Systems | Development | Analytics | API | Testing

Distributed systems have unlocked high performance at a large scale and low latency. You can run your applications worldwide from the comfort of your Amazon Web Services (AWS) platform in California, but the user adding an item to their shopping cart in Japan will not notice any delay or system faults. However, distributed systems - and specifically distributed database systems - also malfunction.

In the modern age, everything runs on the cloud. The majority of modern applications are written with cloud technologies - they use public cloud providers for DNS, distributed caching, and distributed data stores. Cloud solutions are so popular among engineers because of their many advantages: But distributed systems are not impervious to breaking. Foursquare’s example is testimony that even the great and mighty experience failure within distributed systems.

Interested in transformations? Avoid these pitfalls and embrace these best practices to get the most out of data transformation.

North Star metrics can be an effective strategy for aligning all teams around one goal. Here’s how 20+ companies selected their North Stars.

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.