Data Engineering Portfolio Projects: Building Your Showcase

+---------------------------------------------------------------------------+
|              PORTFOLIO PROJECT STRUCTURE                                  |
+---------------------------------------------------------------------------+
|                                                                           |
|  PROJECT 1          PROJECT 2          PROJECT 3          PROJECT 4       |
|  Batch ETL          Streaming          Lakehouse          MLOps           |
|  +-----------+     +-----------+     +-----------+     +-----------+      |
|  | Airflow   |     | Kafka +   |     | Databricks|     | Feature   |      |
|  | + dbt     |     | Flink     |     | + Delta   |     | Store +   |      |
|  | + Snowflake|    | + Redis   |     | + Unity   |     | MLflow    |      |
|  +-----------+     +-----------+     +-----------+     +-----------+      |
|                                                                           |
|  PROJECT 5                                                                |
|  Data Platform                                                            |
|  +-----------+                                                            |
|  | Terraform |                                                            |
|  | + dbt     |                                                            |
|  | + Airflow |                                                            |
|  | + CI/CD   |                                                            |
|  +-----------+                                                            |
+---------------------------------------------------------------------------+

PROJECT: E-Commerce Analytics Pipeline
======================================

OBJECTIVE:
Build an end-to-end batch ETL pipeline that ingests e-commerce data,
transforms it into a star schema, and serves analytics dashboards.

TECHNOLOGY STACK:
- Orchestration: Apache Airflow
- Transformation: dbt
- Warehouse: Snowflake
- Source: Shopify API + Stripe API
- Testing: dbt tests + Great Expectations
- CI/CD: GitHub Actions

DATA SOURCES:
1. Shopify Orders API (10K orders/day)
2. Stripe Payments API (10K transactions/day)
3. Segment Events API (1M events/day)

DELIVERABLES:
- [ ] Airflow DAGs for extraction (daily schedule)
- [ ] dbt models: staging -> intermediate -> marts
- [ ] Star schema: fact_orders, dim_customers, dim_products, dim_date
- [ ] SCD Type 2 for dim_customers
- [ ] Data quality tests (100% coverage)
- [ ] dbt documentation site
- [ ] GitHub Actions CI/CD pipeline
- [ ] README with architecture diagram and setup instructions

SCHEMA DESIGN:
fact_orders:
  - order_key (surrogate)
  - order_id (natural)
  - customer_key (FK)
  - product_key (FK)
  - date_key (FK)
  - quantity, unit_price, net_amount
  - order_status, created_at

dim_customers (SCD Type 2):
  - customer_key (surrogate)
  - customer_id (natural)
  - full_name, email, segment
  - valid_from, valid_to, is_current

SUCCESS METRICS:
- Pipeline SLA: < 2 hours end-to-end
- Data quality: 100% test pass rate
- Documentation: 100% models documented
- Cost: < $100/month (Snowflake)

PROJECT: Real-Time Fraud Detection Pipeline
============================================

OBJECTIVE:
Build a real-time streaming pipeline that processes payment events,
applies fraud detection rules, and alerts on suspicious transactions.

TECHNOLOGY STACK:
- Ingestion: Apache Kafka
- Processing: Apache Flink
- Storage: Redis (online) + Delta Lake (offline)
- Serving: FastAPI
- Monitoring: Prometheus + Grafana

DATA FLOW:
Payment Events -> Kafka -> Flink (windowed aggregation)
  -> Rule Engine (fraud detection)
  -> Redis (real-time alerts) + Delta Lake (historical)

FRAUD DETECTION RULES:
1. Velocity: >5 transactions in 1 minute from same card
2. Amount: Transaction > 3x average transaction amount
3. Geography: Transaction from new country in last 24 hours
4. Time: Transaction at unusual hour (2-5 AM local time)

DELIVERABLES:
- [ ] Kafka producer/consumer setup
- [ ] Flink streaming job with windowed aggregations
- [ ] Redis cache for real-time feature lookup
- [ ] FastAPI endpoint for real-time predictions
- [ ] Delta Lake for historical analysis
- [ ] Grafana dashboard for monitoring
- [ ] Load testing script (1000 TPS)

SUCCESS METRICS:
- End-to-end latency: < 500ms
- Throughput: 1000+ transactions/second
- False positive rate: < 5%
- System uptime: 99.9%

PROJECT: Unified Lakehouse Analytics Platform
=============================================

OBJECTIVE:
Build a data lakehouse that unifies batch and streaming data with
ACID transactions, time travel, and governed access.

