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Elite Computer Science Mastery
·
Module 10
Scalable Data Processing and Platforms
1
MapReduce for Batch Processing
Explain the MapReduce programming model and its application in batch processing.
2
Stream Processing Fundamentals: Time, Windows, and Watermarks
Explain core stream processing concepts: event time vs. processing time, windowing, and watermarks.
3
LSM Trees: Architecture & Read/Write Trade-offs
Describe the architecture of a log-structured merge-tree (LSM Tree) and its read/write path trade-offs.
4
Columnar Storage for Analytical Queries
Explain the benefits of columnar storage formats (e.g., Parquet) for analytical queries.
5
Data Lakes vs. Data Warehouses: Architecture & Separation of Concerns
Contrast the architectures of a data lake and a data warehouse, including the separation of storage and compute.
6
ETL/ELT Pipeline Design: Batch, Streaming, and Hybrid Approaches
Design a high-level ETL/ELT pipeline, choosing between batch, streaming, and hybrid models.
7
Schema Evolution Strategies for Data Pipelines
Manage schema evolution in data pipelines using strategies like schema-on-read and schema registries.
8
Building Data Quality Gates & Anomaly Detection
Implement data quality gates and anomaly detection within a data pipeline.
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Advanced Architectural Patterns and Application Security
Next module
Machine Learning Systems and MLOps