Securing your data
Not really new, but now more important than ever. Security has long been a concern of Chief Information Officers, 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.
Every type of enterprise from global retailers to the Catholic Church have experienced financial losses and reputation damage from data breaches. For businesses, robust data security practices not only minimize legal and regulatory liability but can be an opportunity to differentiate and earn customer trust.
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 very organisation'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 will also control access to the database with passwords and user validation, and a variety of access authorization levels based on a user's role. No matter the measures in place, regular audits and extensive testing by a diverse community of users are required to keep a security protocol current and effective.
The variety, velocity and volume of data is exploding. Every minute we send over 200 million emails and 300 thousand tweets. By some estimates, 90% of the world's data was created in the last 2 years. But size is not everything. Not only has the volume and velocity increased but also the types of data we are collecting, storing and processing has grown.
Relational data from existing applications and connected devices must be processed along-side JSON documents, graph data, geospatial and other forms of data being generated in social media, customer interactions, and the many applications using text and voice recognition.
Many of these data models have different needs in terms of insert and read rates, query rates and data set size. While it is possible to have specialized solutions for each category, there will be an increased trend toward data platforms which can handle this "polyglot persistence". Rather than deploying...