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Principal Engineer, Google Backend
·
Module 6
Large-Scale Data Processing Paradigms
1
MapReduce: Simplified Data Processing on Large Clusters
Analyze the MapReduce programming model and execution framework from the original paper.
2
MapReduce Fault Tolerance
Explain fault tolerance mechanisms in MapReduce, covering master, worker, and task failures.
3
MapReduce Scheduling: Locality and Speculation
Explain the task scheduling strategy in MapReduce, including data locality and speculative execution.
Understanding Dataflow/Beam: Unified Batch and Stream Processing
Describe the Dataflow/Beam model for unified batch and stream processing.
Windowing, Triggers, and Watermarks in Stateful Stream Processing
Explain the roles of windowing, triggers, and watermarks in stateful stream processing.
Batch vs. Streaming Architectures
Compare batch, micro-batch, and true streaming architectures and their trade-offs.
Data Architecture Selection & Justification
Select an appropriate data processing architecture for a given use case and justify the choice.
Designing Resilient Data Pipelines
Design the high-level architecture and fault tolerance strategy for a large-scale data pipeline.
Previous module
Advanced Distributed Transactions
Next module
Fault Tolerance & Reliability Engineering