1.
Data Model
Chu-Cheng Hsieh

2.
L6:Insight
L5: Model
&Experiments
L4: Aggregate
L3: Discover & Explore
L2: Move, Transform, Store
L1: Collect & Monitoring

3.
L1: Collect &
Monitoring
Connect to data
source
Log & record
internal and
external events
Data quality
checks
Missing values
Abnormal values
(schema
evaluation)
Inconsistency
(versioning)

4.
L2: Move, Transform & Store
Controlling cost: data
Storage, computation (ETLs)
From Unstructured to
Structured data
Metadata Generation

5.
L3: Discover & Explore
DATA SEARCH &
DISCOVERY PLATFORM
VALIDATION VISUALIZATION
(DASHBOARD)

6.
L4: Aggregate Layer
FEATURE STORES
(OFFLINE & STREAMING)
METRICS AGGREGATION
FUNCTION (REPORTS)

7.
L5: Model &Experiments
MACHINE LEARNING
(FORECASTING, OUTLIER
DETECTION, ETC.)
A/B TESTS (EXPERIMENTS) AI APPLICATIONS
(PERSONALIZED
MARKETING, ETC.)

8.
L6: Insight (& Act)
EXPLAIN ANALYTICS ACTIONS BASED ON
INSIGHTS