We’re quickly moving toward a world where business-relevant data analysis will support the fast moving, dynamic customer data from operational and social media interactions. Enterprises will want to analyze and act on this new breed of dynamic data in real time.
Such scale-out data management platforms need to handle large data volumes that change with high velocity and can run on commodity cloud or converged infrastructures. The options range from NoSQL and NewSQL to Hadoop. Each of these solves different data and workload challenges so the enterprise architect will need to be savvy in how to use these new building blocks successfully.
But one thing is clear – next-generation SQL databases are here to stay.
However, in-memory or scale-up databases alone will significantly drive up costs. Employing a scale-out database solution with a smart combination of RAM and flash delivers on these benefits with commodity economics.
In this slideshow, Clustrix, provider of a leading scale-out SQL database engineered for the cloud, will discuss five reasons why SQL, or rather, next-generation scale-out SQL, will make waves in the enterprise for years to come.
Click through for five reasons why SQL, or rather, next-generation scale-out SQL, will make waves in the enterprise for years to come, as identified by Clustrix.
Real-time analytics demand
The Big Data industry is shifting its focus from dealing with vast amounts of data to real-time analytics. Having access to up-to-date data is a key competitive advantage that promises real business benefits to companies of all sizes. Scale-out SQL databases that are able to perform real-time analytics on live operational data are critical enablers for mainstream adoption of this trend. Smart uses of in-memory methods together with flash storage promise high performance at commodity infrastructure prices.
Google and Facebook say so
After two years of FUD that “SQL doesn’t scale” by NoSQL evangelists, companies are finally realizing the benefits of SQL. Both Google and Facebook recently published research to show not only that SQL can scale, but that it is the best approach for certain workloads. For example, Google’s F1 SQL database for Adwords enables much simpler application development for very high concurrency OLTP and OLAP workloads, saving those pricy engineers for truly valuable work. And Facebook’s comments that relational databases are essential for analytics adds fuel to the hype of SQL add-ons now being promoted by every major Hadoop distribution.
Massive transaction volume
Scale-out SQL lets you scale linearly as you add nodes, even with highly concurrent workloads. It also means no code changes or replacing databases or hardware as your application needs to grow. Every node can receive and process transactions to scale linearly as cluster sizes grow.
Query processing
Scale-out SQL can move code to where the data is rather than pulling data to the query node. This approach minimizes data movement across the cluster. As the number of queries grows, data motion across the cluster is minimized, allowing the database to scale linearly. This also ensures that only one node is trying to write to any piece of data, reducing contention.
High availability in the cloud
Enterprises expect their services to always be available to run business-critical production applications. If the cloud fails, which often can happen, businesses need to be able to rely on a database that can recover from failures quickly and have disaster recovery features available. Scale-out SQL provides a simple and robust high availability that can keep multiple copies of each slice of the data available with no data loss.
