I. Lakehouse Architecture and Open Table Formats
Core Foundation: Transactional Table Formats
- Apache Iceberg: Launched by Netflix, ETL performance improved 10x
- Delta Lake: Released by Databricks, supports MERGE INTO syntax
- Apache Hudi: Used by Uber, supports CDC
Architecture Advantages
- Cost efficiency: 70% storage cost savings compared to traditional data warehouses
- Flexibility: Supports structured/semi-structured/unstructured data
- Real-time capability: Batch-stream unified processing with second-level latency
II. Data Mesh
Core Principles
- Domain-oriented data ownership: Divide data by business domain
- Data as a product: Ensure data quality and documentation completeness
- Self-serve data platform: Unified infrastructure platform
- Federated computational governance: Establish governance framework while maintaining autonomy
Applicable Scenarios
- Large enterprise groups with complex business lines
- Rapidly expanding technology companies
- Traditional enterprises undergoing digital transformation
III. Apache Beam
Core Design Philosophy
- Write once, run anywhere: Single codebase, multiple execution engines
- Supports both batch and stream processing
Key Features
- Event time processing
- Multiple window types
- Comprehensive IO connectors
IV. Serverless and Cloud-Native Big Data
Major Cloud Services
| Provider | Product | Features |
|---|---|---|
| BigQuery | Serverless data warehouse | |
| Amazon | EMR | Managed Hadoop/Spark |
| Alibaba Cloud | MaxCompute | PB-level data warehouse |
Technology Trends
- Spark on K8s
- Flink on K8s
- Serverless data analytics
V. Other Emerging Technologies
Federated Learning and Privacy Computing
- Healthcare: Sharing patient record features between hospitals
- Finance: Joint modeling between banks
- Recommendation systems: Protecting user privacy
Graph Data Processing
- GraphX, Gemini: Trillion-scale graph computing
- Neo4j, TigerGraph: Graph databases
- Applications: Social networks, financial fraud detection
Real-time Data Analytics
- ClickHouse: One million rows per second ingestion
- Apache Doris: Sub-second response
Summary
Technology evolution exhibits two major characteristics:
- Convergence trend: OLAP + stream processing, graph computing + machine learning
- Simplification trend: Serverless, automated operations