AWS Data and Analytics: What's New in 2025

A Quick Review of the AWS Data and Analytics Services

Before we dive into the latest updates, let's recap the core components of the AWS data and analytics stack. At the heart of the modern data strategy is the data lake, typically built on Amazon S3, the scalable and cost-effective object storage service. On top of this foundation, AWS offers a variety of services for different needs:  

• Databases: Relational databases like Amazon RDS and Aurora, non-relational databases like DynamoDB, and data warehousing solutions like Amazon Redshift.  
• Analytics: Services like Amazon Kinesis for real-time data streaming, AWS Glue for serverless ETL (Extract, Transform, Load), and Amazon EMR for big data processing.  
• Machine Learning: Amazon SageMaker for building, training, and deploying machine learning models, and Amazon Bedrock for generative AI applications.  
• Querying and Searching: Amazon Athena for interactive querying of data in S3 and other sources, and Amazon OpenSearch Service for searching and analyzing petabytes of data.  
• Business Intelligence: Amazon QuickSight for creating interactive dashboards and visualizations.  

What's New in 2025

Now, let's explore some of the most significant updates and additions to the AWS data and analytics stack in 2025:

Amazon S3

Amazon S3, the core storage service for AWS data lakes, has received several key updates aimed at improving performance, data management, and accessibility. One major enhancement is the support for Apache Iceberg, an open table format designed for large-scale analytic datasets. With Iceberg, S3 data lakes can efficiently handle updates and inserts, making them more suitable for dynamic workloads. Additionally, S3 now offers automated metadata capture and querying, allowing users to instantly discover and understand their data. Data integrity has also been strengthened with the introduction of CRC-based whole object checksums, ensuring data accuracy and durability. Finally, the new S3 Storage Browser provides a user-friendly web interface for authorized users to easily browse, download, and upload data directly from their applications.  

Amazon Aurora

Amazon Aurora, the cloud-native relational database service, has introduced a new serverless distributed SQL database engine called DSQL. This engine offers active-active high availability across multiple regions, ensuring continuous availability and virtually unlimited scalability for demanding applications. DSQL provides independent scaling of reads, writes, and compute resources, allowing users to optimize performance and cost for their specific workloads.  

Amazon Redshift

Amazon Redshift, the data warehousing service, has received enhancements focused on simplifying data integration and improving query performance. Materialized views, which store pre-computed query results, can now stay up-to-date automatically, eliminating the need for manual refresh or complex ETL processes. This feature significantly reduces the time and effort required to maintain data consistency and accelerate query performance. Additionally, Redshift has simplified data sharing by enabling write support for multiple Redshift clusters, allowing for flexible scaling and workload isolation.  

Amazon DynamoDB

Amazon DynamoDB, the key-value and document database service, has received updates focused on improving consistency, availability, and cost-effectiveness. Global tables now offer strong consistency in multi-region deployments, ensuring data accuracy and eliminating conflicts for globally distributed applications. DynamoDB also offers a configurable point-in-time recovery period, allowing users to meet compliance requirements by restoring data to any second within the defined recovery window. Additionally, the introduction of warm throughput for tables and indexes enables pre-warming DynamoDB tables to handle peak events like product launches or flash sales. Finally, a significant price reduction on on-demand throughput and global tables makes DynamoDB more cost-effective for a wider range of workloads.  

AWS Glue

AWS Glue, the serverless ETL service, has been upgraded to version 5.0, incorporating the latest versions of Spark and other data processing tools. This new version also includes generative AI upgrades for Spark, enabling automated code migration from older versions of Glue and AI-powered troubleshooting for faster issue resolution. Connectivity has been expanded with new connectors for various data sources, simplifying data ingestion from diverse platforms. Additionally, the Glue Data Catalog now automatically generates statistics for new and updated tables, optimizing query performance without manual intervention.  

Amazon QuickSight

Amazon QuickSight, the business intelligence service, now integrates with Amazon Q, a generative AI service that enables natural language querying of data. This integration allows users to unify insights from structured and unstructured data, making it easier to explore and visualize data from various sources. QuickSight has also received improvements to image and font handling, providing more customization options for creating visually appealing dashboards and reports.  

Amazon SageMaker

Amazon SageMaker, the machine learning platform, has undergone a significant transformation with the introduction of Unified Studio, a single interface that brings together all analytics and AI/ML services. This unified environment simplifies data processing, analysis, model development, and deployment, streamlining the machine learning workflow for data scientists and engineers. SageMaker also introduces Lake House, a feature that unifies data from multiple sources, including S3 data lakes and Redshift data warehouses, into a single repository for data science workloads. Data governance has been enhanced with SageMaker Data and AI Governance, which provides secure data discovery, access control, and automated data lineage capture. Finally, SageMaker now offers zero-ETL integrations with various applications, automating data extraction and loading for faster and more efficient data integration.  

Conclusion

These updates and additions to the AWS data and analytics stack provide a powerful toolkit for organizations to manage, analyze, and leverage their data effectively.

Ascendient Learning is an advanced tier AWS training partner and offers official AWS training and AWS certification paths, including AWS Data Analytics. Not sure where to start? Begin with the foundational AWS Technical Essentials course, then dive deeper with specialized training tracks for data scientists, architects, developers, or infrastructure specialists.

If you would like to view the webinar on this topic by Myles Brown, watch the 1-hour What's New in the AWS Data and Analytics Stack for 2025.