DevOps and continuous delivery allow businesses to deploy software far more frequently than in the past, increasing consistency, predictability, and ultimately, quality. With iterative development, the deltas between builds are much smaller, reducing the likelihood of catastrophic errors. Bugs are smaller and easier to fix — if caught in time. However, though rapid release cycles introduce smaller bugs, they produce them far more frequently, and bugs that evade detection can grow into serious problems.

While functional problems can often be detected through regular use, security vulnerabilities are harder to spot. In companies that deploy many times per day, traditional security procedures such as static scans can often take longer than the life of the build, and excessive human interaction can rob highly automated DevOps projects of the very agility they were designed to create. To deliver on its goals, IT must create protocols that model and address security concerns as code is deployed.

This report will help IT executives and development teams understand the new approaches to security required in a continuous deployment environment.

Key findings include:

• Today’s cloud architectures are much more complex and distributed than the architectures previously built on premises, so new approaches to security are required for managing the additional complexity.
• Since infrastructure as code allows virtual machines to be provisioned and de-provisioned within minutes, keeping track of security vulnerabilities without automation is impossible.
• Companies are deploying more frequently due to the adoption of continuous deployment, resulting in frequent changes to the underlying infrastructure. They must continually ensure that their environments are secure and compliant.
• Threats are becoming more sophisticated. The old model of performing annual assessments and security scans is no longer adequate for protecting today’s environments. Monitoring for compliance and security must be a continuous effort.

Thumbnail image courtesy: iStock/Thinkstock

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Big data is well-suited for cloud environments because of the cloud’s ability to expand and contract almost instantly – provisioning more compute resources when process requests are generated and de-provisioning those same resources when the work is done. Emerging managed big data services greatly increase time-to-value for big data initiatives because organizations don’t need to acquire the necessary talent for implementing NoSQL and big data technologies like Hadoop or for navigating complex cloud environments.

The trick to leveraging managed big data services is determining which projects are good candidates for outsourcing and then finding a service provider that can deliver immediate value while also reducing administrative risks and infrastructure costs. This report will highlight for CIOs, CTOs, directors and VPs of IT, data scientists, and data analysts the value proposition of managed big data services and provide a glimpse of where this space is heading in the next three to five years.

Key findings include:

• Managed big data services accelerate big data deployments so customers can focus on the analytics and extract value from their big data initiatives more quickly.
• Although NoSQL databases can handle big data’s increasing volume, velocity, and variety, skilled engineers to design and manage those databases are in short supply.
• Managed big data services often complement existing data warehouses or local database environments.
• Due to growth in mobile, clickstream, and internet of things (IoT) traffic, the big data market will experience significant growth over the next three to five years.

Thumbnail image courtesy of MicroWorks/Thinkstock.

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