In this blog post, we will delve into the significance of zero-ETL integration and introduce an exciting new feature: history mode for Amazon Aurora PostgreSQL-Compatible Edition, Amazon Aurora MySQL-Compatible Edition, Amazon Relational Database Service (Amazon RDS) for MySQL, and Amazon DynamoDB’s zero-ETL integration with Amazon Redshift. This feature is designed to enhance historical data tracking and analysis for customers.
Additionally, we will present a scalable solution for analyzing AWS WAF logs utilizing Amazon Data Firehose and Apache Iceberg. This method streamlines the process from log ingestion to storage, allowing you to set up a delivery stream that directly channels AWS WAF logs into Apache Iceberg tables stored in Amazon S3. The implementation requires zero infrastructure setup, and you only incur costs for the data processed. For more in-depth insights on this topic, check out this additional blog post.
If you’re planning to migrate from Standard brokers to Express brokers in Amazon MSK, it’s crucial to understand that this transition involves moving to a new Express-based cluster. While establishing a new cluster with Express brokers is straightforward, migrating from an existing MSK cluster requires careful planning. In this section, we will outline how to utilize Amazon MSK Replicator to transfer all data and metadata from your current cluster to a new one featuring Express brokers.
Next, we will discuss the foundational elements of Amazon SageMaker Unified Studio. By abstracting complex technical implementations behind user-friendly interfaces, organizations can enhance governance while enabling efficient resource management. This approach ensures consistency in infrastructure deployment while allowing flexibility for diverse business needs. For further guidance, refer to this excellent resource that provides insights into the onboarding process.
Moreover, we are excited to announce that Amazon Redshift Serverless has increased its base capacity to a remarkable 1024 RPUs, which doubles the previous limit of 512 RPUs. This enhancement allows for improved performance in handling complex queries and write-intensive tasks, ensuring high throughput and low latency.
In another section, we will explore the integration of DeepSeek with Amazon OpenSearch Service’s vector database and Amazon SageMaker. This integration supports RAG workflows by connecting to models hosted by DeepSeek, Cohere, and OpenAI, as well as those on Amazon Bedrock and SageMaker.
Finally, we will share strategies for managing errors in Apache Flink applications on AWS, which can be applied broadly to stream processing applications.
For those interested in modernizing their data platforms, we will review how Open Universities Australia achieved significant reductions in ETL costs by leveraging AWS Cloud Development Kit and AWS Step Functions. If you’re curious about hybrid big data analytics, our discussion on Amazon EMR on AWS Outposts will provide valuable insights.
Location: Amazon IXD – VGT2, 6401 E Howdy Wells Ave, Las Vegas, NV 89115.
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