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NoSQL For Dummies [Pehme köide]

  • Formaat: Paperback / softback, 464 pages, kõrgus x laius x paksus: 236x188x22 mm, kaal: 590 g
  • Ilmumisaeg: 17-Mar-2015
  • Kirjastus: For Dummies
  • ISBN-10: 1118905741
  • ISBN-13: 9781118905746
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  • Formaat: Paperback / softback, 464 pages, kõrgus x laius x paksus: 236x188x22 mm, kaal: 590 g
  • Ilmumisaeg: 17-Mar-2015
  • Kirjastus: For Dummies
  • ISBN-10: 1118905741
  • ISBN-13: 9781118905746
Teised raamatud teemal:
Get up to speed on the nuances of NoSQL databases and what they mean for your organization This easy to read guide to NoSQL databases provides the type of no-nonsense overview and analysis that you need to learn, including what NoSQL is and which database is right for you. Featuring specific evaluation criteria for NoSQL databases, along with a look into the pros and cons of the most popular options, NoSQL For Dummies provides the fastest and easiest way to dive into the details of this incredible technology. You'll gain an understanding of how to use NoSQL databases for mission-critical enterprise architectures and projects, and real-world examples reinforce the primary points to create an action-oriented resource for IT pros.

If you're planning a big data project or platform, you probably already know you need to select a NoSQL database to complete your architecture. But with options flooding the market and updates and add-ons coming at a rapid pace, determining what you require now, and in the future, can be a tall task. This is where NoSQL For Dummies comes in!





Learn the basic tenets of NoSQL databases and why they have come to the forefront as data has outpaced the capabilities of relational databases Discover major players among NoSQL databases, including Cassandra, MongoDB, MarkLogic, Neo4J, and others Get an in-depth look at the benefits and disadvantages of the wide variety of NoSQL database options Explore the needs of your organization as they relate to the capabilities of specific NoSQL databases

Big data and Hadoop get all the attention, but when it comes down to it, NoSQL databases are the engines that power many big data analytics initiatives. With NoSQL For Dummies, you'll go beyond relational databases to ramp up your enterprise's data architecture in no time.
Introduction 1(4)
Foolish Assumptions
2(1)
Icons Used in This Book
3(1)
Beyond the Book
4(1)
Where to Go from Here
4(1)
Part I: Getting Started with NoSOL 5(90)
Chapter 1 Introducing NoSQL: The Big Picture
7(20)
A Brief History of NoSQL
8(3)
Amazon and Google papers
8(3)
What NoSQL means today
11(1)
Features of NoSQL
11(9)
Common features
12(5)
Not-so-common features
17(1)
Enterprise NoSQL
18(2)
Why You Should Care about NoSQL
20(7)
Recent trends in IT
20(1)
Problems with conventional approaches
21(5)
NoSQL benefits and precautions
26(1)
Chapter 2 NoSQL Database Design and Terminology
27(32)
Managing Different Data Types
28(11)
Columnar
30(2)
Key-value stores
32(1)
Triple and graph stores
33(3)
Document
36(1)
Search engines
37(1)
Hybrid NoSQL databases
38(1)
Available NoSQL products
38(1)
Describing NoSQL
39(3)
Applying Consistency Methods
42(9)
ACID
42(2)
BASE
44(1)
Choosing ACID or BASE?
45(1)
Availability approaches
45(3)
Developing applications on NoSQL
48(1)
Polyglot persistence
48(1)
Polyglot persistence explained
49(1)
The death of the RDBMS?
50(1)
Integrating Related Technologies
51(8)
Search engine techniques
52(1)
Business Intelligence, dashboarding, and reporting
53(1)
Batch processing with Hadoop Map/Reduce
54(1)
Hadoop HDFS
55(1)
Semantics
56(1)
Public cloud
57(2)
Chapter 3 Evaluating NoSQL
59(36)
The Technical Evaluation
59(13)
Which type of NoSQL is for you?
