Scalable Data Processing: Systems and Algorithms

As datasets grow exponentially, traditional single-machine analysis tools quickly reach their limits. To unlock insights from massive, complex data, you must understand the distributed systems and scalable algorithms that power modern data platforms. This course provides a clear, text-based introduction to the world of large-scale data manipulation. You will transition from writing basic data scripts to understanding how distributed databases, parallel processing engines, and modern query languages handle gigabytes and terabytes of data. You will gain the conceptual framework needed to choose and apply the right scalable architectures for real-world analytical challenges. What you'll learn: - Understand the core principles of distributed systems, parallel databases, and scalability. - Apply foundational data manipulation algorithms for sorting, filtering, and joining large datasets. - Compare traditional relational databases with modern NoSQL and key-value storage systems. - Explore modern high-performance data tools, including columnar formats and modern dataframe libraries. - Analyze the MapReduce programming model and its evolution into modern distributed compute engines. - Practice optimizing data pipelines for efficiency, fault tolerance, and cost-effective processing. You will start by exploring foundational definitions of scale, storage, and parallel computing before diving into the algorithms and systems that distribute workloads across clusters. Through clear written explanations and practical code examples, you will learn how to design robust pipelines that process data efficiently at scale. This course is designed for beginner data analysts, aspiring data engineers, and software developers who want to scale their data skills. No prior experience with distributed systems or high-performance computing is required. Start reading today to build a strong foundation in scalable data systems and algorithms.