What is NoSQL and what is a NoSQL database?

What is NoSQL and what is a NoSQL database?

JSON documents are used to store data in NoSQL databases rather than the traditional table-based structures of relational databases. NoSQL does not stand for “no SQL,” but rather “not only SQL.” Because of this, a NoSQL JSON database can be used to store and retrieve data with “no SQL.” You can get the best of both worlds by combining the malleability of JSON with the robustness of SQL. This is why NoSQL databases are developed to be adaptable, scalable, and quick to meet the data management needs of contemporary enterprises. 

These are the four most common flavors of NoSQL database:

• Document databases, like JSON databases, are designed primarily for storing data in the form of documents. One further usage for these kinds of systems is the storage of XML files
• Records in key-value stores are organized into collections with a common key that can be used to quickly locate the desired record. To some extent, key-value stores can be thought of as NoSQL analogs to relational databases, with just enough structure to provide similar benefits without sacrificing those advantages
• Wide-column databases are relational databases that use a tabular layout but permit significantly different naming and formatting of data across rows, even within the same table. Wide-column databases, like key-value stores, contain a basic structure but retain considerable flexibility
• The connections between data nodes in a graph database are described by means of graph structures. When dealing with unstructured or semi-structured data, graph databases come in handy for finding patterns

The Benefits of Utilizing NoSQL Database Systems Are Numerous

As a result, the customer experience has risen to prominence as the single most critical difference in the business world, ushering in a period of unprecedented transformation. There has been a massive increase in the number of digital interactions between businesses and their various constituents, including consumers, employees, partners, vendors, and even goods, as a result of this transformation. Interactions like this are made possible by the internet and other technologies of the modern period; at the center of this transformation are the cloud, mobile, social media, big data, and internet of things applications of any given business.

Can you explain the key distinctions between these programs and the traditional enterprise staples of enterprise resource planning (ERP), human resources (HR), and financial accounting? One or more of the following traits is common to most modern online, mobile, and IoT apps. There’s a need for them to:

It must be able to handle a large number of users at once (tens of thousands, if not millions), provide a seamless experience to users located all over the world, never go offline, and have a high uptime guarantee.

• Processing of semi- and unstructured data
• Ability to quickly adjust to new and changing requirements thanks to regular upgrades and additions

There is a new set of technical requirements needed to construct and maintain these highly interactive, huge applications. A real-time data management strategy that can handle unprecedented scale, speed, and data variability is required for the new corporate IT architecture. Since relational databases are inadequate for the tasks at hand, businesses are increasingly adopting NoSQL alternatives.

• Companies in the Fortune 2000 are increasingly turning to NoSQL databases to run their most important programs

The largest newspaper publisher in the United States, Gannett, uses NoSQL for its proprietary content management system, Presto. The largest retailer in Europe, Tesco, uses NoSQL for e-commerce, product catalog, and other applications. The largest hotel chain in the world, Marriott, uses NoSQL for its $38 billion annual reservation system. The largest technology company in the world, General Electric, uses NoSQL for its Predix platform to help manage the Industrial Internet.

• Inadequacies of Relational Databases and What to Do About Them

Before the advent of the web, the cloud, big data, mobile, and today’s massively interactive enterprise, relational DBMS (database management systems) were developed to manage data for business applications running on mainframe computers. These registries are optimized to function on a single server, the bigger of which the better. Upgrading the servers’ processors, memory, and storage was the only method to expand these databases to a higher workload.

The proliferation of websites and web-based programs led to the development of (non-relational) NoSQL database systems. In 2006, Google published the Big table research paper, and in 2007, Amazon published the Dynamo research paper. These databases were developed to fulfill the needs of modern businesses:

• The demand for rapid development, adaptability to requirements changes, and the removal of data transformation
• Maintain nimble growth

Companies need to innovate more quickly than ever before if they want to succeed in today’s experience-based digital economy. Developers are under more pressure than ever to meet the ever-increasing demand for new features and updates to existing programs and services, with most of the new activity centered on the creation of cutting-edge web, mobile, and IoT applications. Since these apps are always improving, it’s important to be quick and flexible. Unfortunately, due to their rigid data format, relational databases present a significant barrier to the adoption of agile development practices.

Accounting for Potential Future Needs

One of the cornerstones of agile development is the willingness to update the program in response to user feedback and new use cases. Because the data model in a relational database is determined by a static schema, this presents a challenge. Therefore, developers must alter the schema, or even worse, submit a “schema change” request to the database administrators, in order to make changes to the data model. This not only slows or halts development because it is a laborious, time-consuming procedure, but also because it affects other apps and services.

Contrarily, a NoSQL database provides complete support for agile development and does not statically specify how the data must be modeled. Instead of dictating how data should be structured as SQL databases do, NoSQL systems leave that decision up to the applications and services they support. In NoSQL, the data model is determined by the needs of the program. In software and online services, data is represented as objects.

Do Away With the Need to Transform Data

Data is typically modeled in applications and services as objects (such as Employee), multi-valued data as collections (such as Roles), and linked data as nested objects or collections (e.g., manager). However, rows and columns in relational databases model data differently, with related data stored in separate tables and multi-valued data stored in the same table.

When working with relational databases, it is necessary to “shred” and reassemble objects in order to read and write data. This “impedance mismatch” exists between objects and relationships. The alternative is to use object-relational mapping frameworks, which are inefficient at best and troublesome at worst, to change the data. Data in a NoSQL database is typically processed in its raw form.