61(1)
Search features
61(4)
Scaling NoSQL
65(2)
Keeping data safe
67(2)
Visualizing NoSQL
69(2)
Extending your data layer
71(1)
The Business Evaluation
72(5)
Developing skills
73(1)
Getting value quickly
73(1)
Finding help
73(1)
Deciding on open-source versus commercial software
74(1)
Building versus buying
75(1)
Evaluating vendor capabilities
76(1)
Finding support worldwide
76(1)
Expanding to the cloud
77(1)
Getting Support
77(20)
Business or mission-critical features
78(1)
Vendor claims
78(1)
Enterprise system issues
79(1)
Security
80(8)
Preparing for failure
88(1)
Scaling up
89(3)
Acceptance testing
92(1)
Monitoring
92(3)
Part II: key-Value Stores 95(44)
Chapter 4 Common Features of Key-Value Stores
97(8)
Managing Availability
98(1)
Trading consistency
98(1)
Implementing ACID support
99(1)
Managing Keys
99(2)
Partitioning
100(1)
Accessing data on partitions
101(1)
Managing Data
101(4)
Data types in key-value stores
102(1)
Replicating data
103(1)
Versioning data
103(2)
Chapter 5 Key-Value Stores in the Enterprise
105(6)
Scaling
105(2)
Simple data model — fast retrieval
107(1)
In-memory caching
107(1)
Reducing Time to Value
107(4)
Using simple structures
108(1)
Complex structure handling
108(3)
Chapter 6 Key-Value Use Cases
111(6)
Managing User Information
111(3)
Delivering web advertisements
112(1)
Handling user sessions
112(1)
Supporting personalization
113(1)
High-Speed Data Caching
114(3)
Chapter 7 Key-Value Store Products
117(16)
High-Speed Key Access
118(3)
Caching data in memory
118(1)
Replicating data to slaves
118(1)
Data modeling in key-value stores
119(1)
Operating on data
120(1)
Evaluating Redis
120(1)
Taking Advantage of Flash
121(2)
Spending money for speed
121(1)
Context computing
121(1)
Evaluating Aerospike
122(1)
Using Pluggable Storage
123(2)
Changing storage engines
123(1)
Caching data in memory
124(1)
Evaluating Voldemort
124(1)
Separating Data Storage and Distribution
125(3)
Using Berkeley DB for single node storage
125(1)
Distributing data
126(1)
Evaluating Oracle NoSQL
126(2)
Handling Partitions
128(5)
Tolerating partitions
128(1)
Secondary indexing
129(1)
Evaluating Riak
130(3)
Chapter 8 Riak and Basho
133(6)
Choosing a Key-Value Store
133(3)
Ensuring skill availability
134(1)
Integrating with Hadoop Map/Reduce
134(1)
Using JSON
135(1)
Finding Riak Support (Basho)
136(5)
Enabling cloud service
136(1)
Handling disasters
137(1)
Evaluating Basho
137(2)
Part III: Bigtable Clones 139(60)
Chapter 9 Common Features of Bigtables
141(12)
Storing Data in Bigtables
142(1)
Using row keys
142(1)
Creating column families
142(1)
Using timestamps
143(1)
Handling binary values
143(1)
Working with Data
143(5)
Partitioning your database
144(1)
Clustering
145(1)
Denormalizing
146(2)
Managing Data
148(2)
Locking data
148(1)
Using tablets
148(1)
Configuring replication
149(1)
Improving Performance
150(3)
Compressing data
150(1)
Caching data
150(1)
Filtering data
151(2)
Chapter 10 Bigtable in the Enterprise
153(12)
Managing Multiple Data Centers
153(3)
Active-active clustering
154(1)
Managing time
154(2)
Reliability
156(2)
Being Google
156(1)
Ensuring availability
157(1)
Scalability
158(7)
Ingesting data in parallel
159(1)
In-memory caching
159(1)
Indexing
160(2)
Aggregating data
162(1)
Configuring dynamic clusters
163(2)
Chapter 11 Bigtable Use Cases
165(6)
Handling Sparse Data
165(3)
Using an RDBMS to store sparse data
