Book description
Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.).
- Explores various ways to leverage Big Data by effectively integrating it into the data warehouse
- Includes real-world case studies which clearly demonstrate Big Data technologies
- Provides insights on how to optimize current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface
- 1. Big Data introduction
-
2. Infrastructure and technology
- Introduction
- Distributed data processing
- Big data processing requirements
- Technologies for big data processing
- MapReduce
- MapReduce programming model
- MapReduce Google architecture
- History
- Hadoop core components
- NameNode
- DataNode
- Image
- Journal
- Checkpoint
- HDFS startup
- Block allocation and storage
- HDFS client
- Replication and recovery
- NameNode and DataNode—communication and management
- Heartbeats
- CheckPointNode and BackupNode
- CheckPointNode
- BackupNode
- Filesystem snapshots
- YARN scalability
- YARN execution flow
- Zookeeper features
- Locks and processing
- Failure and recovery
- Programming with Pig Latin
- Pig data types
- Running Pig programs
- Pig program flow
- Common Pig command
- HBASE architecture
- HBASE architecture implementation
- Hive architecture
- Execution—how does Hive process queries?
- Hive data types
- Hive examples
- HCatalog
- CAP theorem
- A keyspace has configurable properties that are critical to understand
- Cassandra ring architecture
- The design features of document-oriented databases include the following:
- 3. Building big data applications
- 4. Scientific research applications and usage
- 5. Pharmacy industry applications and usage
- 6. Visualization, storyboarding and applications
- 7. Banking industry applications and usage
-
8. Travel and tourism industry applications and usage
- Travel and big data
- Real-time conversion optimization
- Optimized disruption management
- Niche targeting and unique selling propositions
- “Smart” social media listening and sentiment analysis
- Hospitality industry and big data
- Analytics and travel industry
- Examples of the use of predictive analytics
- Develop applications using data and agile API
- 9. Governance
- 10. Building the big data application
- 11. Data discovery and connectivity
- Use cases from industry vendors
- Index
Product information
- Title: Building Big Data Applications
- Author(s):
- Release date: November 2019
- Publisher(s): Academic Press
- ISBN: 9780128158043
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