APIs are the bread and butter of the internet. The ability to interact with our applications programmatically enables interoperability and makes our lives as developers easier. Unfortunately, web applications are vulnerable to malicious actors that seek to misuse them or degrade their performance, which is why rate limiting is an important part of any API.

Businesses that are best able to leverage data have a significant competitive advantage. This is especially true in financial services, an industry in which leading organizations are in constant competition to develop the most responsive, personalized customer experiences. Often, however, legacy infrastructure, data silos, and batch systems introduce significant technical hurdles.

Apps are the way to democratize AI: to make it accessible to everyone and streamline customers’ experiences with faster time to insights. According to a recent IDC survey, AI applications is currently the largest category of AI software, accounting for roughly one-half of the market’s overall revenue in 2023.

The rise of AI brings unprecedented opportunities, deeply rooted in the transformative power of data. As organizations strive for smarter, faster outcomes—whether accelerating product launches, streamlining supply chains, improving customer experiences, or enhancing workforce productivity—they must address five key needs for enterprise AI adoption: Move/ Transform - To effectively adopt AI, companies need to bring together and transform data of all types from all sources.

A data catalog is a collection of metadata that acts as a comprehensive inventory of an organization’s data assets. It provides an overview of all the data available, regardless of its location or technical details. This collection helps analysts and other data users find the needed data.

Market data analytics has always been a classic use case for Apache Kafka. However, new technologies have been developed since Kafka was born. Apache Flink has grown in popularity for stateful processing with low latency output. Streamlit, a popular open source component library and deployment platform, has emerged, providing a familiar Python framework for crafting powerful and interactive data visualizations. Acquired by Snowflake in 2022, Streamlit remains agnostic with respect to data sources.

Google regularly updates its developer policies to make sure Google Play is a safe and trustworthy platform for everyone. While this is unquestionably necessary and essential to protect users and their data, keeping up with the latest changes to ensure applications remain safe and compliant can feel a little overwhelming for developers.

Ably was proud to celebrate our 8th birthday this year, and we’ve enjoyed reflecting on some of our achievements since launching in 2016. When we launched, we believed that realtime interactions would underpin our everyday digital experiences, rather than just augment them. And so we set out on a mission to build the definitive realtime experience platform.

The life sciences industry is rapidly embracing artificial intelligence (AI) to automate complex processes, enhance efficiency, and improve outcomes. From drug development to patient care, AI-driven automation is transforming the landscape. Here are some of the top artificial intelligence use cases for process automation in the pharmaceutical industry.

AWS Lambda is a serverless compute service that enables you to run code without the need to provision or manage servers. This makes it an ideal choice for developers aiming to build scalable and efficient applications with minimal infrastructure overhead.