166(1)
Using a Bigtable
167(1)
Analyzing Log Files
168(3)
Analyzing data in-flight
168(1)
Building data summaries
169(2)
Chapter 12 Bigtable Products
171(22)
Managing Tabular Big Data
172(7)
Designing a row key
172(4)
Distributing data with HDFS
176(1)
Batch processing Bigtable data
177(2)
Assessing HBase
179(1)
Securing Your Data
179(5)
Cell-level security
180(3)
Assessing Accumulo
183(1)
High-Performing Bigtables
184(3)
Using a native Bigtable
184(1)
Indexing data
184(1)
Ensuring data consistency
185(1)
Assessing Hypertable
186(1)
Distributing Data Globally
187(6)
Substituting a key-value store
188(1)
Inserting data fast
189(1)
Replicating data globally
190(1)
Assessing Cassandra
190(3)
Chapter 13 Cassandra and DataStax
193(6)
Designing a Modern Bigtable
193(4)
Clustering
194(1)
Tuning consistency
194(1)
Analyzing data
195(1)
Searching data
195(1)
Securing Cassandra
196(1)
Finding Support for Cassandra
197(4)
Managing and monitoring Cassandra
197(1)
Active-active clustering
197(2)
Part IV: Document Databases 199(58)
Chapter 14 Common Features of Document Databases
201(12)
Using a Tree-Based Data Model
202(6)
Handling article documents
204(1)
Managing trades in financial services
204(1)
Discovering document structure
205(1)
Supporting unstructured documents
206(2)
Document Databases as Key-Value Stores
208(1)
Modeling values as documents
208(1)
Using value information
208(1)
Patching Documents
209(4)
Supporting partial updates
209(1)
Streaming changes
210(1)
Providing alternate structures in real time
211(2)
Chapter 15 Document Databases in the Enterprise
213(8)
Sharding
214(1)
Key-based sharding
214(1)
Automatic sharding
214(1)
Preventing Loss of Data
215(3)
Replicating data locally
216(1)
Using multiple datacenters
217(1)
Selectively replicating data
217(1)
Managing Consistency
218(3)
Using eventual consistency
219(1)
Using ACID consistency
219(2)
Chapter 16 Document Database Use Cases
221(12)
Publishing Content
221(4)
Managing content lifecycle
222(1)
Distributing content to sales channels
223(2)
Managing Unstructured Data Feeds
225(1)
Entity extraction and enrichment
225(1)
Managing Changing Data Structures
226(3)
Handling variety
227(1)
Managing change over time
228(1)
Consolidating Data
229(4)
Handling incoming streams
229(1)
Amalgamating related data
230(1)
Providing answers as documents
231(2)
Chapter 17 Document Database Products
233(18)
Providing a Memcache Replacement
233(3)
Ensuring high-speed reads
234(1)
Using in-memory document caching
234(1)
Supporting mobile synchronization
235(1)
Evaluating Couchbase
235(1)
Providing a Familiar Developer Experience
236(4)
Indexing all your data
236(1)
Using SQL
237(1)
Linking to your programming language
238(1)
Evaluating Microsoft DocumentDB
239(1)
Providing an End-to-End Document Platform
240(7)
Ensuring consistent fast reads and writes
241(1)
Supporting XML and JSON
242(1)
Using advanced content search
243(1)
Securing documents
244(2)
Evaluating MarkLogic Server
246(1)
Providing a Web Application Back End
247(4)
Trading consistency for speed
248(1)
Sticking with JavaScript and JSON
249(1)
Finding a web community
249(1)
Evaluating MongoDB
249(2)
Chapter 18 MongoDB
251(6)
Using an Open-Source Document Database
251(3)
Handling JSON documents
252(1)
Finding a language binding
252(1)
Effective indexing
253(1)
Finding Support for MongoDB
254(5)
