Operations | Monitoring | ITSM | DevOps | Cloud

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?

Users should maintain data consistency to ensure accurate database testing and analysis, especially when generating data in dependent columns. For example, they can use formulas, data generators, or post-scripts to fill in columns based on other columns in the table. However, post-scripts may be complicated and affect database performance. In the article, we’ll explore how to generate values for the referenced columns using dbForge Data Generator for SQL Server.

Manual updating of Salesforce records is far from optimal. If you have ever worked with this CRM, you should know it requires you to find the exact record you want to edit, and only then you’ll be able to change data in its fields. This is fine when you work with separate records, but what if you need to mass update records in Salesforce? If done manually, such an update might eventually lead to errors and consume time.

Although GoMock is one of the most popular ways to create mocks in Golang, it’s important to understand the broader landscape of mocking methods in the language to fully leverage its potential value in testing. Mocking is a powerful technique that allows developers to isolate their code, improve test reliability, and ultimately enhance overall code quality.

In today’s fast-paced software development landscape, efficiency and collaboration are paramount. As teams strive to deliver high-quality software faster, one tool that has emerged as a game-changer is the preview environment. In this blog post, we'll dive into what a preview environment is, why it's essential, and how it can revolutionize your development workflow.

Static code analysis is an important part of testing your software to ensure it is release-ready. In contrast to dynamic testing, which involves executing your code to find errors, static analysis uses automated tools to “look” through the code, without executing it, to find potential errors (including potential security issues) and bugs. Since the code does not need to be executed, static testing can begin much earlier in development than dynamic testing.