Streaming Data Processing

5.

Reliability and Fault Tolerance

5.1.

Processing Guarantees

5.1.1.

At-Most-Once Processing

5.1.1.1.

Fire-and-forget Semantics

5.1.1.2.

Performance Benefits

5.1.1.3.

Data Loss Risks

5.1.2.

At-Least-Once Processing

5.1.2.1.

Retry Mechanisms

5.1.2.2.

Duplicate Handling

5.1.2.3.

Idempotency Requirements

5.1.3.

Exactly-Once Processing

5.1.3.1.

Transactional Processing

5.1.3.2.

Deduplication Strategies

5.1.3.3.

End-to-end Guarantees

5.1.4.

Guarantee Trade-offs

5.1.4.1.

Performance vs Reliability

5.1.4.2.

Complexity Considerations

5.1.4.3.

Use Case Alignment

5.2.

Fault Tolerance Mechanisms

5.2.1.

Checkpointing Strategies

5.2.1.1.

Checkpoint Triggers

5.2.1.2.

Checkpoint Storage

5.2.1.3.

Checkpoint Optimization

5.2.2.

Data Source Replayability

5.2.2.1.

Message Retention

5.2.2.2.

Offset Management

5.2.2.3.

Replay Strategies

5.2.3.

Sink Idempotency

5.2.3.1.

Idempotent Operations

5.2.3.2.

Duplicate Detection

5.2.3.3.

Transaction Management

5.2.4.

Distributed Consensus

5.2.4.1.

Leader Election

5.2.4.2.

Consensus Algorithms

5.2.4.3.

Coordination Services

5.2.5.

Recovery Procedures

5.2.5.1.

Failure Detection

5.2.5.2.

5.2.5.3.

Processing Resumption

5.2.6.

Duplicate Event Handling

5.2.6.1.

Deduplication Windows

5.2.6.2.

Unique Identifiers

5.2.6.3.

Stateful Deduplication

5.3.

Consistency Models

5.3.1.

5.3.1.1.

Partial Ordering

5.3.1.2.

5.3.1.3.

Causal Ordering

5.3.2.

Transactional Processing

5.3.2.1.

ACID Properties

5.3.2.2.

Distributed Transactions

5.3.2.3.

5.3.3.

Eventual Consistency

5.3.3.1.

Consistency Guarantees

5.3.3.2.

Conflict Resolution

5.3.3.3.

Convergence Properties

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6. Advanced Stream Processing