MongoDB in the cloud
254(1)
Licensing advanced features
255(1)
Ensuring a sustainable partner
256(1)
Part V: Graph and Triple Stores 257(58)
Chapter 19 Common Features of Triple and Graph Stores
259(16)
Deciding on Graph or Triple Stores
260(4)
Triple queries
260(2)
Graph queries
262(1)
Describing relationships
263(1)
Making a decision
263(1)
Deciding on Triples or Quads
264(5)
Storing RDF
265(2)
Querying with SPARQL
267(1)
Using SPARQL 1.1
268(1)
Modifying a named graph
268(1)
Managing Triple Store Structures
269(6)
Describing your ontology
269(2)
Enhancing your vocabulary with SKOS
271(1)
Describing data provenance
272(3)
Chapter 20 Triple Stores in the Enterprise
275(8)
Ensuring Data Integrity
275(3)
Enabling ACID compliance
276(1)
Sharding and replication for high availability
277(1)
Replication for disaster recovery
278(1)
Storing Documents with Triples
278(5)
Describing documents
279(1)
Combining queries
280(3)
Chapter 21 Triple Store Use Cases
283(10)
Extracting Semantic Facts
284(2)
Extracting context with subjects
284(1)
Forward inferencing
285(1)
Tracking Provenance
286(1)
Building a Web of Facts
287(3)
Taking advantage of open data
287(1)
Incorporating data from GeoNames
288(1)
Incorporating data from DBpedia
289(1)
Linked open-data publishing
289(1)
Migrating RDBMS data
290(1)
Managing the Social Graph
290(3)
Storing social information
291(1)
Performing social graph queries
291(2)
Chapter 22 Triple Store Products
293(16)
Managing Documents and Triples
294(6)
Storing documents and relationships
294(2)
Combining documents in real time
296(1)
Combined search
297(1)
Evaluating ArangoDB
298(1)
Evaluating OrientDB
298(1)
Evaluating MarkLogic Server
299(1)
Scripting Graphs
300(4)
Automatic indexing
301(1)
Using the SPIN API
301(1)
JavaScript scripting
302(1)
Triple-level security
302(1)
Integrating with SoIr and MongoDB
303(1)
Evaluating AllegroGraph
304(1)
Using a Distributed Graph Store
304(5)
Adding metadata to relationships
305(1)
Optimizing for query speed
305(1)
Using custom graph languages
305(1)
Evaluating Neo4j
306(3)
Chapter 23 Neo4j and Neo Technologies
309(6)
Exploiting Neo4j
309(3)
Advanced path-finding algorithms
310(1)
Scaling up versus scaling out
310(1)
Complying with open standards
311(1)
Finding Support for Neo4j
312(5)
Clustering
313(1)
High-performance caching
313(1)
Cache-based sharding
314(1)
Finding local support
314(1)
Finding skills
314(1)
Part VI: Search Engines 315(44)
Chapter 24 Common Features of Search Engines
317(10)
Dissecting a Search Engine
318(5)
Search versus query
318(1)
Web crawlers
318(1)
Indexing
319(2)
Searching
321(2)
Indexing Data Stores
323(1)
Using common connectors
323(1)
Periodic indexing
324(1)
Alerting
324(3)
Using reverse queries
325(1)
Matchmaking queries
325(2)
Chapter 25 Search Engines in the Enterprise
327(8)
Searching the Enterprise
327(2)
Connecting to systems
328(1)
Ensuring data security
328(1)
Creating a Search Application
329(6)
Configuring user interfaces
329(1)
What a good search API gives you
330(1)
Going beyond basic search with analytics
331(4)
Chapter 26 Search Engine Use Cases
335(6)
Searching E-Commerce Products
335(3)
Amazon-type cataloguing
335(1)
Geospatial distance scoring
336(2)
Enterprise Data Searching
338(1)
Storing web data
338(1)
Searching corporate data
338(1)
Searching application data
339(1)
Alerting
339(2)
Enabling proactive working
