The Future of Database Technology.

Disruptive database technologies have mandated retailers to innovate to keep up with market trends and competition. While this may seem costly, it’s the only way businesses can stay relevant. Project managers are expecting a higher return and lower costs on technology projects.

However, every company needs real-time data to thrive in this race of enhanced user experience. It’s costly to invest your efforts and time in a single monolithic system. However, you can incorporate systems that allow your business to scale up and manage the database infrastructure. Technological trends are fluid and can occur at any time. Thus, it’s essential to keep up with data trends and understand their implications. Below are three trends that might shape up the future of database technology.

First let us consider about types of databases.

In the future, SQL databases may give way to more distributed models while NoSQL and Hadoop vie for the top spot.

SQL has had a hold on databases for years. The relational database model began to emerge in the 1970s and quickly gained traction. Its use cases are so well documented that forty years later, SQL is still the most used type of database. According to db-engines.com, the four of the top five most popular databases are all relational; the only NoSQL database to break through the top five is MongoDB, which recently overtook PostgreSQL’s fourth place slot. Some of the top sites out there use SQL to query their data, including Facebook and Airbnb.

SQL is powerful to use, and according to an infographic by Wired, it will still remain as one of the best tools to use well into the future. However, its structure may change slightly in order to deal with new types and sources of data, and distributed RDBM Systems will see a surge in popularity.

Relational Database:

It was invented by E.F. Codd in the year 1970 at IBM. It is a traditional database based on the tables through which data can be accessed and manipulated. The tables in the relational database cannot be empty i.e. there should be at least one data item in each row corresponding to a column. SQL or structured query language is used to interact with a relational database.

MySQL is an example for Relational database

Distributed Database:

A database which has parts of data stored in multiple locations and the processing is scattered among these locations and connected through a network. They may be all of the same type or multiple types of hardware and software over different locations, but their collective purpose is to serve the systems requests.

Oracle server is an example for Distributed database.

Cloud Database:

It is a virtual database which is hosted on a public or private cloud platform. Its major advantage over all other databases is it doesn’t require all the huge storage and server hardware as the service is hosted over a secure network. Other advantages include high scalability and availability.

Microsoft Azure is an example of a cloud database.

NoSQL Database:

The database which helps organizations analyze enormous amounts of unstructured data is known as NoSQL databases. The main purpose of its origin is that it overcomes the performance issues of a relational database. This kind of databases process information available on multiple virtual servers around the globe.

Hybrid Database:

A hybrid database is a database management system that is a balanced database management system which offers high performance data processing in main memory with the vast storage capabilities of physical disk.

A hybrid database has both in-memory database features and on-disk database features in a single unified engine. As a result, data can be stored and manipulated in main memory alone, on disk alone or a combination of both. The integrated combination of both database types allows for unrivaled flexibility and robust functionality.

Future Trends Of Database Technology.

Converged Database.

Convinced that we can “have it all” within a single database offering. For instance, there is no architectural reason why a database system should not be able to offer a consistency model that includes at one end strict multi-record ACID transactions and at the other end an eventual consistency style model.

An ideal database architecture would support multiple data models, languages, processing paradigms and storage formats within the one system. Application requirements that dictate a specific database feature should be resolved as configuration options or pluggable features within a single database management system, not as choices between disparate database architectures.

Disruptive Database Technologies.

Disruptive technologies emerge which create discontinuities that cannot be extrapolated and cannot always be fully anticipated. It’s possible that a disruptive new database technology is imminent, but it’s just as likely that the big changes in database technology that have occurred within the last decade represent as much change as we can easily accept. There are a few computing technology trends which extend beyond database architecture and which may impinge heavily on the databases of the future.

Universal Memory.

Since the dawn of digital databases, there has been a strong conflict between the economics of speed and the economics of storage. The medium that offers the greatest economies for storing large amounts of data (magnetic disk, tape) offers the slowest times and therefore the worst economics for throughput and latency. Conversely, the medium that offer the lowest latency and the highest throughput (memory, SSD) is the most expensive per unit of storage.

However, should a technology arise that simultaneously provides acceptable economics for mass storage and latency then we might see an almost immediate shift in database architectures. Such a universal memory would provide access speeds equivalent to RAM together with the durability, persistence and storage economics of disk.

Most technologists believe that it will be some years before such a disruptive storage technology arises though, given the heavy and continuing investment, it seems likely that we will eventually create a persistent, fast, and economical storage medium that can meet the needs of all database workloads. When this happens, many of the database architectures we see today will have lost a key part of their rationale. For instance, the difference between Spark and Hadoop would become minimal if persistent storage (aka. disk) was as fast as memory.