Joe Celko's Complete Guide to NoSQL [electronic resource] : What Every SQL Professional Needs to Know about Non-Relational Databases

Celko, Joe [Browse]
1st edition
Burlington : Elsevier Science, 2013.
1 online resource (245 p.)


Summary note
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
Description based upon print version of record.
Language note
  • Front Cover; Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about NonRelational Databases; Copyright; Dedication; Contents; About the Author; Introduction; Chapter 1: NoSQL and Transaction Processing; Introduction; 1.1. Databases Transaction Processing in the Batch Processing World; 1.2. Transaction Processing in the Disk Processing World; 1.3. ACID; 1.4. Pessimistic Concurrency in Detail; 1.4.1. Isolation Levels; 1.4.2. Proprietary Isolation Levels; 1.5. CAP Theorem; 1.6. BASE; 1.7. Server-side Consistency; 1.8. Error Handling; 1.9. Why SQL Does Not Work Here
  • Concluding ThoughtsReferences; Chapter 2: Columnar Databases; Introduction; 2.1. History; 2.2. How It Works; 2.3. Query Optimizations; 2.4. Multiple Users and Hardware; 2.5. Doing an ALTER Statement; 2.6. Data Warehouses and Columnar Databases; Concluding Thoughts; Reference; Chapter 3: Graph Databases; Introduction; 3.1. Graph Theory Basics; 3.1.1. Nodes; 3.1.2. Edges; 3.1.3. Graph Structures; 3.2. RDBMS Versus Graph Database; 3.3. Six Degrees of Kevin Bacon Problem; 3.3.1. Adjacency List Model for General Graphs; 3.3.2. Covering Paths Model for General Graphs
  • 3.3.3. Real-World Data Has Mixed Relationships3.4. Vertex Covering; 3.5. Graph Programming Tools; 3.5.1. Graph Databases; 3.5.2. Graph Languages; SPARQL; SPASQL; Gremlin; Cypher (NEO4j); Trends; Concluding Thoughts; References; Chapter 4: MapReduce Model; Introduction; 4.1. Hadoop Distributed File System; 4.2. Query Languages; 4.2.1. Pig Latin; 4.2.2. Hive and Other Tools; Concluding Thoughts; References; Chapter 5: Streaming Databases and Complex Events; Introduction; 5.1. Generational Concurrency Models; 5.1.1. Optimistic Concurrency; 5.1.2. Isolation Levels in Optimistic Concurrency
  • 5.2. Complex Event Processing5.2.1. Terminology for Event Processing; 5.2.2. Event Processing versus State Change Constraints; 5.2.3. Event Processing versus Petri Nets; 5.3. Commercial Products; 5.3.1. StreamBase 1; 5.3.2. Kx 2; Concluding Thoughts; References; Chapter 6: Key-Value Stores; Introduction; 6.1. Schema Versus no Schema; 6.2. Query Versus Retrieval; 6.3. Handling Keys; 6.3.1. Berkeley DB; 6.3.2. Access by Tree Indexing or Hashing; 6.4. Handling Values; 6.4.1. Arbitrary Byte Arrays; 6.4.2. Small Files of Known Structure; 6.5. Products; Concluding Thoughts; Chapter 7: Textbases
  • Introduction7.1. Classic Document Management Systems; 7.1.1. Document Indexing and Storage; 7.1.2. Keyword and Keyword in Context; 7.1.3. Industry Standards; Contextual Query Language; Commercial Services and Products; Regular Expressions; 7.2. Text Mining and Understanding; 7.2.1. Semantics versus Syntax; 7.2.2. Semantic Networks; 7.3. Language Problem; 7.3.1. Unicode and ISO Standards; 7.3.2. Machine Translation; Concluding Thoughts; References; Chapter 8: Geographical Data; Introduction; 8.1. GIS Queries; 8.1.1. Simple Location; 8.1.2. Simple Distance
  • 8.1.3. Find Quantities, Densities, and Contents within an Area
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