Big Data
Schema Design for Time-Series Data
IoT and log data grow exponentially. This lesson teaches bucketizing — storing multiple events in one document to reduce index size.
Level: AdvancedBig Data
What You'll Learn
Time-series data growth patterns and challenges
Document bucketizing to reduce index overhead
Hourly and daily bucket strategies
TTL indexes for automatic data expiry
Aggregation pipelines for time-series queries
Fitness tracker heart rate data model
Comparing MongoDB, TimescaleDB, and InfluxDB
What's Included
🧭AI Tutor🔒
Interactive learning path with AI-generated lessons read aloud by your personal tutor
🧪Practice Lab🔒
Step-by-step lab instructions with AI guidance — run in your own IDE or sandbox.
🎙️Mock Interviews🔒
AI interviewer asks tailored questions, gives real-time feedback and a score report
📝Evaluation🔒
MCQ questions (theory, practical, code) to test your knowledge and earn a badge
⚔️Challenge AI🔒
Compete head-to-head against AI on real production problems — judged on correctness, performance, scalability, code quality, and edge cases
Course Syllabus
Module 1: Time-Series Data Challenges
- •Why naive one-document-per-event schemas fail
- •Index bloat and write amplification at scale
- •Read patterns for time-series: range, aggregate, latest
- •Overview of time-series databases (InfluxDB, TimescaleDB)
- •When to use MongoDB for time-series
Module 2: The Bucket Pattern
- •Bucketizing: grouping events by time window
- •Hourly vs daily bucket trade-offs
- •Pre-aggregating min / max / avg inside a bucket
- •TTL indexes for automated data expiry
- •Partial updates with $push and $inc operators
Module 3: Fitness Tracker Data Model
- •Storing 24 hours of heart rate in one document
- •Efficient range queries across buckets
- •Aggregation pipeline for daily summary stats
- •Handling late-arriving events
- •Capstone: heart rate data model with 1M simulated events
You're all set
Everything in this course is unlocked.
Continue Learning →✓AI Tutor
✓Practice Lab
✓Mock Interviews
✓Evaluation
✓Challenge AI