Businesses are flooded with constantly changing thresholds brought on by seasonality, special promotions and changes in consumer habits. Manual monitoring with static thresholds can’t account for events that do not occur in a regularly timed pattern. That’s why historical context of influencing events is critical in preventing false positives, wasted resources and disappointed customers.
As enterprises seek to accelerate the process of getting insights from their data, they face numerous sources of friction. Data sprawl across silos, diverse formats, the explosion of data volumes, and the fact that data is spread across many data centers and clouds and processed by many disparate tools, all act to slow the progress.
Developing against third-party or unfamiliar web APIs can be painful and slow compared to using native libraries. For example, making requests with an HTTP client is simple enough but offers no compile-time sanity checks and no way for code suggestion tools like Intellisense to help you navigate. Worst of all, if the API you are consuming introduces breaking changes, you won’t find out until runtime.
In this article, I'll walk you through the differences between self-hosted CI/CD and cloud-based CI/CD (like Bitrise!) and show you why it's time to move into the cloud.
If you’re reading this, chances are your organization is already aware of the additional management effort, devices, and testers required to ensure quality QA through software testing. You’re likely not doing it alone. Your in-house testers and external resources are probably scattered across different testing management platforms and software. But multiple platforms make collaboration more complicated than it needs to be.
In digital transformation projects, it’s easy to imagine the benefits of cloud, hybrid, artificial intelligence (AI), and machine learning (ML) models. The hard part is to turn aspiration into reality by creating an organization that is truly data-driven.
Fivetran dbt Packages accelerate analytics with off-the-shelf SQL for data modeling.
