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Making Sense of NoSQL [Paperback / softback]

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  • Format: Paperback / softback, 300 pages, height x width x depth: 232x189x18 mm, weight: 542 g
  • Pub. Date: 12-Sep-2013
  • Publisher: Manning Publications
  • ISBN-10: 1617291072
  • ISBN-13: 9781617291074
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  • Price: 39,69 €*
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Making Sense of NoSQLLarger Image
  • Format: Paperback / softback, 300 pages, height x width x depth: 232x189x18 mm, weight: 542 g
  • Pub. Date: 12-Sep-2013
  • Publisher: Manning Publications
  • ISBN-10: 1617291072
  • ISBN-13: 9781617291074
Other books in subject:
Permanent link: https://www.kriso.ee/db/9781617291074.html
Keywords:
Provides information and case studies describing the capabilities of NoSQL technologies, covering such topics as data architecture patterns, big data, and security.

Summary

Making Sense of NoSQL clearly and concisely explains the concepts, features, benefits, potential, and limitations of NoSQL technologies. Using examples and use cases, illustrations, and plain, jargon-free writing, this guide shows how you can effectively assemble a NoSQL solution to replace or augment the traditional RDBMS you have now.

About this Book

If you want to understand and perhaps start using the new data storage and analysis technologies that go beyond the SQL database model, this book is for you. Written in plain language suitable for technical managers and developers, and using many examples, use cases, and illustrations, this book explains the concepts, features, benefits, potential, and limitations of NoSQL.

Making Sense of NoSQL starts by comparing familiar database concepts to the new NoSQL patterns that augment or replace them. Then, you'll explore case studies on big data, search, reliability, and business agility that apply these new patterns to today's business problems. You'll see how NoSQL systems can leverage the resources of modern cloud computing and multiple-CPU data centers. The final chapters show you how to choose the right NoSQL technologies for your own needs.

Managers and developers will welcome this lucid overview of the potential and capabilities of NoSQL technologies.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

What's Inside

  • NoSQL data architecture patterns
  • NoSQL for big data
  • Search, high availability, and security
  • Choosing an architecture

About the Authors

Dan McCreary and Ann Kelly lead an independent training and consultancy firm focused on NoSQL solutions and are cofounders of the NoSQL Now! Conference.

Table of Contents

    PART 1 INTRODUCTION
  1. NoSQL: It's about making intelligent choices
  2. NoSQL concepts

