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Foundations of Storage Hardware and Efficiency

Raw Ingredients of Data Storage

Magnetic Disk Drive

Solid-State Drive

Random Access Memory

Networking and CPU

Serialization

Compression

Caching

🎮 Crossword: Hardware and Efficiency Terminology

Storage Systems and Architectures

Data Storage Systems

Single Machine Versus Distributed Storage

Eventual Versus Strong Consistency

File Storage

Block Storage

Object Storage

The Hadoop Distributed File System

Data Engineering Abstractions

Data Engineering Storage Abstractions

The Data Warehouse

The Data Lake

The Data Lakehouse

Stream-to-Batch Storage Architecture

🎮 Pixel Quest: Storage Abstractions Journey

Optimization, Metadata, and Trends

Indexes, Partitioning, and Clustering

Data Catalog

Schema

Separation of Compute from Storage

Zero-copy cloning

Data Storage Lifecycle and Data Retention

Single-Tenant Versus Multitenant Storage

Knowledge Check: Storage Systems

Quiz: Hardware Fundamentals

Quiz: Serialization and Compression

Quiz: Caching Strategies

Quiz: Distributed Storage Concepts

Quiz: Consistency Models

Quiz: File and Block Storage

Quiz: Object Storage Characteristics

Quiz: Data Warehousing

Quiz: Data Lakes

Quiz: Lakehouse Architecture

Quiz: Partitioning and Indexing

Quiz: Metadata and Catalogs

Quiz: Compute Separation

Quiz: Lifecycle Management

Quiz: Final Course Assessment

Course Feedback and Survey

Survey: Course Pacing

Survey: Content Clarity

Survey: Relevance to Role

Survey: Hardware Module Feedback

Survey: Storage Systems Feedback

Survey: Abstractions Feedback

Survey: Trends and Optimization Feedback

Survey: Game and Interaction

Survey: Technical Depth

Survey: Overall Satisfaction

Modern Data Storage Systems: Architecture, Performance, and Abstractions is a hands-on, system-focused course for data engineers, architects, and performance-minded developers who need to design, scale, and optimize storage for analytics and real-time workloads. You will move from hardware fundamentals—HDDs, SSDs, RAM, networking, and CPU—through serialization, compression, and caching, into modern storage architectures spanning file, block, and object storage. Then, you’ll connect these building blocks to data engineering abstractions such as data warehouses, data lakes, and the emerging lakehouse pattern, including stream-to-batch designs. Along the way, you’ll learn how consistency models, indexing, partitioning, clustering, metadata, schemas, and the separation of compute from storage shape performance, cost, and reliability at scale. Practical frameworks, comparison heuristics, and real-world trade-offs help you choose the right storage for your use case, while quizzes and mini-games reinforce key concepts. By the end, you’ll be able to evaluate hardware and software constraints, select the appropriate storage model, and implement strategies that reduce latency, control costs, and improve data quality and governance across your platform.

5. Modern Data Storage Systems: Architecture, Performance, and Abstractions

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            <p>The landscape of data engineering is fundamentally rooted in the physical and logical constraints of storage hardware. This course provides a rigorous examination of the foundational components of data storage, ranging from the mechanical latency of magnetic disk drives to the high-throughput capabilities of solid-state drives and volatile memory. By analyzing the interplay between CPU performance, network bandwidth, and storage I/O, software engineers and IT professionals will gain a comprehensive understanding of how hardware capabilities dictate overall system efficiency. The curriculum progresses into the complexities of distributed storage, contrasting single-machine architectures with horizontally scalable systems while navigating the critical trade-offs between strong and eventual consistency as defined by the CAP theorem.</p>

<p>Beyond hardware fundamentals, the course explores the sophisticated abstractions that define modern data pipelines, including data warehouses, data lakes, and the emerging lakehouse architecture. Participants will evaluate various storage models—file, block, and object—and their specific applications in big data environments such as the Hadoop Distributed File System (HDFS). For data analysts and engineers seeking to transition into architectural roles, the focus extends to operational optimizations such as partitioning, indexing, and the strategic separation of compute from storage. By mastering these architectural patterns, serialization formats, and metadata management through data catalogs, professionals will be equipped to design resilient, cost-effective, and high-performance storage solutions that meet the demands of contemporary enterprise data ecosystems.</p>
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