DataOps is the art of progressing from data to value in seconds. For us, its all about making data operations as easy and fast as using the email.

Apache Kafka is a popular and powerful component of modern data platforms. But it's complicated. Complicated to run, complex to manage and crucially - it's near impossible to drive Kafka adoption from the command line across your organization. So here's your how-to for seeing it through to production (... and possibly fame and fortune). We cover key principles for Kafka observability and monitoring.

As teams adopt Agile for managing software development, tools like Jira Software provide the foundation by promoting collaboration, transparency, commitment and flexibility. When it comes to testing, agile values testing as an integrated part of the SDLC, and testers are encouraged to work alongside developers. So what does Agile Testing in Jira look like and how can we increase agility in our teams?

With over 100+ types of product risks, it's impossible that all of your scripted/pre-planned tests will cover all potential threats to your software. Adding exploratory testing to your testing strategy ensures your software is thoroughly tested and meets the highest standards of quality. Through exploratory testing, you design and execute tests as you go. This iteration covers a wider scope of functionality and ensures your system works properly.

In this new era, taking an outside-in approach to digital business ecosystems can help organizations harness their existing resources and relationships to drive new innovation and efficiency. Digital business ecosystems encompass a network of partners, developers and customers facilitated by modern, cloud-first technologies. Recently, Google Cloud, in partnership with Oxford Economics, surveyed 1,000 CIOs of large organizations (over $2 billion in revenue) to understand their digital business ecosystem strategies. This executive summary dives into the key findings from this research.

Many organizations struggle to share data internally across departments and externally with partners, vendors, suppliers, and customers. They use manual methods such as emailing spreadsheets or executing batch processes that require extracting, copying, moving, and reloading data. These methods are notorious for their lack of stability and security, and most importantly, for the fact that by the time data is ready for consumption, it has often become stale.

Data scientists require massive amounts of data to build and train machine learning models. In the age of AI, fast and accurate access to data has become an important competitive differentiator, yet data management is commonly recognized as the most time-consuming aspect of the process. This white paper will help you identify the data requirements driving today's data science and ML initiatives and explain how you can satisfy those requirements with a cloud data platform that supports industry-leading tools.

Continuous testing is the key to unlocking digital confidence. How does your organization stack up? Now in its second iteration, the Sauce Labs Continuous Testing Benchmark Report details the best practices critical to successful continuous testing, and track organizations' collective performance against those metrics. Based on real-customer data from the more than 3 billion tests run on the Sauce Labs platform, the 2020 Continuous Testing Benchmark is a community resource unlike any other.