    3. PART 2 DATABASE PATTERNS

  3. Foundational data architecture patterns

  4. NoSQL data architecture patterns

  5. Native XML databases

    6. PART 3 NOSQL SOLUTIONS

  6. Using NoSQL to manage big data

  7. Finding information with NoSQL search

  8. Building high-availability solutions with NoSQL

  9. Increasing agility with NoSQL

    10. PART 4 ADVANCED TOPICS
  10. NoSQL and functional programming
  11. Security: protecting data in your NoSQL systems
  12. Selecting the right NoSQL solution
Foreword xvii
Preface xix
Acknowledgments xxi
About This Book xxii
Part 1 Introduction 1(34)
1 NoSQL: It's about making intelligent choices
3(12)
1.1 What is NoSQL?
4(2)
1.2 NoSQL business drivers
6(2)
Volume
7(1)
Velocity
7(1)
Variability
7(1)
Agility
8(1)
1.3 NoSQL case studies
8(5)
Case study: LiveJournal's Memcache
9(1)
Case study: Google's MapReduce-use commodity hardware to create search indexes
10(1)
Case study: Google's Bigtable-a table with a billion rows and a million columns
11(1)
Case study: Amazon's Dynamo-accept an order 24 hours a day, 7 days a week
11(1)
Case study: MarkLogic
12(1)
Applying your knowledge
12(1)
1.4 Summary
13(2)
2 NoSQL concepts
15(20)
2.1 Keeping components simple to promote reuse
15(2)
2.2 Using application tiers to simplify design
17(4)
2.3 Speeding performance by strategic use of RAM, SSD and disk
21(1)
2.4 Using consistent hashing to keep your cache current
22(2)
2.5 Comparing ACID and BASE-two methods of reliable database transactions
24(4)
RDBMS transaction control using ACID
25(2)
Non-RDBMS transaction control using BASE
27(1)
2.6 Achieving horizontal scalability with database sharding
28(2)
2.7 Understanding trade-offs with Brewer's CAP theorem
30(2)
2.8 Apply your knowledge
32(1)
2.9 Summary
33(1)
2.10 Further reading
33(2)
Part 2 Database Patterns 35(90)
3 Foundational data architecture patterns
37(25)
3.1 What is a data architecture pattern?
38(1)
3.2 Understanding the row-store design pattern used in RDBMSs
39(4)
How row stores work
39(2)
Row stores evolve
41(1)
Analyzing the strengths and weaknesses of the row-store pattern
42(1)
3.3 Example: Using joins in a sales order
43(2)
3.4 Reviewing RDBMS implementation features
45(6)
RDBMS transactions
45(2)
Fixed data definition language and typed columns
47(1)
Using RDBMS views for security and access control
48(1)
RDBMS replication and synchronization
49(2)
3.5 Analyzing historical data with OLAP, data warehouse and business intelligence systems
51(6)
How data flows from operational systems to analytical systems
52(2)
Getting familiar with OLAP concepts
54(1)
Ad hoc reporting using aggregates
55(2)
3.6 Incorporating high availability and read-mostly systems
57(1)
3.7 Using hash trees in revision control systems and database synchronization
58(2)
3.8 Apply your knowledge
60(1)
3.9 Summary
60(1)
3.10 Further reading
61(1)
4 NoSQL data architecture patterns
62(34)
4.1 Key-value stores
63(9)
What is a key-value store?
64(1)
Benefits of using a key-value store
65(3)
Using a key-value store
68(2)
Use case: storing web pages in a key-value store
70(1)
Use case: Amazon simple storage service (S3)
71(1)
4.2 Graph stores
72(9)
Overview of a graph store
72(2)
Linking external data with the RDF standard
74(1)
Use cases for graph stores
75(6)
4.3 Column family (Bigtable) stores
81(5)
Column family basics
82(1)
Understanding column family keys
82(1)
Benefits of column family systems
83(2)
Case study: storing analytical information in Bigtable
85(1)
Case study: Google Maps stores geographic information in Bigtable
85(1)
Case study: using a column family to store user preferences
86(1)
4.4 Document stores
86(5)
Document store basics
87(1)
Document collections
88(1)
Application collections
88(1)
Document store APIs
89(1)
Document store implementations
89(1)
Case study: ad server with MongoDB
90(1)
Case study: CouchDB, a large-scale object database
91(1)
4.5 Variations of NoSQL architectural patterns
91(4)
Customization for RAM or SSD stores
92(1)
Distributed stores
92(1)
Grouping items
93(2)
4.6 Summary
95(1)
4.7 Further reading
95(1)
5 Native XML databases
96(29)
5.1 What is a native XML database?
97(3)
5.2 Building applications with a native XML database
