NoSQL For Dummies

NoSQL For Dummies

Adam Fowler

Language: English

Pages: 456

ISBN: 1118905741

Format: PDF / Kindle (mobi) / ePub

NoSQL For Dummies

Adam Fowler

Language: English

Pages: 456

ISBN: 1118905741

Format: PDF / Kindle (mobi) / ePub


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.

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others with a few seconds delay. For highly interconnected systems where data is dependent on other data, or where complex relationships are formed, ACID compliance is a necessity. This is because of the unpredictable interdependency of all the subjects held in a triple store. Graph stores tend to guarantee ACID properties only on their primary master server. Because of the complex math involved, graph stores tend not to be sharded — that is, they don’t have part of their data residing on

document may undergo multiple versions, but it’s based on material in the police report’s source documents. Once the operation is completed, each officer needs to write a statement. A final file is created as evidence to submit to the prosecutor’s office. This file comprises summary information, all source intelligence, and the officers’ statements. In checking evidence, the prosecutors need to know who changed what, when, and how; with what software, and why? What was the reasoning behind an

forward operating bases with limited satellite communications, naval vessels, or even Special Forces soldiers with laptops in the middle of nowhere. Before these operators are detached, they want to search for and replicate useful information to their laptops or base portable servers. Replication configurations of this kind aren’t entire replicas of a whole data center like traditional replication is. Instead, it’s a subset of data specified by a query, a collection, or a directory of

works ACID is a four-letter acronym, as explained here: Atomicity: Each operation affects the specified data, and no other data, in the database. Consistency: Each operation moves the database from one consistent state to another. Isolation: One operation in-flight does not affect the others. Durability: The database will not lose your data once the transaction reports success. ACID transactional consistency can be provided various ways: In the locking model, you stop data from being read

tagging them as such in their metadata, or adding them to the same collection, makes sense. In this way, you can quickly look up all data related to the same topic. Some document databases, for example MarkLogic Server, support adding documents to multiple collections. Others just support one collection for a document. You can work around this issue by using a metadata property, like this JSON document with an internal _topic property: { “_id”: 1234, “_someInternalProperty”:”a value”,

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