Applying MapReduce to Common Data Problems
Knowing how to program MapReduce is only half the battle. In this course, you'll learn how to set up the correct MapReduce based on what you want to accomplish.
What you'll learn
This course, Applying MapReduce to Common Data Problems, helps you with three unique MapReduce patterns: summarizing numeric data, filtering large datasets, and building an index for fast data lookup. First, you'll learn about how you start "Thinking MapReduce" including what's involved and what needs to be broken down to start thinking in these terms. Next, you'll explore how to compute numeric summary metrics, and how to filter large data sets. Finally, you'll wrap up the course by learning about building indices, and why an inverted index is so important in the context of search engines. After watching this course, you'll have the confidence to spot patterns in MapReduce problems and will be on you're way to mastering this programming model.
Table of contents
- Insights from Numeric Summary Metrics 3m
- The Numeric Summarization MapReduce Pattern 4m
- Getting Census Data to Extract Insights 2m
- Calculating Averages: Map Code 4m
- Calculating Averages: Reduce Code 3m
- Calculating Averages: The Main Class 5m
- Combiners for Greater Parallelism 3m
- Calculating Averages: Using a Reducer as a Combiner 3m
- Calculating Averages: The Right Way to Use a Combiner 3m
- The Custom Writable Data Type 4m
- Calculating Averages with a Combiner: Custom WritableComparable 4m
- Calculating Averages with a Combiner: Updating the MapReduce Code 2m
- Calculating Averages with a Combiner: Combiner Code 3m