AWS News Blog
Author: Donnie Prakoso
Donnie Prakoso is a software engineer, self-proclaimed barista, and Principal Developer Advocate at AWS. With more than 17 years of experience in the technology industry, from telecommunications, banking to startups. He is now focusing on helping the developers to understand varieties of technology to transform their ideas into execution. He loves coffee and any discussion of any topics from microservices to AI / ML.
Now Open — AWS Asia Pacific (Melbourne) Region in Australia
Following up on Jeff’s post on the announcement of the Melbourne Region, today I’m pleased to share the general availability of the AWS Asia Pacific (Melbourne) Region with three Availability Zones and API name ap-southeast-4. The AWS Asia Pacific (Melbourne) Region is the second infrastructure Region in Australia, in addition to the Asia Pacific (Sydney) […]
New — Create Point-to-Point Integrations Between Event Producers and Consumers with Amazon EventBridge Pipes
It is increasingly common to use multiple cloud services as building blocks to assemble a modern event-driven application. Using purpose-built services to accomplish a particular task ensures developers get the best capabilities for their use case. However, communication between services can be difficult if they use different technologies to communicate, meaning that you need to […]
New — Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler
To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment. The main objective of […]
New — Amazon SageMaker Data Wrangler Supports SaaS Applications as Data Sources
Data fuels machine learning. In machine learning, data preparation is the process of transforming raw data into a format that is suitable for further processing and analysis. The common process for data preparation starts with collecting data, then cleaning it, labeling it, and finally validating and visualizing it. Getting the data right with high quality […]
New — Amazon Athena for Apache Spark
When Jeff Barr first announced Amazon Athena in 2016, it changed my perspective on interacting with data. With Amazon Athena, I can interact with my data in just a few steps—starting from creating a table in Athena, loading data using connectors, and querying using the ANSI SQL standard. Over time, various industries, such as financial […]
New — Create and Share Operational Reports at Scale with Amazon QuickSight Paginated Reports
There are various ways to report on data insights, and paginated reports is one of them. Paginated reports are essential documents that contain critical business information for end-users. For decades, paginated reports have been the standard business reporting format. The following are examples of paginated reports. On the left shows the report for income statement […]
New — Fine-Grained Visual Embedding Powered by Amazon QuickSight
Today, we are announcing a new feature, Fine-Grained Visual Embedding Powered by Amazon QuickSight. With this feature, individual visualizations from Amazon QuickSight dashboards can now be embedded in high-traffic webpages and applications. Additionally, this feature enables you to provide rich insights for your end-users where they need them the most, without server or software setup […]
New — Detect and Resolve Issues Quickly with Log Anomaly Detection and Recommendations from Amazon DevOps Guru
Today, we are announcing a new feature, Log Anomaly Detection and Recommendations for Amazon DevOps Guru. With this feature, you can find anomalies throughout relevant logs within your app, and get targeted recommendations to resolve issues. Here’s a quick look at this feature: AWS launched DevOps Guru, a fully managed AIOps platform service, in December […]