You are previewing Professional NoSQL.

Professional NoSQL

Cover of Professional NoSQL by Shashank Tiwari Published by Wrox
  1. Cover
  2. Contents
  3. Introduction
  4. Part I: Getting Started
    1. Chapter 1: NoSQL: What It Is and Why You Need it
      1. Definition and Introduction
      2. Sorted Ordered Column-Oriented Stores
      3. Key/Value Stores
      4. Document Databases
      5. Graph Databases
      6. Summary
    2. Chapter 2: Hello NoSQL: Getting Initial Hands-on Experience
      1. First Impressions — Examining Two Simple Examples
      2. Working with Language Bindings
      3. Summary
    3. Chapter 3: Interfacing and Interacting with NoSQL
      1. If No SQL, Then What?
      2. Language Bindings for NoSQL Data Stores
      3. Summary
  5. Part II: Learning the NoSQL Basics
    1. Chapter 4: Understanding the Storage Architecture
      1. Working with Column-Oriented Databases
      2. HBase Distributed Storage Architecture
      3. Document Store Internals
      4. Understanding Key/Value Stores in Memcached and Redis
      5. Eventually Consistent Non-relational Databases
      6. Summary
    2. Chapter 5: Performing CRUD Operations
      1. Creating Records
      2. Accessing Data
      3. Updating and Deleting Data
      4. Summary
    3. Chapter 6: Querying NoSQL Stores
      1. Similarities Between SQL and MongoDB Query Features
      2. Accessing Data from Column-Oriented Databases Like HBase
      3. Querying Redis Data Stores
      4. Summary
    4. Chapter 7: Modifying Data Stores and Managing Evolution
      1. Changing Document Databases
      2. Schema Evolution in Column-Oriented Databases
      3. HBase Data Import and Export
      4. Data Evolution in Key/Value Stores
      5. Summary
    5. Chapter 8: Indexing and Ordering Data Sets
      1. Essential Concepts Behind a Database Index
      2. Indexing and Ordering in MongoDB
      3. Creating and Using Indexes in MongoDB
      4. Indexing and Ordering in CouchDB
      5. Indexing in Apache Cassandra
      6. Summary
    6. Chapter 9: Managing Transactions and Data Integrity
      1. RDBMS AND ACID
      2. Distributed ACID Systems
      3. Upholding CAP
      4. Consistency Implementations in a Few NoSQL Products
      5. Summary
  6. Part III: Gaining Proficiency with NoSQL
    1. Chapter 10: Using NoSQL in the Cloud
      1. Google App Engine Data Store
      2. Amazon SimpleDB
      3. Summary
    2. Chapter 11: Scalable Parallel Processing with MapReduce
      1. Understanding MapReduce
      2. MapReduce with HBase
      3. MapReduce Possibilities and Apache Mahout
      4. Summary
    3. Chapter 12: Analyzing Big Data with Hive
      1. Hive Basics
      2. Back to Movie Ratings
      3. Good Old SQL
      4. JOIN(s) in Hive QL
      5. Summary
    4. Chapter 13: Surveying Database Internals
      1. MongoDB Internals
      2. Membase Architecture
      3. Hypertable Under the Hood
      4. Apache Cassandra
      5. Berkeley DB
      6. Summary
  7. Part IV: Mastering NoSQL
    1. Chapter 14: Choosing Among NoSQL Flavors
      1. Comparing NoSQL Products
      2. Benchmarking Performance
      3. Contextual Comparison
      4. Summary
    2. Chapter 15: Coexistence
      1. Using MySQL as a NoSQL Solution
      2. Mostly Immutable Data Stores
      3. Web Frameworks and NoSQL
      4. Migrating from RDBMS to NoSQL
      5. Summary
    3. Chapter 16: Performance Tuning
      1. Goals of Parallel Algorithms
      2. Influencing Equations
      3. Partitioning
      4. Scheduling in Heterogeneous Environments
      5. Additional MapReduce Tuning
      6. HBase Coprocessors
      7. Leveraging Bloom Filters
      8. Summary
    4. Chapter 17: Tools and Utilities
      1. RRDTool
      2. Nagios
      3. Scribe
      4. Flume
      5. Chukwa
      6. Pig
      7. Nodetool
      8. OpenTSDB
      9. Solandra
      10. Hummingbird and C5t
      11. GeoCouch
      12. Alchemy Database
      13. Webdis
      14. Summary
  8. Appendix: Installation and Setup Instructions
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INTRODUCTION

THE GROWTH OF USER-DRIVEN CONTENT has fueled a rapid increase in the volume and type of data that is generated, manipulated, analyzed, and archived. In addition, varied newer sets of sources, including sensors, Global Positioning Systems (GPS), automated trackers and monitoring systems, are generating a lot of data. These larger volumes of data sets, often termed big data, are imposing newer challenges and opportunities around storage, analysis, and archival.

In parallel to the fast data growth, data is also becoming increasingly semi-structured and sparse. This means the traditional data management techniques around upfront schema definition and relational references is also being questioned.

The quest to solve the problems related to large-volume and semi-structured data has led to the emergence of a class of newer types of database products. This new class of database products consists of column-oriented data stores, key/value pair databases, and document databases. Collectively, these are identified as NoSQL.

The products that fall under the NoSQL umbrella are quite varied, each with their unique sets of features and value propositions. Given this, it often becomes difficult to decide which product to use for the case at hand. This book prepares you to understand the entire NoSQL landscape. It provides the essential concepts that act as the building blocks for many of the NoSQL products. Instead of covering a single product exhaustively, it provides a fair coverage ...

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