NoSQL For Dummies
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.
Co-occurrence of different fields in the search results. How often a particular actor is shown in each genre of the search results, for example. This is an example of two-way co-occurrence. Some search engines support N-way co-occurrences, which is particularly useful for discovering patterns you didn’t know existed. Examples include products mentioned with other products or with medical conditions on Twitter. All of these calculations are performed at high speeds by accessing just the search
of triple stores that are optimized for queries of relationships, rather than just the individual assertions, or facts, themselves. Graph math is complex and specialized and may not be required in all situations where storing triples are required. Throughout this book, I point out where the difference matters. The query types supported also affect the design of a graph store, which I talk about in Chapter 19. If you need to store facts, dynamically changing relationships, or provenance
introduction to the field. This meetup included speakers from LinkedIn, Facebook, Powerset, Stumbleupon, ZVents, and couch.io who discussed Voldemort, Cassandra, Dynamite, HBase, Hypertable, and CouchDB, respectively. This meeting represented the first time that people came together to discuss these different approaches to nonrelational databases and to brand them as NoSQL. What NoSQL means today Today the NoSQL movement includes hundreds of NoSQL database products, which has led to a variety
easier to learn key-value semantics than it is to learn the Structured Query Language (SQL) of relational database systems. Integrating with Hadoop Map/Reduce Normally in a Hadoop Map/Reduce job, the Hadoop Distributed File System (HDFS) is the input source and output destination of an operation’s data. It’s possible, though, to use Riak as input, or output, or both. Using Riak as an input means that you can specify a set of keys, a secondary index query, or a Riak Search query to execute
recognize as replication. I call this disaster recovery (DR) replication to avoid confusion. All DR functionality works on the premise that you don’t want to block data writes on the primary site in order to keep the DR site up to date. So, DR replication is asynchronous. However, the changes are applied in order so that the database can replay the edit logs in the correct sequence. As a result, it’s possible to lose some data if the primary site goes down before the DR site is updated. This