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MariaDB 2016 Predictions: What’s new for the database in 2016?

Virtualization and Cloud executives share their predictions for 2016.  Read them in this 8th Annual series exclusive.

Contributed by Roger Levy, VP of Product, MariaDB

What’s new for the database in 2016?

Some say ‘data is the new oil', but while oil companies have a handle on the processes for turning oil into gasoline, the same can't be said for organizations trying to corral their data. How best to store and manage data is on the minds of most CIOs as they head into the New Year. It's exciting to see that databases, which underlie every app and enterprise on the planet, are now back in the spotlight. So what's new?

Securing your data at multiple layers

2015 saw every type of organization, from global retailers to the Catholic Church, experience financial losses and reputation damage from data breaches. Security has long been a concern of CIOs, but the growing frequency of high-profile attacks and new regulations make data protection a critical 2016 priority for businesses, governments, and non-profit organizations.

The days of relying on a firewall to protect your data are long gone. Amidst a myriad of threats, a robust security regimen requires multiple levels of protection including network access, firewalls, disk-level encryption, identity management, anti-phishing education, and so forth. Ultimately, hackers want access to the contents of an enterprise's database, so securing the database itself must be a core component of every organization's IT strategy.  

Prudent software development teams will use database technology with native encryption to protect data as it resides in the database, and SSL encryption to protect data as it moves between applications. They also will control access to the database with passwords and user validation, and a variety of access authorization levels based on a user's role. Of course organizations can't kick back and rely on software alone; they still have to hold themselves accountable via regular audits and testing.  

Multi-model databases

The variety, velocity and volume of data is exploding.  Every minute we send over 200 million emails and over 300 thousand tweets. Already by 2013, 90% of the world's data had been created in two years. But size is not everything. Not only have the volume and velocity of data increased, there is also an increasing variety of formats of data that organizations are collecting, storing and processing.

While different data models have different needs in terms of insert and read rates, query rates and data set size, companies are getting tired of the complexity in juggling different databases. Next year will kick off an increased trend toward data platforms which offer "polyglot persistence" - the ability to handle multiple data models within a single database. The demand for multi-model databases is exploding as Structured Query Language (SQL) relational data from existing applications and connected devices must be processed along-side JavaScript Object Notation (JSON) documents, unstructured data, graph data, geospatial and other forms of data generated in social media, customer interactions, and the many applications using text and voice recognition.

Growth in applying machine learning

With the rapid growth in the type and volume of data being created and collected comes the opportunity for enterprises to mine that data for valuable information and insights into their business and customers. As IT recruiters know well, more and more companies are employing specialist "data scientists" to introduce and implement machine learning technologies. But the number of experts in this field simply isn't growing fast enough, and this rarity makes hiring a data scientist cost-prohibitive for most companies. In fact, the US alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data, according to McKinsey & Company. In response, organizations are turning to machine learning tools that enable all of their employees to derive insights without needing to rely on specialists. Just as crucial as collecting the data is the need to provide analytical processing of large data sets in order to understand what lies in a company's database and how it can be turned into valuable insights.

Recently the major public cloud vendors have introduced a variety of offerings to provide machine learning services. These include offers such as Azure ML Studio from Microsoft, the Google Prediction API, Amazon Machine Learning and IBM's Watson Analytics. While these services do not fully eliminate the need for machine learning expertise, they are a step in the right direction to making machine learning more widely accessible. Organizations will also need database management systems that can integrate and work with machine learning systems smoothly for fast performance. We'll see more companies adopt these types of solutions to fill remaining gaps next year and to do more with their data than just store it.

Hybrid cloud and on-premise application deployment

With the recent revenue announcements by public cloud providers such as Amazon AWS and Microsoft Azure, it is clear that adoption of public cloud services is becoming mainstream. But they may never fully replace on-premise data storage.  While the cloud offers greater scalability and flexibility, better business continuity, disaster recovery, and capital cost savings, most organizations won't move all their tech workloads to the public cloud for economic and security reasons. As a result, companies will optimize a mix of public and private cloud and traditional on-premise data management solutions, though operating across multiple environments will present challenges.

Enterprises are gradually learning when and how to leverage the public cloud and when it makes more sense to keep data in their own data centers or use a hybrid approach. In 2016, we expect to see a greater focus placed on creating solutions to improve the migration to hybrid cloud architectures. Examples include cloud bursting from private clouds to public clouds when demand spikes too high, and the use of hybrid cloud for disaster recovery by replicating databases in the cloud as backups. Furthermore, as more countries put different data privacy laws in effect, databases will need to be in different cloud and on-premise deployments depending on the region.

A database renaissance?

With the recent rise of the Chief Data Officer, the widespread adoption of new database technologies, and the acute need for better IT security, the database is back in the spotlight. One of the most foundational technologies is once again one of the hottest. IT personnel should pay close attention to the innovations 2016 will bring.


About the Author

Roger Levy brings extensive international, engineering and business leadership experience to his role as VP, Products, at MariaDB. He has a proven track record of growing businesses, transforming organizations and fostering innovation in the areas of data networking, security, enterprise software, cloud computing and mobile communications solutions, resulting in on-time, high-quality and cost-effective products and services. Previous roles include VP and GM of HP Public Cloud at Hewlett-Packard, SVP of Products at Engine Yard, as well as founding R.P. Levy Consulting LLC.


Published Tuesday, January 05, 2016 11:40 AM by David Marshall
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