Category: Advanced (300)

In this post, you learn how to assess your existing Amazon EMR Spark applications, use the Spark upgrade agent directly from the Kiro IDE, upgrade a sample e-commerce order analytics Spark application project (including build configs, source code, tests, and data quality validation), and review code changes before rolling them out through your CI/CD pipeline.

Socure is one of the leading providers of digital identity verification and fraud solutions. Socure’s data science environment includes a streaming pipeline called Transaction ETL (TETL), built on OSS Apache Spark running on Amazon EKS. TETL ingests and processes data volumes ranging from small to large datasets while maintaining high-throughput performance. In this post, we show how Socure was able to achieve 50% cost reduction by migrating the TETL streaming pipeline from self-managed spark to Amazon EMR serverless.

Hundreds of thousands of customers build artificial intelligence and machine learning (AI/ML) and analytics applications on AWS, frequently transforming data through multiple stages for improved query performance—from raw data to processed datasets to final analytical tables. Data engineers must solve complex problems, including detecting what data has changed in base tables, writing and maintaining transformation […]

AWS recently announced the general availability of auto-optimize for the Amazon OpenSearch Service vector engine. This feature streamlines vector index optimization by automatically evaluating configuration trade-offs across search quality, speed, and cost savings. You can then run a vector ingestion pipeline to build an optimized index on your desired collection or domain. Previously, optimizing index […]

AWS recently announced the general availability of GPU-accelerated vector (k-NN) indexing on Amazon OpenSearch Service. You can now build billion-scale vector databases in under an hour and index vectors up to 10 times faster at a quarter of the cost. This feature dynamically attaches serverless GPUs to boost domains and collections running CPU-based instances. With […]

AWS Glue zero-ETL with SAP now supports data ingestion and replication from SAP data sources such as Operational Data Provisioning (ODP) managed SAP Business Warehouse (BW) extractors, Advanced Business Application Programming (ABAP), Core Data Services (CDS) views, and other non-ODP data sources. Zero-ETL data replication and schema synchronization writes extracted data to AWS services like Amazon Redshift, Amazon SageMaker lakehouse, and Amazon S3 Tables, alleviating the need for manual pipeline development. In this post, we show how to create and monitor a zero-ETL integration with various ODP and non-ODP SAP sources.

This post shows how Amazon EMR 7.12 can make your Apache Spark and Iceberg workloads up to 4.5x faster performance.

In this post, we analyze the results from our benchmark tests comparing the Amazon EMR 7.9 optimized Spark runtime against Spark 3.5.5 without encryption optimizations. We walk through a detailed cost analysis and provide step-by-step instructions to reproduce the benchmark.

AWS Glue now supports catalog federation for remote Iceberg tables in the Data Catalog. With catalog federation, you can query remote Iceberg tables, stored in Amazon S3 and cataloged in remote Iceberg catalogs, using AWS analytics engines and without moving or duplicating tables. In this post, we discuss how to get started with catalog federation for Iceberg tables in the Data Catalog.

In this post, we show how you can use Amazon Redshift to write data directly to Apache Iceberg tables stored in Amazon S3 and S3 Tables for seamless integration between your data warehouse and data lake while maintaining ACID compliance.