Apache 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 various versions of Spark for production. Today we are announcing the general availability of Amazon Redshift integration for Apache Spark, which makes it easy to build and run Spark applications on Amazon Redshift and Redshift Serverless, enabling customers to open up the data warehouse for a broader set of AWS analytics and machine learning (ML) solutions. With Amazon Redshift integration for Apache Spark, you can…
Category: AWS
Reposts from Amazon Web Services (AWS).
Preview: Amazon OpenSearch Serverless – Run Search and Analytics Workloads without Managing Clusters
Most 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, Amazon MSK Serverless, and Amazon Redshift Serverless this year. Today, we announce the preview release of a new serverless option for Amazon OpenSearch Service that makes it easy for customers to run large-scale search and analytics workloads without managing clusters. It automatically provisions and scales the underlying resources to deliver fast data ingestion and query responses for even the most demanding and unpredictable workloads, eliminating…
New – Accelerate Your Lambda Functions with Lambda SnapStart
Our 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 patterns. As you might know if you are already using Lambda, your functions are run inside of a secure and isolated execution environment. The lifecycle of each environment consists of three main phases: Init, Invoke, and Shutdown. Among other things, the Init phase bootstraps the runtime for the function and runs the function’s static code. In many cases, these operations are completed within milliseconds and…
Amazon Inspector Now Scans AWS Lambda Functions for Vulnerabilities
Amazon Inspector is a vulnerability management service that continually scans workloads across Amazon Elastic Compute Cloud (Amazon EC2) instances, container images living in Amazon Elastic Container Registry (Amazon ECR), and, starting today, AWS Lambda functions and Lambda layers. Until today, customers that wanted to analyze their mixed workloads (including EC2 instances, container images, and Lambda functions) against common vulnerabilities needed to use AWS and third-party tools. This increased the complexity of keeping all their workloads secure. In addition, the log4j vulnerability a few months ago was a great example that scanning your functions for vulnerabilities only before deployment is not enough. Because new vulnerabilities can appear at any time, it is very important for the security of your applications that…