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Preview environments are temporary environments where developers can test code changes before deploying them to production, also called ephemeral environments, they’re temporary and should be discarded after testing changes. Carrying out tests using accurate data is a major challenge when creating and destroying environments. Put differently, you need realistic data and traffic in the preview environment to reflect the performance of code changes in production.

Authentication is a crucial part of your application. Failing to handle your authentication flow with caution can create a lot of problems for your business. In this era of rising security risks on popular websites and apps, it is very important to add multifactor authentication to your application. Multifactor authentication adds another security layer to your app, helping users to retain their accounts even if their password is compromised.

The practice of DevOps — development operations — has taken organizations by storm. According to a 2021 report by Redgate Software, 74 percent of enterprises surveyed say they now use DevOps in some form or fashion, compared with just 47 percent in 2016. DevOps practitioners seek to improve the software development lifecycle by fostering closer collaboration between developers and IT operations teams.

A Kubernetes preview environment is an isolated environment that allows developers to test their code at any time without worrying about how others may be affected. While implementations and use cases may vary, simulating a production environment as closely as possible is the main goal. Imagine you’re part of a team developing a complex API, and you’ve been tasked with adding a new endpoint that relies on features within the codebase currently being optimized by one of your team members.

Datastream, a serverless database replication service, has integrated with Terraform to enhance app modernization, data analytics and ML pipelines.

By Jason Bloomberg, President, Intellyx Part 2 of the Demystifying Data Observability Series for Unravel Data In part one of this series, fellow Intellyx analyst Jason English explained the differences between DevOps and DataOps, drilling down into the importance of DataOps observability. The question he left open for this article: how did we get here? How did DevOps evolve to what it is today, and what parallels or differences can we find in the growth of DataOps?

A mock server can prove useful in many circumstances. Imagine you’re an engineer working on optimizing a feature inside of an existing API that relies on multiple other microservices to function properly. To fully test the optimizations, you’ll have to set up test versions of all the dependencies, which quickly proves to be quite a task in and of itself. This is where a mocks—a server that simulates the behavior of a real server—can be very beneficial.

We see a lot of teams working with automated testing, and many feel the need to take the next step in order to optimize and excel in their product release cycle. Prior to implementing DevOps, companies can experience a stall in releases and only make new ones every 6 months. This means that development efforts are not bringing value to users quickly enough. For companies that work in regulated industries, it’s also critical that they set up reporting and traceability into their DevOps pipeline.

In this third and final part, you’ll get to see a few examples that aren’t strictly related to configuration changes or new tools, but rather ways that traffic replay can help streamline the development process for those deploying in Kubernetes.