100(10)
Loading data can be as simple as drag-and-drop
101(1)
Using collections to group your XML documents
102(3)
Applying simple queries to transform complex data with XPath
105(1)
Transforming your data with XQuery
106(3)
Updating documents with XQuery updates
109(1)
XQuery full-text search standards
110(1)
5.3 Using XML standards within native XML databases
110(2)
5.4 Designing and validating your data with XML Schema and Schematron
112(3)
XML Schema
112(1)
Using Schematron to check document rules
113(2)
5.5 Extending XQuery with custom modules
115(1)
5.6 Case study: using NoSQL at the Office of the Historian at the Department of State
115(4)
5.7 Case study: managing financial derivatives with MarkLogic
119(3)
Why financial derivatives are difficult to store in RDBMSs
119
An investment bank switches from
20(99)
RDBMSs to one native XML system
119(2)
Business benefits of moving to a native XML document store
121(1)
Project results
122(1)
5.8 Summary
122(1)
5.9 Further reading
123(2)
Part 3 NoSQL Solutions 125(82)
6 Using NoSQL to manage big data
127(27)
6.1 What is a big data NoSQL solution?
128(4)
6.2 Getting linear scaling in your data center
132(1)
6.3 Understanding linear scalability and expressivity
133(2)
6.4 Understanding the types of big data problems
135(1)
6.5 Analyzing big data with a shared-nothing architecture
136(1)
6.6 Choosing distribution models: master-slave versus peer-to-peer
137(2)
6.7 Using MapReduce to transform your data over distributed systems
139(4)
MapReduce and distributed filesystems
140(2)
How MapReduce allows efficient transformation of big data problems
142(1)
6.8 Four ways that NoSQL systems handle big data problems
143(3)
Moving queries to the data, not data to the queries
143(1)
Using hash rings to evenly distribute data on a cluster
144(1)
Using replication to scale reads
145(1)
Letting the database distribute queries evenly to data nodes
146(1)
6.9 Case study: event log processing with Apache Flume
146(4)
Challenges of event log data analysis
147(1)
How Apache Flume works to gather distributed event data
148(1)
Further thoughts
149(1)
6.10 Case study: computer-aided discovery of health care fraud
150(2)
What is health care fraud detection?
150(1)
Using graphs and custom shared-memory hardware to detect health care fraud
151(1)
6.11 Summary
152(1)
6.12 Further reading
153(1)
7 Finding information with NoSQL search
154(18)
7.1 What is NoSQL search?
155(1)
7.2 Types of search
155(3)
Comparing Boolean, full-text keyword and structured search models
155(1)
Examining the most common types of search
156(2)
7.3 Strategies and methods that make NoSQL search effective
158(3)
7.4 Using document structure to improve search quality
161(1)
7.5 Measuring search quality
162(1)
7.6 In-node indexes versus remote search services
163(1)
7.7 Case study: using MapReduce to create reverse indexes
164(2)
7.8 Case study: searching technical documentation
166(2)
What is technical document search?
166(1)
Retaining document structure in a NoSQL document store
167(1)
7.9 Case study: searching domain-specific languages-findability and reuse
168(2)
7.10 Apply your knowledge
170(1)
7.11 Summary
170(1)
7.12 Further reading
171(1)
8 Building high-availability solutions with NoSQL
172(20)
8.1 What is a high-availability NoSQL database?
173(1)
8.2 Measuring availability of NoSQL databases
174(4)
Case study: the Amazon's S3 SLA
176(1)
Predicting system availability
176(1)
Apply your knowledge
177(1)
8.3 NoSQL strategies for high availability
178(6)
Using a load balancer to direct traffic to the least busy node
178(1)
Using high-availability distributed filesystems with NoSQL databases
179(1)
Case study: using HDFS as a high-availability filesystem to store master data
180(2)
Using a managed NoSQL service
182(1)
Case study: using Amazon DynamoDB for a high-availability data store
182(2)
8.4 Case study: using Apache Cassandra as a high-availability column family store
184(3)
Configuring data to node mappings with Cassandra
185(2)
8.5 Case study: using Couchbase as a high-availability document store
187(2)
8.6 Summary
189(1)
8.7 Further reading
190(2)
9 Increasing agility with NoSQL
192(15)
9.1 What is software agility?
