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 […]
0 CommentsData 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 […]
0 CommentsGathering insights from data is a more effective process if that data isn’t fragmented across multiple systems and data stores, whether on premises or in the cloud. Amazon AppFlow provides bidirectional data integration between on-premises systems and applications, SaaS applications, and AWS services. It helps customers break down data silos using a low- or no-code, […]
0 CommentsWe have given you the flexibility and ability to run the largest and most complex high performance computing (HPC) workloads with Amazon Elastic Compute Cloud (Amazon EC2) instances that feature enhanced networking like C5n, C6gn, R5n, M5n, and our recently launched HPC instances Hpc6a. We heard feedback from customers asking us to deliver more options to support […]
0 CommentsTo identify potential security threats and vulnerabilities, customers should enable logging across their various resources and centralize these logs for easy access and use within analytics tools. Some of these data sources include logs from on-premises infrastructure, firewalls, and endpoint security solutions, and when utilizing the cloud, services such as Amazon Route 53, AWS CloudTrail, […]
0 CommentsApache Spark is an open-source, distributed processing system commonly used for big data workloads. Spark application developers working in Amazon EMR, Amazon SageMaker, and AWS Glue often use third-party Apache Spark connectors that allow them to read and write the data with Amazon Redshift. These third-party connectors are not regularly maintained, supported, or tested with […]
0 CommentsMost AWS analytics services have compelling serverless offerings that make it even easier for customers to analyze vast amounts of data without having to configure, scale, or manage the underlying infrastructure. Along with other serverless analytics, such as Amazon QuickSight for business intelligence and AWS Glue for data integration, we have introduced Amazon EMR Serverless, […]
0 CommentsOur customers tell me that they love AWS Lambda for many reasons. On the development side they appreciate the simple programming model and ease with which their functions can make use of other AWS services. On the operations side they benefit from the ability to build powerful applications that can respond quickly to changing usage […]
0 CommentsWe launched Amazon S3 Multi-Region Access Points to give you a global endpoint that spans S3 buckets in multiple AWS Regions. With S3 Multi-Region Access Points, you can build multi-region applications with the same simple architecture used in a single Region. This cool and powerful feature uses AWS Global Accelerator to monitor network congestion and […]
0 CommentsToday, we announce automated data discovery for Amazon Macie. This new capability allows you to gain visibility into where your sensitive data resides on Amazon Simple Storage Service (Amazon S3) at a fraction of the cost of running a full data inspection across all your S3 buckets. At AWS, security is our first priority. The […]
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