NoSQL For Dummies (2015)

Part VIII. The Part of Tens

In this part. . .

· Surpass RDBMS.

· Correct misunderstandings.

· Connect with the world.

· Visit www.dummies.com for great Dummies content online.

Chapter 34. Ten Advantages of NoSQL over RDBMS

In This Chapter

Saving development time

Increasing flexibility

Reducing cost

I’ve said throughout this book that NoSQL databases are not a direct replacement for an relational database management system (RDBMS). For many data problems, though, NoSQL is a better match than an RDBMS. I point out the most useful advantages in this chapter.

Less Need for ETL

NoSQL databases support storing data “as is.” Key-value stores give you the ability to store simple data structures, whereas document NoSQL databases provide you with the ability to handle a range of flat or nested structures.

Most of the data flying between systems does so as a message. Typically, the data takes one of these formats:

· A binary object to be passed through a set of layers

· An XML document

· A JSON document

Being able to handle these formats natively in a range of NoSQL databases lessens the amount of code you have to convert from the source data format to the format that needs storing. This is called extract, transform, and load (ETL).

Using this approach, you greatly reduce the amount of code required to start using a NoSQL database. Moreover, because you don’t have to pay for updates to this “plumbing” code, ongoing maintenance costs are significantly decreased.

Support for Unstructured Text

The vast majority of data in enterprise systems is unstructured. Many NoSQL databases can handle indexing of unstructured text either as a native feature (MarkLogic Server) or an integrated set of services including Solr or Elasticsearch.

Being able to manage unstructured text greatly increases information and can help organizations make better decisions. For example, advanced uses include support for multiple languages with facetted search, snippet functionality, and word-stemming support. Advanced features also include support for dictionaries and thesauri.

Furthermore, using search alert actions on data ingest, you can extract named entities from directories such as those listing people, places, and organizations, which allows text data to be better categorized, tagged, and searched.

Entity enrichment services such as SmartLogic, OpenCalais, NetOwl, and TEMIS Luxid that combine extracted information with other information provide a rich interleaved information web and enhance efficient analysis and use.

Ability to Handle Change over Time

Because of the schema agnostic nature of NoSQL databases, they’re very capable of managing change — you don’t have to rewrite ETL routines if the XML message structure between systems changes.

Some NoSQL databases take this a step further and provide a universal index for the structure, values, and text found in information. Microsoft DocumentDB and MarkLogic Server both provide this capability.

If a document structure changes, these indexes allow organizations to use the information immediately, rather than having to wait for several months before you can test and rewrite systems.

No Reliance on SQL Magic

Structured Query Language (SQL) is the predominant language used to query relational database management systems. Being able to structure queries so that they perform well has over the years become a thorny art. Complex multi-table joins are not easy to write from memory.

Although several NoSQL databases support SQL access, they do so for compatibility with existing applications such as business intelligence (BI) tools. NoSQL databases support their own access languages that can interpret the data being stored, rather than require a relational model within the underlying database.

This more developer-centric mentality to the design of databases and their access application programming interfaces (API) are the reason NoSQL databases have become very popular among application developers.

Application developers don’t need to know the inner workings and vagaries of databases before using them. NoSQL databases empower developers to work on what is required in the applications instead of trying to force relational databases to do what is required.

Ability to Scale Horizontally on Commodity Hardware

NoSQL databases handle partitioning (sharding) of a database across several servers. So, if your data storage requirements grow too much, you can continue to add inexpensive servers and connect them to your database cluster (horizontal scaling) making them work as a single data service.

Contrast this to the relational database world where you need to buy new, more powerful and thus more expensive hardware to scale up (vertical scaling). If you were to double the amount of data you store, you would easily quadruple the cost of the hardware you need.

Providing durability and high availability of a NoSQL database by using inexpensive hardware and storage is one of NoSQL’s major assets. Being able to do so while providing generous scalability for many uses also doesn’t hurt!

Breadth of Functionality

Most relational databases support the same features but in a slightly different way, so they are all similar.

NoSQL databases, in contrast, come in four core types: key-value, columnar, document, and triple stores. Within these types, you can find a database to suit your particular (and peculiar!) needs. With so much choice, you’re bound to find a NoSQL database that will solve your application woes.

Support for Multiple Data Structures

Many applications need simple object storage, whereas others require highly complex and interrelated structure storage. NoSQL databases provide support for a range of data structures.

· Simple binary values, lists, maps, and strings can be handled at high speed in key-value stores.

· Related information values can be grouped in column families within Bigtable clones.

· Highly complex parent-child hierarchal structures can be managed within document databases.

· A web of interrelated information can be described flexibly and related in triple and graph stores.

Vendor Choice

The NoSQL industry is awash with databases, though many have been around for less than ten years. For example, IBM, Microsoft, and Oracle only recently dipped their toes into this market. Consequently, many vendors are targeting particular audiences with their own brew of innovation.

Open-source variants are available for most NoSQL databases, which enables companies to explore and start using NoSQL databases at minimal risk. These companies can then take their new methods to a production platform by using enterprise offerings.

No Legacy Code

Because they are so new, NoSQL databases don’t have legacy code, which means they don’t need to provide support for old hardware platforms or keep strange and infrequently used functionality updated.

NoSQL databases enjoy a quick pace in terms of development and maturation. New features are released all the time, and new and existing features are updated frequently (so NoSQL vendors don’t need to maintain a very large code base). In fact, new major releases occur annually rather than every three to five years.

Executing Code Next to the Data

NoSQL databases were created in the era of Hadoop. Hadoop’s highly distributed file-system (HDFS) and batch-processing environment (Map/Reduce) signaled changes in the way data is stored, queried, and processed.

Queries and processing work now pass to several servers, which provides high levels of parallelization for both ingest and query workloads. Being able to calculate aggregations next to the data has also become the norm.

You no longer need a separate data warehouse system that is updated overnight. With fast aggregations and query handling, analysis is passed to the database for execution next to the data, which means you don’t have to ship a lot of data around a network to achieve locally combined analysis.