NoSQL and NewSQL Data Management Practices In Cloud Environments

Advancement in web technologies, database management, and the proliferation of portable devices like smartphones and sensor-enabled gadgets have resulted in huge data generation. This further mandated the development of databases with huge storage and immense processing capabilities. Cloud computing has emerged as a paradigm, which further promises to meet such requirements.

The traditional relational databases of SQL type were meant for a different era of data management needs, which are now facing challenges in meeting the Big Data era’s increased performance and scaling requirements. There are new types of databases in NoSQL and NewSQL, which come up as an alternative to SQL to handle huge data volumes.

The Big Data Database Challenges

Big Data is now a term used to represent a massive amount of data with complex data sets made of a variety of data structures in the form of fully structured, semi-structured, as well as unstructured data. As per a Gartner group study, Big Data is defined by three V’s as volume, velocity, and variety. Modern-day businesses are aware that these huge data volumes can be ideally used to bring up new opportunities and procedural improvements in terms of analysis and processing of data.

Simultaneously, cloud computing, artificial intelligence, and machine learning have also emerged by enabling on-demand access to networks over a shared pool of computing resources spread across the globe. Systems server, networks, storage, services, and applications can be rapidly provisioned on the cloud with minimal administrative effort.

Cloud computing is now associated with service provisioning, where the providers of such services offer remote services to the users over a high-speed network (usually the internet). These services often are based on the pay-per-use model, in which the consumers will pay only for what they use. The cloud computing model offers optimum benefits to the users in terms of very less investment up-front, highly reduced operating costs, anytime scalability, high flexibility, and easy access through the web. This approach will also help reduce maintenance expenses and business risks.

Due to these welcoming characteristics of cloud-based database management, many applications, including frontline enterprises, have now been moved on to cloud platforms. It is also interesting to note that the synergy between the processing needs of Big Data based applications and the availability and flexibility of the computational resources offered by cloud services are in sync. However, to effectively leverage the cloud infrastructure, you must carefully design and implement the applications and DBMS. Here are the primary advantages of taking a cloud-based approach. To analyze these in light of your specific business needs, you may contact the expert consultants like

  • High performance and scalability. Modern-day applications are experiencing continuous growth in the data volume and the users they may serve, which is the major catch of cloud-based services.
  • Cloud applications may be subjected to larger fluctuations in the access patterns, which demands the platform to be highly elastic.
  • Capability to run on heterogeneous commodity servers. Most of the cloud environments are now based on those.
  • Higher fault tolerance. Given the commodity, machines are more prone to fail than those high-end servers.
  • Privacy and security. As the data is not stored on a third-party premised or own premise server, it will be secured as the resources are shared across different tenants.
  • High availability. Availability is a crucial aspect of the performance of any application. Even the critical applications which need to ensure a high availability are now moving on to the cloud as these guarantee minimum downtime if not nil.

Cloud computing is a model of enabling a very convenient and ubiquitous on-demand network access for a shared pool of computing resources spread across the globe. These can be rapidly provisioned and released with only a minimal effort for managing the same. Cloud denotes a model where the computing infrastructure is viewed as a virtual cloud. Businesses and individuals can access the application and information on-demand from anywhere anytime. The advantages of cloud-database applications are:

  • Self-service, on-demand access to the provided services without any human interactions.
  • Broad network access enables heterogeneous thin, and thick client applications for accessing the services.
  • The pooling of computing resources by the service provider to serve many consumers at a time.
  • Rapid, elastic, and automatic provisioning of the computing and database resources.
  • Measured services in which resource usage is closely monitored and controlled.

Overall, the cloud computing model aims to offer benefits to the users in terms of a very less up-front cost on deployment and reduced operational cost. Along with these benefits, users can also enjoy higher scalability, flexibility, ease of access through web interfaces, and very reduced business risks and maintenance expenses.

CAP Theorem

To store, manage, and process massive data volumes, the most common approach is to partition the data and store it across various server nodes. These partitions can also be replicated across multiple servers to ensure data availability in case of any of the server cracks. In many modern data stores like BigTable and Cassandra, using these strategies to implement a highly scalable and available database can be leveraged in cloud-based environments.

‘Consistency’ of performance as the ‘C’ in CAP theorem denotes, is about having a single up-to-date data instance. So, consistency is somewhat dissimilar in meaning and represents one subset of the consistency, as we have seen in ACID transactions, i.e., Atomicity, Consistency, Isolation, and Durability of relational databases. ACID refers to the capability of the database maintenance in a consistent state at any given point. ‘Availability’ property in CAP means that the data must be made available to process a request instantly when needed. Finally, ‘Partition Tolerance’ in CAP refers to the shared-data system’s tolerance capacity in the case of network partitions.

So, the CAP theorem’s basic interpretation is the consideration of a distributed data store, which is partitioned into two participant nodes, which will remain consistent. This is the fundamental requirement for modern-day cloud-based big data applications.

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