TECHNOLOGY STACK:
- Platform: Databricks
- Table Format: Delta Lake
- Governance: Unity Catalog
- Processing: Spark Structured Streaming
- BI: Databricks SQL

ARCHITECTURE:
Medallion Architecture:
- Bronze: Raw ingestion (JSON, CSV, Parquet)
- Silver: Cleaned, deduplicated, validated
- Gold: Business-ready aggregations

DELIVERABLES:
- [ ] Bronze layer ingestion (Auto Loader)
- [ ] Silver layer transformations (Spark)
- [ ] Gold layer aggregations (Delta tables)
- [ ] Unity Catalog setup with access controls
- [ ] Time travel queries and data recovery
- [ ] Schema evolution handling
- [ ] Delta Live Tables pipeline

SUCCESS METRICS:
- Data freshness: < 15 minutes (streaming)
- Query performance: < 10 seconds (Gold tables)
- Data quality: 99.5%+ pass rate
- Cost: < $500/month (Databricks)

PROJECT: ML Feature Store and Model Serving
============================================

OBJECTIVE:
Build an MLOps infrastructure that manages features, tracks experiments,
and serves models in production with monitoring.

TECHNOLOGY STACK:
- Feature Store: Feast
- Experiment Tracking: MLflow
- Model Serving: FastAPI + Docker
- Monitoring: Evidently AI
- Orchestration: Airflow

DATA FLOW:
Raw Data -> Feature Engineering -> Feature Store -> Model Training
  -> Model Registry -> Model Serving -> Monitoring

DELIVERABLES:
- [ ] Feast feature store with offline/online stores
- [ ] MLflow experiment tracking setup
- [ ] Model training pipeline (Airflow)
- [ ] FastAPI model serving endpoint
- [ ] Evidently monitoring dashboard
- [ ] A/B testing framework
- [ ] Model retraining trigger

SUCCESS METRICS:
- Feature freshness: < 1 hour
- Model serving latency: < 100ms
- Model accuracy: > 0.85 F1
- Monitoring coverage: 100% of production models

PROJECT: End-to-End Data Platform with Infrastructure as Code
=============================================================

OBJECTIVE:
Build a complete data platform provisioned entirely with IaC,
including CI/CD, monitoring, and governance.

TECHNOLOGY STACK:
- IaC: Terraform
- Warehouse: Snowflake
- Orchestration: Airflow
- Transformation: dbt
- CI/CD: GitHub Actions
- Monitoring: Datadog

DELIVERABLES:
- [ ] Terraform modules for all infrastructure
- [ ] Snowflake (databases, schemas, warehouses, roles)
- [ ] S3 data lake with lifecycle policies
- [ ] Airflow on ECS/EKS
- [ ] dbt Cloud integration
- [ ] GitHub Actions CI/CD
- [ ] Datadog monitoring dashboards
- [ ] Cost allocation tagging

SUCCESS METRICS:
- New environment provisioning: < 30 minutes
- Infrastructure consistency: 100% (no drift)
- CI/CD pipeline: < 10 minutes
- Cost visibility: 100% resources tagged

portfolio-project/
+-- README.md                    # Project overview, architecture, setup
+-- architecture/
|   +-- diagram.png              # Architecture diagram
|   +-- data-flow.md             # Data flow description
+-- src/
|   +-- ingestion/               # Data extraction code
|   +-- transformation/          # dbt models or Spark jobs
|   +-- loading/                 # Load to warehouse
|   +-- serving/                 # API endpoints
+-- infrastructure/
|   +-- terraform/               # IaC configurations
|   +-- docker/                  # Containerization
+-- tests/
|   +-- unit/                    # Unit tests
|   +-- integration/             # Integration tests
|   +-- data_quality/            # Data quality checks
+-- docs/
|   +-- setup.md                 # Detailed setup guide
|   +-- decisions.md             # Architecture Decision Records
|   +-- performance.md           # Performance benchmarks
+-- .github/
|   +-- workflows/               # CI/CD pipelines
+-- notebooks/                   # Exploration and analysis
+-- scripts/                     # Utility scripts