Joe Celko's Complete guide to NoSQL : what every SQL professional needs to know about nonrelational databases / Joe Celko.

By:

Celko, Joe

Material type: Text

TextPublication details: Amsterdam ; Boston : Elsevier/Morgan Kaufmann, [2014]Description: xx, 224 pages : illustrations ; 24 cmISBN:

9780124071926 (paperback : alk. paper)
0124071929 (paperback : alk. paper)

Other title:

Complete guide to NoSQL

Subject(s):

DDC classification:

005.75 CEL

Contents:

NoSQL and transaction processing -- Columnar databases -- Graph databases -- The MapReduce model -- Streaming databases and complex events -- Key-value stores -- Textbases -- Geographical data (GIS) -- Big data and the cloud -- Biometrics, fingerprints and specialized databases -- Analytic databases -- Multi-valued or NFNF databases -- Hierarchical and network database systems.

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Standard Loan	Thurles Library Main Collection	005.75 CEL (Browse shelf(Opens below))	1	Available		39002100655670

Enhanced descriptions from Syndetics:

Joe Celko's Complete Guide to NoSQL provides a complete overview of non-relational technologies so that you can become more nimble to meet the needs of your organization. As data continues to explode and grow more complex, SQL is becoming less useful for querying data and extracting meaning. In this new world of bigger and faster data, you will need to leverage non-relational technologies to get the most out of the information you have. Learn where, when, and why the benefits of NoSQL outweigh those of SQL with Joe Celko's Complete Guide to NoSQL .

This book covers three areas that make today's new data different from the data of the past: velocity, volume and variety. When information is changing faster than you can collect and query it, it simply cannot be treated the same as static data. Celko will help you understand velocity, to equip you with the tools to drink from a fire hose. Old storage and access models do not work for big data. Celko will help you understand volume, as well as different ways to store and access data such as petabytes and exabytes. Not all data can fit into a relational model, including genetic data, semantic data, and data generated by social networks. Celko will help you understand variety, as well as the alternative storage, query, and management frameworks needed by certain kinds of data.

Includes bibliographical references and index.

