The 3 V's stand for:
- Volume
- Variety
- Velocity
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Exploring the Data Ecosystem
- Defining a Reference Architecture for Data-Intensive Systems
- What is a reference architecture?
- Reference architecture for a data-intensive system
- Oracle's information management conceptual reference architecture
- Oracle's information management reference architecture
- Desired properties of a data-intensive system
- Defining architectural principles
- Listing architectural assumptions
- Architectural capabilities
- Summary
- Patterns of the Data Intensive Architecture
- Discussing Data-Centric Architectures
- Understanding Data Collection and Normalization Requirements and Techniques
- Creating a Data Pipeline for Consistent Data Collection, Processing, and Dissemination
- Building a Robust and Fault-Tolerant Data Collection System
- Challenges of Data Processing
- Let Us Process Data in Batches
- Handling Streams of Data
- What is a streaming system?
- Capabilities (and non-capabilities) of a streaming application
- High-level reference architecture
- Samza architecture
- Kafka streams
- Samza's stream processing API
- The scheduler/executor component of the streaming architecture
- Processing concepts and tradeoffs
- Micro-batch stream processing
- Windowing
- Summary
- References
- Let Us Store the Data
- The data explosion problem
- Relational Database Management Systems and Big data
- Introducing Hadoop, the Big Elephant
- HDFS file formats
- HBase
- Graph stores
- Semantic graph
- Stardog
- BITES – Unstructured/Semistructured document store
- Data integrity and validating constraints
- Monitoring and operation
- Performance
- Summary
- Further reading
- When Data Dissemination is as Important as Data Itself
- Data dissemination
- Discussing the non-functional aspects of the preceding architecture
- Summary
- Other Books You May Enjoy
The 3 V's
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