# 📚 Table of Contents
- **Preface:** Data Troubles — *Data’s Acting Up Again*
- **I. Data Architecture:** Evolution of Thought & Trade-offs
- **II. Data Storage:** Architectural Analysis
- **III. Data Warehouse Design:** Service Applications
- **IV. Metrics Definition:** Theory & Business Thinking
- **V. Data Quality & Efficiency Improvements
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Data architecture design is **not** just a collection of tools and frameworks — it’s an *art of trade-offs*, balancing:
- **Scale**
- **Real‑time capabilities**
- **Cost**
- **Complexity**
- **Governance**
In this article, we’ll move past oversimplifications to uncover **professional data concepts**, **technical principles**, and their **practical trade-offs**.
We’ll also share insights on **metrics design**, **anomaly monitoring**, and **data efficiency** — complete with actionable tips.
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## Preface: Data Troubles — *Data’s Acting Up Again*
It’s another morning dashboard review...
Boss: “This number looks different from yesterday’s report?”
Me: "*Uh... I’ll check.*" ❄️
Yep. The metrics are misbehaving again.
The more you work in data:
- The more complex the SQL becomes
- The more blame you inherit
- The more hair you lose...
Wish the data would just **behave itself for once**!
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We’ll approach this topic from **five angles**:
1. **Architecture**
2. **Storage**
3. **Data Warehouse**
4. **Metric Design**
5. **Quality & Efficiency Enhancements**
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## I. Data Architecture — Evolution & Trade-Offs
### Mainstream Architectures & Use Cases
Architectures evolve alongside **business needs** and **technical capabilities**.
No single model wins — each is optimal for certain situations.
**Key Driver:** Unified batch–stream processing with balanced **cost vs performance**.
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#### **Massively Parallel Processing (MPP)**
- **Concept:** Divide tasks into subtasks, process them in parallel, merge the results
- **Pros:** Linear scalability, low-latency analytics, great for *data warehouses* & *BI*
- **Structure:** Multiple independent nodes connected via interconnect network
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In **distributed real-time workflows**, you must weigh MPP against:
- Storage–compute separated systems
- Cloud-native lakehouses
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#### **Lambda Architecture** (Batch + Stream Separation)
**Layers:**
- **Batch Layer** (Spark)
- **Speed Layer** (Flink/Kafka Streams)
- **Serving Layer** (HBase/Druid)
**Pros:** Maturity, proven in production
**Cons:** High complexity, dual logic maintenance, resource heavy
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#### **Kappa Architecture** (Unified Stream Processing)
- **All data** handled as streams
- Batch = bounded historical streams
- Core: Centralized log (Kafka), single stream engine (Flink)
- **Pros:** Simple, unified code
- **Cons:** Demands long-term queue storage & strong replay
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#### **Lakehouse Architecture** (Data Lake + Warehouse)
- Combines **flexibility of lakes** with **performance of warehouses**
- Uses formats like **Iceberg**, **Delta Lake**, **Hudi**
- Features: **ACID**, **Time Travel**, **Schema Evolution**
- Best choice depends on needs for **consistency**, **latency**, **op cost**, **historical scale**
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### Data Processing Semantics
Stream delivery guarantees:
- **At-most-once**: No repeats, but possible loss
- **At-least-once**: No loss, possible repeats
- **Exactly-once**: Perfect fidelity
**Exactly-once** relies on:
- **State Checkpointing** (e.g., Flink barriers + snapshots)
Complementary:
- **Idempotent Writes**
- **Two-phase commits (2PC)**
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### Data Quality: Observability Engineering
Quality = **Usable + Reliable**
Naming conventions matter:
| Entity | Field Examples |
|------------------|----------------|
| Date | `fdate`, `ftime` |
| User Account | `uin`, `uid` |
| Device ID | `uuid`, `oaid` |
| Table Prefix | `ods_`, `dwd_`, `dws_`, `ads_` |
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## II. Data Storage — Architectural Analysis
### 1. Relational Databases (RDBMS)
**ACID Implementations:**
- **Atomicity:** Write-Ahead Logging (WAL)
- **Isolation:** Locks (2PL), MVCC
- **Durability:** WAL + fsync
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**Architecture Evolutions:**
- **Master–Slave Replication**
- **Sharding**
- **NewSQL:** Spanner, TiDB, CockroachDB
- Distributed transactions
- Elastic scaling
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### 2. NoSQL & CAP Trade-offs
Modes:
- **Key–Value**: Redis, DynamoDB
- **Document**: MongoDB, Couchbase
- **Wide-Column**: HBase, Cassandra
- **Graph**: Neo4j, Nebula Graph
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### 3. Big Data Storage & Lakehouses
- **HDFS:** Sequential big file reads/writes, high throughput
- **Table Formats:** Iceberg/Hudi/Delta Lake
- ACID
- Time Travel
- Schema Evolution
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### 4. Storage Engines
**B-Tree vs LSM-Tree**
- B-Tree: Great reads, poor mass writes
- LSM-Tree: Great writes, compaction overhead
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### 5. Distributed Consistency Protocols
Replication Modes:
- Async
- Semi-sync
- Full-sync
Consensus:
- Paxos, Raft, ZAB
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## III. Data Warehouse Design — Serving Apps
### 1. Healthy Metric Layering
**Evaluation metrics:**
- Completeness
- Reusability
- Standardization
- Resourcefulness
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### 2. Storage Design Optimizations
**Example:** TDW Hive partitioning
- Use secondary keys (e.g., media) to reduce scan size
- Pre-create partitions
- Restrict queries to both primary & secondary partitions
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## IV. Metrics Definition — Theory & Business Thinking
### 1. Metric Components
- Name & Definition
- Unit
- Formula
- Dimensions
- Value
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### 2. Atomic vs Derived Metrics
- **Atomic:** Undivisible, raw measures
- **Derived:** Built from atomic + time period + modifiers
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### 3. Design Models
- **OSM:** Objectives → Strategies → Measurements
- **UJM:** Map full user journey stages to metrics
- **AARRR:** Acquisition, Activation, Retention, Revenue, Referral
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### 4. Metric Hierarchies
Levels:
- **North Star**
- **Primary**
- **Secondary**
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## V. Data Quality & Efficiency
### Impossibility Triangle
Balance between:
- Quality
- Efficiency
- Cost
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### Three Lines of Defense (Monitoring)
Layers:
1. Source & ingestion
2. Processing
3. Application
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### Alert Levels
| Level | Impact | Response |
|-------|--------|----------|
| P0 | Critical outage | Immediate |
| P1 | Key function loss | 1hr |
| P2 | Potential issue | Same day |
| P3 | Informational | — |
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### Attribution Analysis
Methods:
- First-touch / Last-touch
- Linear
- Time Decay
- Shapley Value
- ML-driven
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**Workflow:**
1. Validate data
2. Drill-down by dimensions
3. Identify contributing factors
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### Final Note
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