Amazon SageMaker Canvas now supports sharing machine learning (ML) models with Amazon QuickSight, enabling analysts to build models in Canvas and generate predictions to build dashboards in QuickSight. This extends the ML/Analytics integrated solution between Canvas and QuickSight for analysts to build models, generate predictions, enrich them with interactive dashboards, and use insights for effective business decisions, without writing a single line of code. 

Amazon SageMaker Canvas now supports five new data transforms, enabling you to better prepare and analyze your data before building machine learning (ML) models. Data is the foundation of machine learning and transforming raw data to make it suitable for ML model building and generating predictions is key to better insights. Starting today, SageMaker Canvas allows you to change the type of data in your columns between numeric, text, and datetime, while also displaying the associated feature for that data type such as binary and categorical. This gives you the flexibility to manually change the type of data in your columns based on the features. The ability to choose the right data type ensures data integrity and accuracy prior to building ML models. As an example, using a datetime data type ensures only valid dates are stored in that particular column. 

Amazon SageMaker Canvas now provides the ability to train machine learning (ML) models with different objective metrics, allowing you to gain a more comprehensive understanding on the model’s strengths and weaknesses. SageMaker Canvas is a visual interface that enables business analysts and citizen data scientists to generate accurate ML predictions on their own — without requiring any ML expertise or having to write a single line of code.

Amazon Web Services customers can now get detailed health metrics and analysis of their multi-application deployments residing in the same instance with Amazon CloudWatch Application Insights. CloudWatch Application Insights helps customers gain actionable insights for their application environment and AWS resources by making it easy to set up and monitor applications, recognize problems, and use data to make decisions. 

Amazon SageMaker Canvas now supports the ability to provide a custom output location in Amazon S3 for machine learning (ML) artifacts, such as trained models, explainability reports, and prediction results allowing you to organize and structure your output directory in a way that aligns with your specific needs and preferences. SageMaker Canvas is a visual interface that enables business analysts and citizen data scientists to generate accurate ML predictions on their own — without requiring any ML expertise or having to write a single line of code. 

AWS DataSync support for copying data to and from Azure Blob Storage is now generally available. Using DataSync, you can move your object data at scale between Azure Blob Storage and AWS Storage services such as Amazon S3. AWS DataSync supports writing to block blobs and can read from all blob types within Azure Blob Storage. It can also be used with Azure Data Lake Storage (ADLS) Gen 2.

Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks like Apache Spark and Apache Hive without configuring, managing, and scaling clusters or servers. Starting today, you can store logs for your EMR Serverless Spark and Hive applications in Amazon CloudWatch.

AWS Glue Studio now supports Amazon Redshift Serverless as a data source or target out-of-the-box. AWS Glue Studio enables ETL (Extract, Transform and Load) developers to visually transform data with a no-code, drag-and-drop interface. Glue’s visual interface saves the developer time authoring, running, and monitoring highly scalable ETL jobs. Developers can pull data from a variety of data sources including AWS services like Amazon S3, Amazon Kinesis, and Amazon Redshift. With this new feature, developers can read and write data into Amazon Redshift Serverless more effectively.

AWS Glue Studio Visual ETL jobs now let you use DataBrew Recipes as steps in the flow of transformations. AWS Glue Studio Visual ETL is a no-code job authoring UI for ETL developers with a flow-diagram interface. AWS Glue DataBrew is a no code data preparation tool for business users and data analysts with a spreadsheet-style UI. The new integration between the two makes it simpler to deploy and scale DataBrew jobs and gives DataBrew users access to AWS Glue features not available in DataBrew. The integration also works in code-based jobs.

Amazon Redshift today announces the preview release of Apache Iceberg support, enabling users to run analytics queries on Apache Iceberg tables within Redshift. Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service used by tens of thousands of customers. Whether your data is stored in operational data stores, data lakes, streaming engines or within your data warehouse, Amazon Redshift helps you quickly ingest, securely share data, and achieve the best performance for the best price. Apache Iceberg, one of the most recent open table formats, has been used by many customers to simplify data processing on rapidly expanding and evolving tables stored in data lakes.