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Overcoming scale challenges with AWS & CloudFront - 5 key takeaways

The Ably service handles massive amounts of data throughput and concurrent connections for many customers while maintaining a highly reliable and available service, with a 5x9s uptime guarantee. Ably has no scale ceiling, and that’s challenging work (it’s one of the reasons I joined Ably). While the challenges we face in delivering our service are compelling, we sometimes face novel internet scale problems, such as breaching the limits of AWS services!

Ensuring Trust in Augmented Reality: Addressing the Cybersecurity Conundrum

As Augmented Reality (AR) becomes more prevalent, imagine entering a world where digital overlays seamlessly blend with your physical surroundings, enriching your reality with interactive information and immersive experiences. This is the promise of AR, a groundbreaking technology reshaping how we interact with the digital world.

Exposed: How Secure Are Your Embedded Analytics Really?

The ever-growing threat landscape of hackers, cyberattacks, and data breaches makes data security a top priority, especially when integrating analytics capabilities directly into customer-facing applications. To make informed decisions, it’s crucial to understand how embedded analytics platforms function from a security standpoint.

Logi Symphony Has You Prepared for New Cookie Restrictions

Embedding analytics into your application? New browser restrictions are on the way for Google Chrome that can block some third-party content that uses cookies from being accessible to users by default, in addition to restrictions in Safari and Firefox you may have already seen.

Choosing The Perfect Message Queue: Factors To Consider

Not long ago, I was handed a problem that’s no stranger to the world of programming: making asynchronous threads communicate effectively within the same process. Given the widespread nature of this issue, I expected to find an existing solution to resolve it. My search led me to the concept of message queue, which seemed promising for streamlining this communication challenge.

Serverless GPUs in Private Preview: L4, L40S, V100, and more

Today, we’re excited to share that Serverless GPUs are available for all your AI inference needs directly through the Koyeb platform! We're starting with GPU Instances designed to support AI inference workloads including both heavy generative AI models and lighter computer vision models. These GPUs provide up to 48GB of vRAM, 733 TFLOPS and 900GB/s of memory bandwidth to support large models including LLMs and text-to-image models.

Master Documenting Your APIs: Tips for Effective API Documentation

API (application programming interface) document works as a developer’s compass for navigating complex services. In this guide, we provide straightforward insights into crafting excellent API documentation. At the end of this article, you will know how to succeed as both creators and consumers of APIs through effective documentation.

Using Python for GET API Calls: A Step-by-Step Guide for Developers

Understanding how to make a GET request to an API using Python is an essential skill for developers. This article will guide you through the process, demonstrating how to use Python’s ‘requests’ library to fetch data, handle the full JSON object in response, and manage API errors efficiently. Step into the practical world of Python GET API calls without any detours.

What is Data Preprocessing? Definition, Importance, and Steps

Did you know data scientists spend around 60% of their time preprocessing data? Data preprocessing plays a critical role in enhancing the reliability and accuracy of analytics. This blog will discuss why data preprocessing is essential for making data suitable for comprehensive analysis.

Data Integrity vs. Data Quality: Here's How They Are Different

Data integrity refers to protecting data from anything that can harm or corrupt it, whereas data quality checks if the data is helpful for its intended purpose. Data quality is a subset of data integrity. One can have accurate, consistent, and error-free data, but it is only helpful once we have the supporting information for this data. Data integrity and quality are sometimes used interchangeably in data management, but they have different implications and distinct roles in enhancing data usability.