- Big Data Analytics
- Table of Contents
- Big Data Analytics
- Credits
- About the Author
- Acknowledgement
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Big Data Analytics at a 10,000-Foot View
- 2. Getting Started with Apache Hadoop and Apache Spark
- 3. Deep Dive into Apache Spark
- Starting Spark daemons
- Learning Spark core concepts
- Lifecycle of Spark program
- Spark applications
- Persistence and caching
- Spark resource managers – Standalone, YARN, and Mesos
- Summary
- 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets
- History of Spark SQL
- Architecture of Spark SQL
- Introducing SQL, Datasources, DataFrame, and Dataset APIs
- Evolution of DataFrames and Datasets
- Why Datasets and DataFrames?
- When to use RDDs, Datasets, and DataFrames?
- Analytics with DataFrames
- Analytics with the Dataset API
- Data Sources API
- Spark SQL as a distributed SQL engine
- Hive on Spark
- Summary
- 5. Real-Time Analytics with Spark Streaming and Structured Streaming
- 6. Notebooks and Dataflows with Spark and Hadoop
- 7. Machine Learning with Spark and Hadoop
- 8. Building Recommendation Systems with Spark and Mahout
- 9. Graph Analytics with GraphX
- 10. Interactive Analytics with SparkR
- Index
Recommendation systems have some limitations. Understanding these limitations is important in order to build a successful recommendation system:
- The cold-start problem: Collaborative filtering systems are based on the action of available data from similar users. If you are building a brand new recommendation system, you would have no user data to start with. You can use content-based filtering first and then move on to the collaborative filtering approach.
- Scalability: As the number of users grow, the algorithms suffer scalability issues. If you have 10 million customers and 100,000 movies, you would have to create a sparse matrix with one trillion elements.
- The lack of right data: Input data may not always be accurate because humans are not perfect at providing ratings. User behavior is more important than ratings. Item-based recommendations provide a better answer in this case.
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