Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making — we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.
Learning Goals:
1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields.
2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models.
3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics
4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends.
5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages.
write programs in Spark
6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams