Dependency matching is a powerful tool to tie the experience of a customer between two features. Combined with Split’s ability to ramp, combine different matchers, and negation, you will benefit from the capability to granularly understand and target your customer base, and to roll out features accordingly.
Feature flags are a necessary part of your Continuous Delivery (“CD”) pipeline. In fact, as your team grows, you can’t do CD without feature flags. This article provides best practices on when to use feature flags.
With Dropbox’s announcement of Stormcrow, they join an elite group of large tech companies handling controlled rollouts internally. But what do these systems have in common, and how can they be applied to any business using Split?
Feature branching relies heavily on a human process to validate releases, often causing backups that effect the rest of the business. These slowdowns can be avoided by using feature flags, allowing engineers to QA as they go, and QA teams to spend their time creating automation and verification software, rather than inspecting branches.
Our mission at Split is to give companies control over their users’ experience. Software is behind every modern customer interaction, yet today’s companies can neither deliver software fast enough nor adequately measure the impact of these new features on their users. Split changes that.
Our new integration with Rollbar brings Split event data into the error monitoring environment, so teams can investigate incidents faster and take action the moment things go wrong.
The Power of Controlled Rollouts: Software Development Lessons from the Samsung Galaxy Note 7 Recall
What can software engineers learn from Samsung’s Galaxy Note 7 debacle? Plenty. As we build and ship new products it’s important to protect customers from failure. Controlled rollouts give us a powerful tool to slowly introduce new features, thoroughly testing them as they go live.
When we think about app infrastructure planning, we often ask how will it scale. Equally important though, is how will it fail. You might not be able to 100% prevent failure, but you can mitigate its impact on your customers by building the the capacity for failure, or graceful degradation, into your app.
Anomaly detection in application monitoring systems (APM) is the gold standard. The idea is that if your APM can tell you when something is wrong, you can multiply the effectiveness of your site reliability team. Unnecessary alerts are never thrown and humans are not stuck watching dashboards.
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