Video: Challenges of API Testing
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.
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.
See how the Logit.io platform helped give Youredi a more streamlined reporting and data visualisation alternative to using Microsoft’s Power BI in our latest customer case study. Outside of its BI capabilities, the Logit.io platform is used throughout Youredi by everyone from their technical teams through to their customer support and professional services department.
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.