable: Screenshots of the import process.

Part $2$: Indexing and Query Performance

Task $2$: Implementing Indexes

Analyze your dataset and identify fields that are frequently queried or involved in join operations.

Create appropriate indexes on these fields in each of the new collections to optimize query performance.

Deliverable: Submit the list of indexes created

Part $3$: Data Analysis

Task $3$: Aggregation Queries

$3\mathrm{.}1$ Aggregation Queries: Find the top $5$ best$-$selling products, calculating the average order value, and determining the most active customers by order count.

$3\mathrm{.}2$ Product Affinity Analysis: Determine which products are frequently bought together by analyzing order data.

Deliverables: Submit the MongoDB queries used for each analysis.

Part $4$: Reflection and Optimization

Task $4$: Query Performance Review

Choose one of your complex aggregation queries. Run it before and after applying indexes, then document the difference in performance.

Reflect on how indexing impacts the performance of big data queries in MongoDB.

Deliverable: A short report on your query performance review.