Core Concepts
Unbundling Databases
- Databases combine many functions: storage, indexing, caching, replication
- Modern systems decompose these into separate services
- Trade-off: Flexibility vs Complexity
Data Integration
- Combining data from multiple sources
- Keeping derived data in sync
- Event-driven architectures
Designing Applications Around Dataflow
Event-Driven Architecture
- Events as first-class citizens
- Asynchronous processing
- Decoupled components
Dataflow Through Services
- REST and RPC for synchronous communication
- Message queues for asynchronous
- Event logs for durability
Derived Data
- Materialized views
- Search indexes
- Caches
- Analytics aggregates
Observability and Auditability
Monitoring
- Metrics, logs, traces
- Detect anomalies
- Understand system behavior
Auditability
- Track data provenance
- Compliance requirements
- Debugging and forensics
Data Lineage
- Where did data come from?
- How was it transformed?
- Who accessed it?
Trust, but Verify
Data Integrity
- Don't blindly trust what systems promise
- Verify data correctness
- End-to-end validation
Checking Data Integrity
- Checksums
- Hash trees
- Consistency checks
Auditable Data Systems
- Immutable logs
- Cryptographic verification
- Tamper detection
Ethics and Data Systems
Bias and Discrimination
- Data can encode bias
- Algorithms can amplify bias
- Need for fairness and accountability
Privacy
- Data protection regulations
- Encryption, access control
- Anonymization techniques
Responsibility
- Data engineers have ethical obligations
- Consider societal impact
- Transparency and accountability
The Future
Batch and Stream Convergence
- Same processing model for batch and stream
- Lambda and Kappa architectures
- Unified data processing frameworks
Declarative Data Systems
- Specify what you want, not how
- Systems automatically optimize
- SQL-like interfaces for everything
Data Mesh
- Domain-oriented data ownership
- Self-serve data infrastructure
- Federated computational governance
Key Takeaways
- Unbundling databases provides flexibility but adds complexity
- Event-driven architecture enables scalable, decoupled systems
- Observability is crucial for understanding data systems
- Verify data integrity - don't just trust
- Ethics matter - consider bias, privacy, responsibility
- Batch and stream processing are converging
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99 - Key Concepts Glossary