Operations | Monitoring | ITSM | DevOps | Cloud

Cost management in Google Cloud Platform (GCP) can be complex. It’s a lot like costs in Azure or AWS. Often, companies don’t know where to begin when they receive 20-page invoices. GCP billing tools can be unfamiliar, especially to users from a different cloud provider. However, the platform continues to simplify its cost monitoring and billing tools.

TLDR: We raised $60M in new funding, led by Scale Venture Partners, with participation from World Innovation Labs, Cross Creek, Alpha Square Group, Patrick and John Collison (Stripe) IVP, Sequoia, and Y Combinator. With this new infusion of capital, we’re looking to further grow developer awareness around IDPs and invest in our platform capabilities with enhanced workflows, engineering intelligence, user interface, and of course, AI.

Creating a positive user experience is always one of the most important goals our teams focus on. Whether it’s administrators working behind the scenes on the platform, or users looking to uncover cost savings opportunities in Explorer, or engineers working with our APIs, we always strive to provide a frictionless experience making sure each person has the right information to achieve their goals in our platform.

In 2024, an interconnected network is a must-have. Here’s how to get one and supercharge your business operations. Globally distributed networks, remote workforces, multicloud architectures – all of these setups have something important in common (besides the fact they’re the norm now). They all leverage multiples to maximize business benefits. Multiple locations, multiple endpoint types, multiple cloud providers.

Data quality testing is a subset of data observability. It is the process of evaluating data to ensure it meets the necessary standards of accuracy, consistency, completeness, and reliability before it is used in business operations or analytics. This involves validating data against predefined rules and criteria, such as checking for duplicates, verifying data formats, ensuring data integrity across systems, and confirming that all required fields are populated.

Most load balancers only check a client certificate when the client first connects. However, this can be problematic if a client stays connected for an extended period of time. Staying connected would allow clients to continually send and receive data. Imagine you have an employee whose certificate and key were stolen by an adversary. If you are using TLS client authentication, that adversary can connect to your infrastructure and maintain illegal access.

While building our Service Level Objectives (SLO) product, our team at Datadog often needs to consider how error budget and burn rate work in practice. Although error budgets and burn rates are discussed in foundational sources such as Google’s Site Reliability Workbook, for many these terms remain ambiguous. Is an error budget a static quantity or a varying percentage? Does burn rate indicate how fast I’m spending a fixed quantity, or is it just another way to express error rate?