pt. I. Getting started with big data
1. Grasping the fundamentals of big data
Evolution of data management
Understanding the waves of managing data
Creating manageable data structures
Web and content management
Building a successful big data management architecture
Capture, organize, integrate, analyze and act
Traditional and advanced analytics
2. Examining big data types
Exploring sources of big structured data
Understanding the role of relational databases in big data
Defining unstructured data
Exploring sources of unstructured data
Understanding the role of a CMS in big data management
Looking at real-time and non-real-time requirements
Putting big data together
Managing different data types
Integrating data types into a big data environment
3. Old meets new: distributed computing
Brief history of distributed computing
The value of a consistent model
Understanding the basics of distributed computing
Why we need distributed computing for big data
The changing economics of computing
Getting performance right.
pt. II. technology foundations for big data
4. Digging into the big data technology components
Exploring the big data stack
Redundant physical infrastructure
Physical redundant networks
Managing hardware : storage and servers
Infrastructure operations
Interfaces and feeds to and from applications and the internet
Organizing data services and tools
Analytical data warehouses
5. Virtualization and how it supports distributed computing
Understanding the basics of virtualization
The importance of virtualization to big data
Application virtualization
Processor and memory virtualization
Data and storage virtualization
Managing virtualization with the Hypervisor
Abstraction and virtualization
Implementing virtualization to work with big data
6. Examining the cloud and big data
Defining the cloud in the context of big data
Understanding cloud deployment and delivery models
The cloud as an imperative for big data
Making use of the cloud for big data
Providers in the big data cloud market
Amazon's public Elastic Compute Cloud
Where to be careful when using cloud services.
pt. III. Big data management
RDBMs are important in a big data environment
PostgreSQL relational database
8. MapReduce fundamentals
Tracing the origins of MapReduce
Understanding the map function
Adding the reduce function
Putting map and reduce together
Optimizing MapReduce tasks
Hardware/network topology
9. Exploring the world of Hadoop
Understanding the Hadoop Distributed File system (HDFS)
10. The Hadoop foundation and ecosystem
Building a big data foundation with the Hadoop ecosystem
Managing resources and applications with Hadoop YARN
Storing big data with HBase
Mining big data with Hive
Interacting with the Hadoop ecosystem
11. Appliances and big data warehouses
Integrating big data with the traditional data warehouse
Optimizing the data warehouse
Differentiating big data structures from data warehouse data
Examining a hybrid process case study
Big data analysis and the data warehouse
Rethinking extraction, transformation, and loading
Changing the role of the data warehouse
Changing deployment models in the Big data era
Examining the future of data warehouses.
pt. IV. Analytics and big data
12. Defining big data analytics
Using big data to get results
Operationalized analytics
Modifying business intelligence products to handle big data
Studying big data analytics examples
Big data analytics solutions
13. Understanding text analytics and big data
Exploring unstructured data
Understanding text analytics
Difference between text analytics and search
Analysis and extraction techniques
Understanding the extracted information
Putting your results together with structured data
Text analytics tools for big data
14. Customized approaches for analysis of big data.
pt. V. Big data implementation
15. Integrating data sources
Identifying the data you need
Integration and incorporation stage
Understanding the fundamentals of big data integration
Understanding ELT : extract, load, and transform
Prioritizing big data quality
Best practices for data integration in a big data world
16. Dealing with real-time data streams and complex event processing
Explaining streaming data and complex event processing
The need for metadata in streams
Using complex event processing
Differentiating CEP from streams
Understanding the impact of streaming data and CEP on business
17. Operationalizing big data
Making big data a part of your operational process
Incorporating big data into the diagnosis of diseases
Understanding big data workflows
Workload in context to the business problem
Ensuring the validity, veracity, and volatility of big data
18. Applying big data within your organization
Figuring the economics of big data
Identification of data types and sources
Business process modifications or new process creation
The technology impact of big data workflows
Finding the talent to support big data projects
Calculating the return on investment (ROI) from big data investments
Enterprise data management and big data
Defining enterprise data management
Creating a big data implementation road map
Understanding business urgency
Projecting the right amount of capacity
Selecting the right software development methodology
Balancing budgets and skill sets
Determining your appetite for risk
Starting your big data road map
19. Security and governance for big data environments
Security in context with big data
Assessing the risk for the business
Risks lurking inside big data
Understanding data protection options
The data governance challenge
Auditing your big data process
Identifying the key stakeholders
Putting th right organizational structure in place
Preparing for stewardship and management of risk
Setting the right governance and quality policies
Developing a well-governed and secure big data environment.
pt. VI. Big data solutions in the real world
20. The importance of big data to business
Big data as business planning tool
Adding new dimensions to the planning cycle
Keeping data analytics in perspective
Getting started with the right foundation
Getting your big data strategy started
Transforming business processes with big data
21. Analyzing data in motion : a real-world view
Understanding companies' needs for data in motion
The value of streaming data
Streaming data with an environmental impact
Using sensors to provide real-time information about rivers and oceans
The benefits of real-time data
Streaming data with a public policy impact
Streaming data in the healthcare industry
Capturing the data stream
Streaming data in the energy industry
Using streaming data to increase energy efficiency
Using streaming data to advance the production of alternative sources of energy
Connecting streaming data to historical and other real-time data sources
22. Improving business processes with big data analytics: a real-world view
Understanding companies' needs for big data analytics
Improving the customer experience with text analytics
The business value to the big data analytics implementation
Using big data analytics to determine next best action
Preventing fraud with big data analytics
The business benefit of integrating new sources of data.
pt. VII. The part of tens
23. Ten big data best practices
24. Ten great big data resources
The Cloud Security Alliance
National Institute of Standards and Technology
apache Software Foundation
Online collaborative sites
25. Ten big data do's and don'ts
