AWS Big Data Blog
Introducing the HubSpot connector for AWS Glue
This post introduces the new HubSpot managed connector for AWS Glue, and demonstrates how you can integrate HubSpot data into your existing data lake on AWS. By consolidating HubSpot data with data from your AWS accounts and from other SaaS services, you can enhance, analyze, and optionally write the data back to HubSpot, creating a seamless and integrated data experience.
Scaling RISE with SAP data and AWS Glue
AWS Glue OData connector for SAP uses the SAP ODP framework and OData protocol for data extraction. This framework acts in a provider-subscriber model to enable data transfers between SAP systems and non-SAP data targets. This blog post details how you can extract data from SAP and implement incremental data transfer from your SAP source using the SAP ODP OData framework with source delta tokens.
Amazon EMR streamlines big data processing with simplified Amazon S3 Glacier access
In this post, we demonstrate how to set up and use Amazon EMR on EC2 with S3 Glacier for cost-effective data processing.
Develop a business chargeback model within your organization using Amazon Redshift multi-warehouse writes
Now, we are announcing general availability (GA) of Amazon Redshift multi-data warehouse writes through data sharing. This new capability allows you to scale your write workloads and achieve better performance for extract, transform, and load (ETL) workloads by using different warehouses of different types and sizes based on your workload needs.
Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud
In this post, we explore how to use Aurora MySQL-Compatible Edition Zero-ETL integration with Amazon Redshift and dbt Cloud to enable near real-time analytics. By using dbt Cloud for data transformation, data teams can focus on writing business rules to drive insights from their transaction data to respond effectively to critical, time sensitive events.
Intel Accelerators on Amazon OpenSearch Service improve price-performance on vector search by up to 51%
OpenSearch Service is a managed service for the OpenSearch search and analytics suite, which includes support for vector search. By running your OpenSearch 2.17+ domains on C/M/R 7i instances, you can achieve up to a 51% price-performance gain compared to the past R5 instances on OpenSearch Service. As we discuss in this post, this launch offers improvements to your infrastructure total cost of ownership (TCO) and savings.
Run Apache XTable in AWS Lambda for background conversion of open table formats
In this post, we explore how Apache XTable, combined with the AWS Glue Data Catalog, enables background conversions between open table formats residing on Amazon S3-based data lakes, with minimal to no changes to existing pipelines, in a scalable and cost-effective way.
Introducing generative AI troubleshooting for Apache Spark in AWS Glue (preview)
This post demonstrates how generative AI troubleshooting for Spark in AWS Glue helps your day-to-day Spark application debugging. It simplifies the debugging process for your Spark applications by using generative AI to automatically identify the root cause of failures and provides actionable recommendations to resolve the issues.
Introducing generative AI upgrades for Apache Spark in AWS Glue (preview)
Today, we are excited to announce the preview of generative AI upgrades for Spark, a new capability that enables data practitioners to quickly upgrade and modernize their Spark applications running on AWS. Starting with Spark jobs in AWS Glue, this feature allows you to upgrade from an older AWS Glue version to AWS Glue version 4.0. This new capability reduces the time data engineers spend on modernizing their Spark applications, allowing them to focus on building new data pipelines and getting valuable analytics faster.
Accelerate your data workflows with Amazon Redshift Data API persistent sessions
In this post, we’ll walk through an example ETL process that uses session reuse to efficiently create, populate, and query temporary staging tables across the full data transformation workflow—all within the same persistent Amazon Redshift database session. You’ll learn best practices for optimizing ETL orchestration code, reducing job runtimes by eliminating connection overhead, and simplifying pipeline complexity