Recently NoSQL has gained immense popularity.
What are the advantages of NoSQL over traditional RDBMS?
SQL has several very big advantages:
Mistake 6: Failure to Initiate Business Process Changes
Installing a well designed technical implementation of a real-time data warehouse will deliver no value. Technology, on its own, rarely does. It is improvement in the underlying business processes of an organization that drives the real value. The real-time data warehouse is simply a tool for delivering a technical capability for high-performance decision making against very up-to-date data. But it is what an enterprise does with this capability that really matters.
Buffer management: A main memory database system does not need to access pages through a buffer pool, eliminating a level of indirection on every record access.
Documents can be stored in non-relational databases, like CouchDB.
NOSQL has no special advantages over the relational database model. NOSQL does address certain limitations of current SQL DBMSs but it doesn't imply any fundamentally new capabilities over previous data models.
NOSQL means only no SQL (or "not only SQL") but that doesn't mean the same as no relational. A relational database in principle would make a very good NOSQL solution - it's just that none of the current set of NOSQL products uses the relational model.
The history seem to look like this:
The biggest advantage of NoSQL over RDBMS is scalability. NoSQL databases can easily scale-out to many nodes, but for RDBMS it is very hard. Scalability not only gives you more storage space, but also much higher performance since many hosts work at the same time.
Real-time data warehousing is clearly emerging as a new breed of decision support. Providing both tactical and strategic decision support from a single, consistent repository of information has compelling advantages. The result of such an implementation naturally encourages the alignment of strategy development with execution of the strategy. However, a radical re-thinking of existing data warehouse architectures will need to be undertaken in many cases. Evolution toward more strict service levels in the areas of data freshness, performance, and availability are critical. The pages that follow identify the 10 most commonly made mistakes when designing a real-time data warehouse and give advice to help you avoid these pitfalls.
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Those haven't gone away.
This does not mean that we have to use NoSQL over SQL.
Maybe reading will help.
If you need to process huge amount of data with high performance
C - Consistency A - Availability P - Partition toleranceK/V - Key/Value
If data model is not predetermined
Just because real time can be implemented, does not mean that it should be implemented in all cases. The idea of right-time data warehousing is that data freshness service levels are aligned with and driven by the needs of specific business processes. Aligning service levels for data acquisition with the business processes within an organization will result in more cost-effective implementations and, ultimately, better return on investment. For example, consider the requirements for an analytic application designed to support decisions related to exception handling for late flights in an airline data warehouse.
NoSQL database is a better choice.
Does data acquisition of late flight events really need to occur in a small number of seconds from the operational bookkeeping systems? Probably not. The airline will know at least 10 minutes prior to the flight landing time whether it will be late or not. Assuming that the analytic decisions related to gate assignment, holding connecting flights, re-accommodation, and so on can be accomplished within a reasonable amount of time (minutes), the need for immediate (small number of seconds) acquisition of the late flight event would be overkill. A more cost-effective capacity plan and implementation solution can be realized when the data freshness requirements are not overstated relative to the business requirements.
RDBMS focus more on relationship and NoSQL focus more on storage.
Latching: In a multi-threaded database, many data structures have to be latched before they can be accessed. Removing this feature and going to a single-threaded approach has a noticeable performance impact.
What Are Some Hadoop Related Thesis Topics? – Quora
On the other hand, a data warehousing solution designed to perform the analytics related to quality control on a high-speed assembly line may very well need data freshness measured in (near) “real” time. The immediate capture and analysis of test data from the assembly lines is essential for process control and quality management. Proactively detecting machine drift and taking corrective action before missed tolerances force shutdown of an assembly line for more drastic repairs can mean millions of dollars in savings.
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Focusing on “right-time,” using data freshness service level agreements (SLAs) based on understanding business process requirements, leads to far more effective implementations than technology-driven solutions striving for “real-time” solutions influenced by market hype. However, it is important not to make short-sighted decisions in the design of the data acquisition architecture for the data warehouse. A well designed architecture will allow for increasing data freshness SLAs as business requirements evolve. It is important to implement a scalable solution that can be adjusted upwards in capacity to support more aggressive data freshness according to the needs of maturing business processes. Re-writing or re-architecting a data acquisition infrastructure due to lack of foresight can be a significant drain on the ROI for a real-time data warehouse. On the other hand, over-engineering the initial implementation can be just as big a drain. The key is a scalable architecture that allows just the right amount of capacity to be deployed at each stage of evolution in deployment of an organization's real-time data warehousing capabilities (without code re-writes!).
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