A functional database by Trinder P. PDF

By Trinder P.

ISBN-10: 0902928619

ISBN-13: 9780902928619

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All this needs to happen at that very moment. While the Hadoop ecosystem provides you the platform to access and process the data, fundamentally it still remains a batch-oriented architecture. In this context, we encounter technologies used by Storm, in-memory appliances like Terracota, heavily indexed search patterns through Lucene and Solr. Problem Can we access data in near real-time from HDFS? ” The right tools need to be used to ingest, and at the same time data should be filtered/sorted in multiple storage destinations (as you saw in the multidestination pattern in an earlier chapter).

Solution Certainly. Because the type of data to be stored by an application has changed from being text to other unstructured formats, data can be persisted in multiple sources, like RDBMS, Content Management Systems (CMS), and Hadoop. As seen in Figure 4-16, for a single application and for various use cases, the storage mechanism changes from traditional RDBMS to a key-value store to a NoSQL database to a CMS system. This contrasts with the traditional view of storing all application data in one single storage mechanism.

This phenomenon—basically available soft state and eventually consistent—is the rule followed by the big data databases, which are generally NoSQL databases following BASE properties. Database theory suggests that any distributed NoSQL big database can satisfy only two properties predominantly and will have to relax standards on the third. The three properties are consistency, availability, and partition tolerance (CAP). This is the CAP theorem. The aforementioned paradigms of ACID, BASE, and CAP give rise to new big data storage patterns (Figure 4-1) like the following: • Façade pattern: HDFS serves as the intermittent façade for the traditional DW systems.

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A functional database by Trinder P.

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