Load Balancing is the process of distributing network traffic among multiple server resources. The objective of load balancing is to optimize certain network operations. Ensuring that a workload is spread evenly among the computing resources, this “balanced load” improves application responsiveness and accommodates unexpected traffic spikes — all without compromising application performance. Let’s take a deeper look at this important networking function.

In the fast-paced world of software development, efficiency is everything. Developers are constantly under pressure to keep up with rapid technological changes, unclear requirements, and tight deadlines. With the demand for skilled developers rapidly increasing and projected to grow by 22% by 2029 in the US alone , finding ways to enhance productivity and streamline workflows can provide a much-needed relief, making the difference between delivering a project on time or spending long nights debugging.

In most situations, you will have several sources of telemetry data that you want to send to multiple destinations, such as storage locations and observability tools. In turn, the data that you are sending needs to be optimized for its specific destination. If your data contains Personally Identifying Information (PII) for example, this data will need to be redacted or encrypted before reaching its destination.

While building an app with FastAPI can be reasonably straightforward, deploying and operating it might be more challenging. The whole user experience can be ruined by unexpected errors, slow responses, or even worse — downtime. AppSignal is a great tool of choice for efficiently tracking your FastAPI app's performance. It allows you to easily monitor average/95th percentile/90th percentile response times, error rates, throughput, and much more. Useful charts are available out of the box.

Getting the right people working in the CoPE is crucial to success because these change agents must limber up the organization and promote the flexibility necessary to perform resilience. We’ll look for teammates who share enough in common to work well together, but who don’t necessarily perfectly overlap so that they can play off each other’s strengths.

The more customers I talk to, the more I see a trend toward wanting a low-cost vendor-agnostic data lake. Customers want the freedom to store their data long-term and typically look to object stores from AWS, Azure, and Google Cloud. To optimize for data access, users will partition their data into directories to optimize for use cases such as Cribl Replay and Cribl Search. Only relevant files will be accessed for rehydration or search by partitioning data.

To win the space race, aerospace and aviation companies must be fast. The end-to-end cycle of testing, visualizing test data, and making improvements demands swiftness, especially when a single launch yields billions of data points. It starts with real-time access to data. Real-time data analysis with nanosecond precision is crucial for monitoring environmental and habitat conditions when lives are at stake. Speeding up the iteration pipeline is essential but not sufficient. Cost efficiency matters too.

The internet is basically just a bunch of websites calling each other. You make a call to some service, that service calls you back, and then that service goes down and ruins your afternoon. Requests, our latest addition to Insights, is a place to see, understand, track, and improve the behavior of outgoing HTTP requests.

Azure Structured Query Language (SQL) has 18 different deployment options, service tiers, compute models, and two different pricing models: vCores and Database Transaction Units (DTU). Because of these complexities, it’s nearly impossible to project monthly budgets! This guide will explain the common Azure SQL pricing configurations and offer tips on optimizing your cloud budget.

Republicans and Democrats don’t agree on much, but when it comes to generative AI, they’ve managed to find 32 billion points of agreement. On May 15, a bipartisan group of senators unveiled a roadmap that would have the U.S. government spending $32 billion annually by 2026 to drive AI-related innovation. The U.S. isn’t alone; AI spending in India is growing at an annual average rate of 31.5%, and will exceed $5 billion by 2027.