193(3)
Apply your knowledge: local or cloud-based deployment?
195(1)
9.2 Measuring agility
196(3)
9.3 Using document stores to avoid object-relational mapping
199(2)
9.4 Case study: using XRX to manage complex forms
201(4)
What are complex business forms?
201(1)
Using XRX to replace client JavaScript and object-relational mapping
202(3)
Understanding the impact of XRX on agility
205(1)
9.5 Summary
205(1)
9.6 Further reading
206(1)
Part 4 Advanced Topics 207(68)
10 NoSQL and functional programming
209(23)
10.1 What is functional programming?
210(9)
Imperative programming is managing program state
211(2)
Functional programming is parallel transformation without side effects
213(3)
Comparing imperative and functional programming at scale
216(1)
Using referential transparency to avoid recalculating transforms
217(2)
10.2 Case study: using NetKernel to optimize web page content assembly
219(3)
Assembling nested content and tracking component dependencies
219(1)
Using NetKernel to optimize component regeneration
220(2)
10.3 Examples of functional programming languages
222(1)
10.4 Making the transition from imperative to functional programming
223(3)
Using functions as a parameter of a function
223(1)
Using recursion to process unstructured document data
224(1)
Moving from mutable to immutable variables
224(1)
Removing loops and conditionals
224(1)
The new cognitive style: from capturing state to isolated transforms
225(1)
Quality, validation and consistent unit testing
225(1)
Concurrency in functional programming
226(1)
10.5 Case study: building NoSQL systems with Erlang
226(3)
10.6 Apply your knowledge
229(1)
10.7 Summary
230(1)
10.8 Further reading
231(1)
11 Security: protecting data in your NoSQL systems
232(22)
11.1 A security model for NoSQL databases
233(4)
Using services to mitigate the need for in-database security
235(1)
Using data warehouses and OLAP to mitigate the need for in-database security
235(1)
Summary of application versus database-layer security benefits
236(1)
11.2 Gathering your security requirements
237(9)
Authentication
237(3)
Authorization
240(2)
Audit and logging
242(1)
Encryption and digital signatures
243(2)
Protecting pubic websites from denial of service and injection attacks
245(1)
11.3 Case Study: access controls on key-value store-Amazon S3
246(3)
Identity and Access Management (IAM)
247(1)
Access-control lists (ACL)
247(1)
Bucket policies
248(1)
11.4 Case study: using key visibility with Apache Accumulo
249(1)
11.5 Case study: using MarkLogic's RBAC model in secure publishing
250(2)
Using the MarkLogic RBAC security model to protect documents
250(1)
Using MarkLogic in secure publishing
251(1)
Benefits of the MarkLogic security model
252(1)
11.6 Summary
252(1)
11.7 Further reading
253(1)
12 Selecting the right NoSQL solution
254(21)
12.1 What is architecture trade-off analysis?
255(2)
12.2 Team dynamics of database architecture selection
257(3)
Selecting the right team
258(1)
Accounting for experience bias
259(1)
Using outside consultants
259(1)
12.3 Steps in architectural trade-off analysis
260(3)
12.4 Analysis through decomposition: quality trees
263(4)
Sample quality attributes
264(2)
Evaluating hybrid and cloud architectures
266(1)
12.5 Communicating the results to stakeholders
267(4)
Using quality trees as navigational maps
267(2)
Apply your knowledge
269(1)
Using quality trees to communicate project risks
270(1)
12.6 Finding the right proof-of-architecture pilot project
271(2)
12.7 Summary
273(1)
12.8 Further reading
274(1)
Index 275
AUTHOR BIO

 





Dan McCreary is a consultant with a focus on advanced technologies. He has worked for Bell Labs, the supercomputing industry, Steve Jobs NeXT Computer and cofounded a75-person consulting company. Beginning in 2000 he has focused on XML integration technologies and in 2006 he started focusing on native XML and XQuery systems. He is cofounder of the NoSQL Now! Conference.





 





Ann Kelly is a NoSQL developer and NoSQL project manager with over 20 years experience in the computer and insurance industries. She has built NoSQL solutions for federal agencies as well as commercial projects. She is a CPCU (Chartered Property Casualty Underwriter)