Operations | Monitoring | ITSM | DevOps | Cloud

So you've set up a Google Cloud Logging sink along with a Dataflow pipeline and are happily ingesting these events into your Splunk infrastructure — great! But now what? How do you start to get meaningful insights from this data? In this blog post, I'll share eight useful signals hiding within Google Cloud audit logs that will help you uncover meaningful insights. You'll learn how to detect: Finally, we’ll wrap up with a simple dashboard that captures all these queries in one place.

In this handbook, we’ll explain the AWS Step Functions Input and Output manipulation. There’s plenty to talk about AWS Step Functions. There are numerous articles available online talking about AWS Step Functions ever since Step Functions were introduced in 2016. Most of these articles might make you think that Step Functions are actually an extension of the Lambda function, allowing you to combine several Lambda functions to call each other.

At Google Cloud, we strive to bring Site Reliability Engineering (SRE) culture to our customers not only through training on organizational best practices, but also with the tools you need to run successful cloud services. Part and parcel of that is comprehensive observability tooling—logging, monitoring, tracing, profiling and debugging—which can help you troubleshoot production issues faster, increase release velocity and improve service reliability.

Although AWS Lambda is a blessing from the infrastructure perspective, while using it, we still have to face perhaps the least-wanted part of software development: debugging. In order to fix issues, we need to know what is causing them. In AWS Lambda that can be a curse. But we have a solution that could save you dozens of hours of time. TL;DR: Dashbird offers a shortcut to everything presented in this article.

The Splunk Deep Learning Toolkit (DLTK) is a very powerful tool that allows you to offload compute resources to external container environments. Additionally, you can use GPU or SPARK environments. In last Splunk blog post, The Power of Deep Learning Analytics and GPU Acceleration, you can learn more about building a GPU-based environment. Splunk DLTK supports Docker as well as Kubernetes and OpenShift as container environments.

Azure IoT Edge is a Microsoft Azure service that allows you to run containerized workloads on IoT devices. With IoT Edge and Azure IoT Hub, Azure’s device-management platform, organizations across science, manufacturing, energy production, and other industries can provision their IoT devices and workloads at the edge of their cloud networks for immediate in-unit computing, a necessity when running AI algorithms or parsing large datasets directly on IoT devices.

The adoption of cloud-based solutions has become increasingly common. The proof for this is evident – according to Gartner, Inc., the worldwide public cloud services market is expected to grow by 6.3% in 2020, up to a staggering $257.9 billion in value. The Flexera 2020 State of the Cloud Report, released on April 28, 2020, states that more than 90% of respondents have adopted cloud computing, with the top three cloud service providers being – AWS, Azure, and Google Cloud Platform.