Businesses are in a continual race against each other to optimize their processes and drive progress. Often, especially in our digital age, the ability to carve out a competitive advantage comes back to a company’s ability to process, control, and draw insight from data. Data-driven systems are now everywhere, with businesses that engage with data-driven practices having better financial outcomes and showing more innovation.
Yet, as the demands for data insight grow, so does the need to rapidly process, collect, store, and query data. While legacy systems facilitate these processes, they pail in comparison to new systems in terms of speed and efficiency. In our era of data-driven decision-making, businesses need to modernize data infrastructure to stay ahead.
One of the most recent leaders of data infrastructure modernization is the arrival and proliferation of distributed SQL databases. In this article, we’ll dive into this data architecture, demonstrating what it is and why businesses around the globe are rapidly incorporating them into their processes.
Let’s dive right in.
A distributed SQL database is a singular relational database that is deployed over a series of network clusters or servers. Typically, this horizontal scaling approach is mostly used within NoSQL databases, but it is used here to boost availability and scalability. Within a distributed SQL database, data is replicated across the server, and distributed to individual nodes.
While still retaining the ACID-compliant regulations that SQl databases often have, this system allows enterprises to scale their databases while boosting availability and support. Distributed SQL databases are a result of talking tactics from alternative environments and applying them to relational systems.
Especially as more people are beginning to use cloud-native digital environments, distributed SQL databases are becoming much more popular. For businesses that want to reduce their cost of ownership or find a more flexible way of scaling, distributed SQL databases are rapidly becoming a go-to solution.
Distributed SQL databases offer the same functionality as typical relational databases, like SQL API, which allows for data modeling and querying. Equally, all other relational database management systems are supported, allowing developers to create foreign keys, stored procedures, triggers, partial indexes, and more.
What makes distributed SQl databases different is that it also then pushes their indexes across several distinct nodes. Instead of centralizing all data on one server, it is pushed to many clusters, facilitating connection and access. By optimizing these nodes, developers are able to query data faster than before, leading to more effective interactions with data.
Modernization has been a buzzword for quite some time in the world of business. Always looking to optimize processes where possible, businesses search for ways of improving their data infrastructure to enable faster querying, rapid insights, and smooth integration with BI tools.
In the search for new modernization strategies, using distributed SQL databases has quickly become one of the most popular options for those looking to obtain the following benefits:
- High Availability
- Increased Scalability
- Simplified Data Management
- Improved Performance
Let’s break these down further.
When distributing across many nodes, the majority of distributed networks use a primary and duplicate node system. One primary node logs changes and then projects them to all replica nodes in order to create availability. Not only does this system create the grounds for scalability, but it also offers high availability.
If a primary node were to go down, the system automatically begins the process of selecting a new primary node from the replicas. This distributed network architecture ensures that businesses are always able to access the data they need to make informed decisions.
Distributed infrastructure allows for horizontal scaling, where engineers add more nodes to a system to distribute the workload across many different servers then. The additional elasticity businesses can achieve with horizontal scaling ensures that servers are highly efficient, even at peak times.
What’s more, distributed SQL databases also allow for limitless scaling. While vertical scaling often has hard upper limits, depending on cost and throughput capabilities, horizontal scalability is almost limitless. Depending on how much data a business or its application generates, this is a vital benefit that companies should take advantage of.
Especially when businesses are involved with real-time data analytics or client-facing analytics, the ability to provide a flawless service with a low lag time and high dependency is an unrivaled benefit. By using distributed infrastructures, businesses can obtain a high level of scalability while also maintaining the best practices that they’ve already engrained into their relational databases.
Distributed databases also offer a unified view of data, allowing users to access data despite their physical location. Due to still having SQL databases at their core, developers can use SQL commands – something they will be extremely familiar with.
This simplified method of data management is enhanced by the increased performance, scalability, and availability that allow businesses to unlock a new era of effective data-driven insights and querying.
Another major advantage distributed query systems offer is that they can use distributed query processing. This form of processing allows for databases to query across multiple nodes at once, improving the performance of querying and streamlining results.
By using distributed query processing, these databases are able to leverage the power of multiple nodes at the same time, decreasing the time it takes to perform a query. Alongside allowing businesses to do more queries in the same number of time, this allows for a more in-depth query to take place, pulling from a larger quantity of data without hindering the system.
Additionally, the distributed nature of this system allows for increased performance due to load balancing, as the servers can distribute work across them instead of overloading a singular one. With this in place, it’s unlikely to run into bottlenecks in the system.
Businesses that want to stay ahead of their competition should use every tool available to them. When it comes to optimizing data infrastructure, one of the most effective currently available systems is distributed SQL databases. While maintaining the plethora of benefits that SQL databases offer, the added level of distribution helps with scaling and availability.
For businesses that want to radically improve their data availability, query speed, and insight generation frameworks, then turning to distributed networks could be a major tactic for 2023. As this year continues to evolve, we’re likely to see even more people using distributed data infrastructure to optimize their legacy systems.