What is the process behind NoSQL?

So how does NoSQL stack up? Allow us to examine this matter at length. The next NoSQL tutorial features a sample application for handling resumes. Specifically, it has a collection of positions, and an array of abilities, and interacts with resumes as an object (the user object). To avoid this, applications should “shred” the user object before submitting a résumé to a relational database.

What’s the Deal with SQL and Queries?

Despite what you may have heard, it is not more difficult to query NoSQL databases. New tools enable querying in document-oriented NoSQL databases much faster than before, and the databases’ intrinsic flexibility makes it possible to deal with both structured and unstructured data effectively.

N1QL (pronounced “nickel”) is a sophisticated query language that extends SQL to JSON, allowing developers to take advantage of the strengths of both languages. It was launched with Couchbase Server 4.0. It allows for the typical SELECT, FROM, and WHERE queries, as well as aggregation (GROUP BY), sorting (SORT BY), joins (LEFT OUTER and INNER), and querying of nested arrays and collections. Composite, partial, and covering indexes are just a few more ways to boost query performance.

Work On Any Size Scale

Web, mobile, and IoT application databases must be scalable to any size. Scaling a relational database like Oracle (through techniques like Oracle RAC, for instance) is conceivable, but it is usually difficult, costly, and unreliable. For instance, Oracle’s RAC technology for scaling out calls for many moving parts and introduces a potential single point of failure, putting availability at risk. In contrast, the distributed nature of a NoSQL database, complete with a scale-out architecture and no single point of failure, has significant practical benefits.

Adaptability to Fluctuating Workloads

The quantity of users and data that applications and services need to handle is always growing, from hundreds to thousands to millions of users and from gigabytes to terabytes of operational data. Concurrently, they need to scale effectively without sacrificing performance.

Any reads write, or storage needs to be easily scalable in the database.

For relational databases, which can only grow in size, this poses a dilemma (i.e., adding more processors, memory, and storage to a single physical server). This makes it difficult to scale effectively and on demand. Costs climb as businesses must purchase more powerful servers to handle the influx of customers and information. Furthermore, hardware updates to the database may necessitate taking it down, leading to disruptions in service.

However, a NoSQL database that is distributed uses commodity technology to scale out, or add additional resources by adding more servers. Businesses can scale more economically when they can (a) use only as much hardware as is necessary to handle the current workload, (b) make use of cheaper hardware and/or cloud infrastructure, and (c) scale on demand with minimal disruption.

Distributed NoSQL databases can scale both effectively and efficiently, and they are also simple to set up and expand. They were designed to handle managing and monitoring clusters of any size, as well as distributing reads, writes, and storage among all nodes.

The Capability of Global, Round-the-Clock Deployment

It’s becoming increasingly important to prioritize accessibility in light of the growing prevalence of web and mobile app-based client engagements. These really must be up and running all the time, every time, and can’t be down for even a second. Relational databases that are deployed to a single physical server or that rely on clustering with shared storage face difficulties in providing continuous availability. The database will be inaccessible if it is hosted on a single server and it crashes, or if it is hosted on a cluster of servers and the shared storage fails.

A distributed NoSQL database, in contrast to relational technology, splits data and distributes it to several database instances that do not share any of their resources. More so, data can be replicated to several replicas so that it is always accessible (intercluster replication). Replication in NoSQL databases is both built-in and automatic, unlike relational databases like Oracle, which need separate software for replication (e.g., Oracle Active Data Guard). Furthermore, automated failover guarantees that the database can continue to perform reads and writes by routing the requests to a different node in the event of a node failure.

It is more important to have a presence in several countries and/or regions as client interactions shift to the digital realm. Having a database spread over different data centers improves availability and disaster recovery, but it also boosts performance since all reads and writes may be processed by the data center that is geographically closest to the user.

The complexity of ensuring global availability for relational databases grows when additional add-ons are needed (for example, Oracle requires Oracle GoldenGate to move data between databases) or when replication between multiple data centers can only be used for failover because only one data center is active at a time. However, applications that rely on relational databases may notice a drop in speed or discover that their data centers are significantly out of sync if they use data replication.

A distributed NoSQL database has in-built replication between datacenters, therefore no further replication software is needed. Furthermore, some provide both unidirectional and bidirectional replication for full active-active deployments to various data centers, allowing the database to be deployed in multiple countries or regions and providing local data access to local applications and their users. This not only boosts performance but also allows for instant failover via hardware routers, so applications don’t have to wait for the database to detect a failure before failing over.

Conclusion

So, what exactly are NoSQL databases, and why are they significant now? Developers and operations teams are under increasing pressure to create and manage web, mobile, and IoT applications at a higher rate and scope as businesses transition to the Digital Economy made possible by the cloud, mobile, social media, and big data technologies. Today’s web, mobile, and IoT applications favor NoSQL’s high performance and flexibility as the go-to database technology.

NoSQL has been used by hundreds of Global 2000 companies and thousands of other businesses, both large and small. Many organizations have begun using NoSQL for what began as a cache, proof of concept, or modest application and have since expanded its use to include mission-critical systems.

With NoSQL, organizations have a higher chance of meeting the needs of the Digital Economy in terms of development speed, scalability, and the performance and availability their customers expect.

About Enteros

​Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.