Monitoring vs observability – is there even a difference and is your monitoring system observable? Observability has gained a lot of popularity in recent years. Modern DevOps paradigms encourage building robust applications by incorporating automation, Infrastructure as Code, and agile development. To assess the health and “robustness” of IT systems, engineering teams typically use logs, metrics, and traces, which are used by various developer tools to facilitate observability.

In a relatively short amount of time, Kubernetes has evolved from an internal container orchestration tool at Google to the most important cloud-native technology across the world. Today’s enterprises are adopting Kubernetes to speed up the development process and build and deploy modern applications at scale, and they’re doing so at an incredible pace.

Data sources ingest data in different sizes and shapes across on-premises and in the cloud, including product data, historical customer behaviour data, and user data. Enterprise could store these data in data storage services like Azure Blob store, an on-premises SQL Server, Azure SQL Database, and many more. This blog will highlight how users can define pipelines to migrate the unstructured data from different data stores to structured data using the Azure ETL tool, Azure Data Factory.

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.

Here at CloudZero, we work with some of the top software-driven companies out there. Like us, they’re building their products on Amazon Web Services (AWS), along with whatever best-of-breed providers meet their needs. It’s no secret that in recent years, Snowflake has seen — well, some serious success. For many companies, including CloudZero, they're the data warehouse provider of choice — and an essential component of delivering their products.

Release management is a topic that leaders in DevOps teams should be concerned with as organizations move toward implementing systems of automated continuous deployment. The practice will make your organization more efficient, but how do you implement it? Modernizing your infrastructure for the cloud is essential to distributing trusted releases globally. Many enterprises choose AWS as their platform, and seek to use AWS edge services to distribute their production applications to nearby end-points.

Cloud environments are susceptible to security issues. A big contributor is misconfigured resources. Misconfigured S3 buckets are one example of a security risk that could expose your organization’s sensitive data to bad actors. Policies and regular enforcement of best practices are key to reducing this security risk. However, manually checking and enforcing security is time-consuming and can fall behind with all the demands a busy DevOps team faces every day.

Although it is no surprise that Gartner is forecasting that overall public cloud consumption will grow by 18%, hitting $304.9 billion in 2021, Gartner’s research also indicates that the vast majority of organizations overshoot their cloud infrastructure budgets due to a lack of cost optimization approaches.

In this article we explore the basics of monitoring Amazon Web Services (AWS) by feeding metrics to Grafana through Hosted Graphite’s agent and also through Hosted Graphite’s AWS add-on. This will allow us to monitor metrics from applications and servers hosted in AWS with clarity and depth. This article assumes you have created a Hosted Graphite account.

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.