Table of contents provided by Syndetics

About the Author (p. xv)
Introduction (p. xvii)
1 NoSQL and Transaction Processing (p. 1)
Introduction (p. 1)
1.1 Databases Transaction Processing in the Batch Processing World (p. 1)
1.2 Transaction Processing in the Disk Processing World (p. 2)
1.3 ACID (p. 3)
1.4 Pessimistic Concurrency in Detail (p. 5)
1.4.1 Isolation Levels (p. 6)
1.4.2 Proprietary Isolation Levels (p. 8)
1.5 CAP Theorem (p. 10)
1.6 BASE (p. 11)
1.7 Server-side Consistency (p. 13)
1.8 Error Handling (p. 13)
1.9 Why SQL Does Not Work Here (p. 14)
Concluding Thoughts (p. 14)
References (p. 14)
2 Columnar Databases (p. 15)
Introduction (p. 15)
2.1 History (p. 16)
2.2 How It Works (p. 21)
2.3 Query Optimizations (p. 22)
2.4 Multiple Users and Hardware (p. 22)
2.5 Doing an ALTER Statement (p. 24)
2.6 Data Warehouses and Columnar Databases (p. 24)
Concluding Thoughts (p. 25)
Reference (p. 25)
3 Graph Databases (p. 27)
Introduction (p. 27)
3.1 Graph Theory Basics (p. 28)
3.1.1 Nodes (p. 28)
3.1.2 Edges (p. 29)
3.1.3 Graph Structures (p. 30)
3.2 RDBMS Versus Graph Database (p. 31)
3.3 Six Degrees of Kevin Bacon Problem (p. 31)
3.3.1 Adjacency List Model for General Graphs (p. 31)
3.3.2 Covering Paths Model for General Graphs (p. 35)
3.3.3 Real-World Data Has Mixed Relationships (p. 38)
3.4 Vertex Covering (p. 40)
3.5 Graph Programming Tools (p. 42)
3.5.1 Graph Databases (p. 42)
3.5.2 Graph Languages (p. 43)
Concluding Thoughts (p. 46)
References (p. 46)
4 MapReduce Model (p. 47)
Introduction (p. 47)
4.1 Hadoop Distributed File System (p. 49)
4.2 Query Languages (p. 50)
4.2.1 Pig Latin (p. 50)
4.2.2 Hive and Other Tools (p. 60)
Concluding Thoughts (p. 62)
References (p. 62)
5 Streaming Databases and Complex Events (p. 63)
Introduction (p. 63)
5.1 Generational Concurrency Models (p. 64)
5.1.1 Optimistic Concurrency (p. 64)
5.1.2 Isolation Levels in Optimistic Concurrency (p. 65)
5.2 Complex Event Processing (p. 67)
5.2.1 Terminology for Event Processing (p. 68)
5.2.2 Event Processing versus State Change Constraints (p. 70)
5.2.3 Event Processing versus Petri Nets (p. 71)
5.3 Commercial Products (p. 73)
5.3.1 StreamBase (p. 73)
5.3.2 Kx (p. 76)
Concluding Thoughts (p. 79)
References (p. 79)
6 Key-Value Stores (p. 81)
Introduction (p. 81)
6.1 Schema Versus no Schema (p. 81)
6.2 Query Versus Retrieval (p. 82)
6.3 Handling Keys (p. 82)
6.3.1 Berkeley DB (p. 83)
6.3.2 Access by Tree Indexing or Hashing (p. 84)
6.4 Handling Values (p. 84)
6.4.1 Arbitrary Byte Arrays (p. 84)
6.4.2 Small Files of Known Structure (p. 85)
6.5 Products (p. 86)
Concluding Thoughts (p. 88)
7 Textbases (p. 89)
Introduction (p. 89)
7.1 Classic Document Management Systems (p. 89)
7.1.1 Document Indexing and Storage (p. 90)
7.1.2 Keyword and Keyword in Context (p. 90)
7.1.3 Industry Standards (p. 92)
7.2 Text Mining and Understanding (p. 96)
7.2.1 Semantics versus Syntax (p. 97)
7.2.2 Semantic Networks (p. 98)
7.3 Language Problem (p. 99)
7.3.1 Unicode and ISO Standards (p. 100)
7.3.2 Machine Translation (p. 100)
Concluding Thoughts (p. 101)
References (p. 102)
8 Geographical Data (p. 103)
Introduction (p. 103)
8.1 G1S Queries (p. 105)
8.1.1 Simple Location (p. 105)
8.1.2 Simple Distance (p. 105)
8.1.3 Find Quantities, Densities, and Contents within an Area (p. 105)
8.1.4 Proximity Relationships (p. 106)
8.1.5 Temporal Relationships (p. 106)
8.2 Locating Places (p. 106)
8.2.1 Longitude and Latitude (p. 107)
8.2.2 Hierarchical Triangular Mesh (p. 108)
