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Use Cases

Updated Oct 07, 2020 ·

Streaming Music​

Streaming systems often focus on how users interact rather than the actual music content.

  • Focus on user actions instead of song data
  • Track behavior, preferences, and interactions
  • Analyze what information users provide versus what is streamed

1. Interactions​

User interactions tell us how people use the app. Analytics teams often focus on what, when, and where actions happen.

  • Actions like liking, skipping, or changing songs
  • Selecting or removing channels and playlists
  • Tracking when and where in the app each action occurs

2. How To Store Data​

Collected data needs to be stored efficiently. A log-based format works well for recording user actions.

  • Logs are easy to manage and scalable
  • Different users have different activity levels
  • Data can be analyzed later for trends

Example:

{'user_id': 10, 'action': 'skip_song', 'timestamp': '2025-10-27T14:03:00'}

Logs make it easier to analyze user actions later without interrupting the live stream.

3. Analytics​

Once data is stored, analytics can extract insights such as preferences and usage patterns.

  • Identify favorite artists and genres
  • Discover peak usage times
  • Analyze devices, platforms, and app versions

Sensor Data​

Sensor data comes from devices that automatically monitor the environment. These devices send readings to central systems for analysis.

  • Common sensors include temperature, light, and motion detectors
  • Devices send data continuously or at set intervals
  • Systems can manage data from a few to millions of sensors

Consider a connected doorbell. It combines several sensors and features for home monitoring.

  • Detects button presses and motion
  • Streams audio and video for live interaction
  • Uses temperature and light sensors for added context
  • Monitor visitors and receive alerts remotely.

1. What Are We Monitoring​

Monitoring focuses on what actions or events are most important.

  • Send instant alerts when the button is pressed
  • Stream movement or sound events for quick detection
  • Process audio or video for deeper analysis

Each type of data can have different speed or priority requirements, depending on importance.

2. Data Handling​

Data from devices must be stored and processed based on urgency and purpose.

  • Button press events need fast storage and processing
  • Sensor readings can be processed with slightly less urgency
  • Audio and video files are stored for later review

Example:

data = [
  {"event": "button_press", "priority": "high"},
  {"event": "motion_detected", "priority": "medium"},
  {"event": "video_upload", "priority": "low"}
]

Each part of the product can have its own SLA. This ensures the most important data is handled first.

Vaccination Clinic​

A vaccination clinic is a good example of a system with multiple moving parts.

  • Patients arrive, register, and receive vaccines
  • Different stations handle different steps
  • Each step can represent a separate data process

Each area of the clinic can map to a different data process.

  • Arrival: Check temperature and symptoms (single entry)
  • Registration: Validate patient data (multiple workers)
  • Vaccination: Administer shots (parallel stations)
  • Monitoring: Watch for reactions (timed waiting)
  • Departure: Log patient exit

Example mapping:

arrival: batch
registration: queue
vaccination: stream
monitoring: batch
departure: stream