For most of my software engineering career, my experience with DevOps was all about configuration management. But after many years of experience with some key mentors, I began to see DevOps as so much more. In this article, I’ll summarize my DevOps evolution from a high level. If you’d like to go further in-depth, check out the recording from my Destination: Automation presentation below.
The global increase of realtime data is spurred on by the steady rise of user demand for realtime digital experiences - from virtual events, news, and financial information to IoT devices. The Ably platform has been providing realtime infrastructure for over 5 years now , and we’ve seen, and helped, the demand increase first-hand. A few years ago, our customers only had realtime requirements for one or two specific features, usually around firing updates and notifications.
Excellent developers are the lifeblood of software companies. Therefore, organizations and HR teams must think hard about the values that make an individual the best fit for their teams. Different organizations follow different practices and working styles, and have unique preferences regarding a good candidate.
Cloud native API Testing comes with a lot of challenges. In this video see how to overcome these challenges with the novel approach of traffic replay.
In the most recent season of BigQuery Spotlight, we discussed key concepts like the BigQuery Resource hierarchy, query processing, and the reservation model. This blog focuses on extending those concepts to operationalize workload management for various scenarios.
So far in this series, we’ve been focused on generic concepts and console-based workflows. However, when you’re working with huge amounts of data or surfacing information to lots of different stakeholders, leveraging BigQuery programmatically becomes essential. In today’s post, we’re going to take a tour of BigQuery’s API landscape - so you can better understand what each API does and what types of workflows you can automate with it.
If the COVID-19 pandemic has taught us anything, it is that speed and intelligence are of the essence when it comes to making business decisions. Organizations must find ways of keeping ahead of competitors and disruptions by continually leveraging data to make smart decisions. The problem? Data may be everywhere, but it’s not always available in a form that businesses can use to generate analytics in real time.
Look around and you’ll see the benefits of hyperautomation everywhere as it scales business processes faster than a speeding algorithm, takes the friction out of customer engagement, and drives business transformation to the moon and back. In the long run, this trend will likely create a new generation of jobs. But in the short term, it may also raise anxiety among workers fearing job loss amid the post-COVID hyperautomation boom. To put this anxiety into perspective.
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.