339(1)
Finding bad guys
340(1)
Chapter 27 Types of Search Engines
341(12)
Using Common Open-Source Text Indexing
341(3)
Using Lucene
342(1)
Distributing Lucene
342(1)
Evaluating Lucene/SolrCloud
343(1)
Combining Document Stores and Search Engines
344(4)
Universal indexing
345(1)
Using range indexes
345(1)
Operating on in-memory data
346(1)
Retrieving fine-grained results
346(1)
Evaluating MarkLogic
347(1)
Evaluating Enterprise Search
348(1)
Using SharePoint search
348(1)
Integrating NoSQL and HP Autonomy
348(1)
Using IBM OmniFind
349(1)
Evaluating Google search appliance
349(1)
Storing and Searching JSON
349(4)
JSON universal indexing
350(1)
Scriptable querying
350(2)
Evaluating Elasticsearch
352(1)
Chapter 28 Elasticsearch
353(6)
Using the Elasticsearch Product
353(4)
ELK stack
354(1)
Using Elasticsearch
354(1)
Using Logstash
355(1)
Using Kibana
355(2)
Finding Support for Elasticsearch
357(4)
Evaluating Elasticsearch BV
357(2)
Part VII: Hybrid NoSQL Databases 359(40)
Chapter 29 Common Hybrid NoSQL Features
361(8)
The Death of Polyglot Persistence
362(1)
One product, many features
362(1)
Best-of-breed solution versus single product
363(1)
Advantages of a Hybrid Approach
363(6)
Single product means lower cost
364(1)
How search technology gives a better column store
365(1)
How semantic technology assists content discovery
366(3)
Chapter 30 Hybrid Databases in the Enterprise
369(6)
Selecting a Database by Functionality
369(2)
Ensuring functional depth and breadth
370(1)
Following a single product's roadmap
370(1)
Building Mission-Critical Applications
371(4)
Ensuring data safety
371(1)
Ensuring data is accessible
372(1)
Operating in high-security environments
373(2)
Chapter 31 Hybrid NoSQL Database Use Cases
375(6)
Digital Semantic Publishing
375(2)
Journalists and web publishing
376(1)
Changing legislation over time
377(1)
Metadata Catalogs
377(4)
Creating a single view
378(1)
Replacing legacy systems
378(1)
Exploring data
379(2)
Chapter 32 Hybrid NoSQL Database Products
381(8)
Managing Triples and Aggregates
381(2)
Generating triples from documents
382(1)
Enforcing schema on read
383(1)
Evaluating OrientDB
383(1)
Combining Documents and Triples with Enterprise Capabilities
383(6)
Combined database, search, and application services
384(1)
Schema free versus schema agnostic
385(1)
Providing Bigtable features
385(1)
Securing access to information
386(1)
Evaluating MarkLogic
387(2)
Chapter 33 MarkLogic
389(10)
Understanding MarkLogic Server
389(1)
Universal Indexing
390(6)
Range indexing and aggregate queries
391(1)
Combining content and semantic technologies
392(1)
Adding Hadoop support
393(1)
Replication on intermittent networks
394(1)
Ensuring data integrity
394(1)
Compartmentalizing information
395(1)
MarkLogic Corporation
396(3)
Finding trained developers
396(1)
Finding 24/7 support
397(1)
Using MarkLogic in the cloud
397(2)
Part VIII: The Part of Tens 399(20)
Chapter 34 Ten Advantages of NoSQL over RDBMS
401(6)
Chapter 35 Ten NoSQL Misconceptions
407(6)
Chapter 36 Ten Reasons Developers Love NoSQL
413(6)
Index 419
Adam Fowler is a principal sales engineer with MarkLogic, Inc. He has previously worked for IPK, FileNet, and IBM as well as smaller companies. Adam writes for and runs a popular blog on NoSQL and big data, which is republished on DZone.com. Hes a frequent speaker at NoSQL conferences.