8.2.3 Street Addresses (p. 111)
8.2.4 Postal Codes (p. 112)
8.2.5 ZIP Codes (p. 112)
8.2.6 Canadian Postal Codes (p. 113)
8.2.7 Postcodes in the United Kingdom (p. 114)
8.3 SQL Extensions for GIS (p. 116)
Concluding Thoughts (p. 116)
References (p. 117)
9 Big Data and Cloud Computing (p. 119)
Introduction (p. 119)
9.1 Objections to Big Data and the Cloud (p. 121)
9.1.1 Cloud Computing Is a Fad (p. 121)
9.1.2 Cloud Computing Is Not as Secure as In-house Data Servers (p. 122)
9.1.3 Cloud Computing Is Costly (p. 122)
9.1.4 Cloud Computing Is Complicated (p. 122)
9.1.5 Cloud Computing Is Meant for Big Companies (p. 122)
9.1.6 Changes Are Only Technical (p. 123)
9.1.7 If the Internet Goes Down, the Cloud Becomes Useless (p. 124)
9.2 Big Data and Data Mining (p. 124)
9.2.1 Big Data for Nontraditional Analysis (p. 125)
9.2.2 Big Data for Systems Consolidation (p. 126)
Concluding Thoughts (p. 127)
References (p. 128)
10 Biometrics, Fingerprints, and Specialized Databases (p. 129)
Introduction (p. 129)
10.1 Naive Biometrics (p. 130)
10.2 Fingerprints (p. 132)
10.2.1 Classification (p. 132)
10.2.2 Matching (p. 133)
10.2.3 NIST Standards (p. 134)
10.3 DNA Identification (p. 136)
10.3.1 Basic Principles and Technology (p. 137)
10.4 Facial Databases (p. 138)
10.4.1 History (p. 139)
10.4.2 Who Is Using Facial Databases (p. 141)
10.4.3 How Good Is It? (p. 142)
Concluding Thoughts (p. 144)
References (p. 144)
11 Analytic Databases (p. 145)
Introduction (p. 145)
11.1 Cubes (p. 145)
11.2 Dr. Codd's OLAP Rules (p. 146)
11.2.1 Dr. Coddis Basic Features (p. 147)
11.2.2 Special Features (p. 149)
11.2.3 Reporting Features (p. 150)
11.2.4 Dimension Control (p. 150)
11.3 MOLAP (p. 151)
11.4 ROLAP (p. 151)
11.5 HOLAP (p. 152)
11.6 OLAP Query Languages (p. 152)
11.7 Aggregation Operators in SQL (p. 153)
11.7.1 Group By Grouping Set (p. 153)
11.7.2 ROLLUP (p. 154)
11.7.3 CUBE (p. 156)
11.7.4 Notes about Usage (p. 157)
11.8 OLAP Operators in SQL (p. 157)
11.8.1 OLAP Functionality (p. 158)
11.8.2 NTILE (n) (p. 164)
11.8.3 Nesting OLAP Functions (p. 165)
11.8.4 Sample Queries (p. 165)
11.9 Sparseness in Cubes (p. 166)
11.9.1 Hypercube (p. 167)
11.9.2 Dimensional Hierarchies (p. 168)
11.9.3 Drilling and Slicing (p. 170)
Concluding Thoughts (p. 170)
References (p. 171)
12 Multivalued or NFNF Databases (p. 173)
Introduction (p. 173)
12.1 Nested File Structures (p. 173)
12.2 Multivalued Systems (p. 176)
12.3 NFNF Databases (p. 178)
12.4 Existing Table-Valued Extensions (p. 182)
12.4.1 Microsoft SQL Server (p. 182)
12.4.2 Oracle Extensions (p. 182)
Concluding Thoughts (p. 184)
13 Hierarchical and Network Database Systems (p. 185)
Introduction (p. 185)
13.1 Types of Databases (p. 185)
13.2 Database History (p. 186)
13.2.1 DL/I (p. 187)
13.2.2 Control Blocks (p. 188)
13.2.3 Data Communications (p. 188)
13.2.4 Application Programs (p. 188)
13.2.5 Hierarchical Databases (p. 189)
13.2.6 Strengths and Weaknesses (p. 189)
13.3 Simple Hierarchical Database (p. 190)
13.3.1 Department Database (p. 192)
13.3.2 Student Database (p. 192)
13.3.3 Design Considerations (p. 192)
13.3.4 Example Database Expanded (p. 193)
13.3.5 Data Relationships (p. 194)
13.3.6 Hierarchical Sequence (p. 195)
13.3.7 Hierarchical Data Paths (p. 196)
13.3.8 Database Records (p. 197)
13.3.9 Segment Format (p. 198)
13.3.10 Segment Definitions (p. 199)
13.4 Summary (p. 199)
Concluding Thoughts (p. 200)
References (p. 201)
Glossary (p. 203)
Index (p. 21)

Author notes provided by Syndetics

Joe Celko Independent Consultant