Streaming analytics in AWS gives enterprises the ability to process and analyze log data in real time, enabling use cases that range from delivering personalized customer experiences to anomaly and fraud detection, application troubleshooting, and user behavior analysis. In the past, real-time log analytics solutions could process just a few thousand records per second and it would still take minutes or hours to process the data and get answers.

Build log grouping is a new feature that streamlines the build log process, making it easier to understand why a build failed and at which Step the failure occurred. Read how build logs are now grouped by steps, and how we improved our error message display.

This is the most fertile time for app development since the launch of the App Store 15 years ago. Our industry is in the grip of several simultaneous revolutions, each of them bending, flexing and moulding to the others. 5G promises to make our apps 10 times faster; wearable technology lets them wrap themselves around our bodies; artificial intelligence enables them to learn from us and get smarter every day. But this torrent of innovation brings challenges, too.

As developers, it’s easy to get fixated on the mobile sphere. We’re now spending 4-5 hours a day browsing apps on our phone (that’s over 1,800 hours a year), which means a huge volume of demand is channelling into Android and iOS projects. But desktop apps are booming too.

Since the 2006 launch of Amazon Web Services (AWS), the world’s first hyper-scale public cloud provider, thousands of data-driven businesses have shifted on-premise data storage and analytics workloads into the cloud by architecting or adopting a cloud data platform. As the volume, variety, and velocity of enterprise data continues to grow in 2023, cloud data platforms with legacy tech and complex architectures are becoming increasingly time-consuming and costly to manage.

There is a long history of clustering architectures with respect to building distributed databases for two primary reasons. The first is scalability. If a cluster of nodes has reached its capacity to perform work, adding additional nodes are introduced to handle the increased load. The second is availability. The ability to ensure that if a node fails, let’s say during ingestion and/or querying, remaining nodes would continue to execute due to state replication.

For the past 30 years, the primary data source for business intelligence (BI) and data visualization tools has generally been either a data warehouse or a data mart. But as enterprises today struggle to cope with the growing complexity, scale, and speed of data, it’s becoming clear that the data tools of 30 years ago weren’t designed to handle the enterprise data management challenges of today - especially with the growing variety and amounts of data that enterprises are generating.

If you use a data lake, chances are you need a way to keep your data searchable for business users. When combined with the analytics capabilities of a cloud data platform, a data catalog can solve some of the common pain points around “data swamps,” where users fail to gain any meaningful insights from their data. Some of a business’s most valuable assets lie within its data.

We all know that debugging and troubleshooting cloud-native environments is no walk in the park. Sometimes we forget that debugging the frontend portion of those applications is no simpler and comes with its own set of challenges. We also all know how hard it is to get logging just right: managing verbosity, volume, and usefulness to just the right level.