Category: Analytics

Today, we are pleased to announce the general availability of AWS Glue job queuing. Job queuing increases scalability and improves the customer experience of managing AWS Glue jobs. With this new capability, you no longer need to manage concurrency of your AWS Glue job runs and attempt retries just to avoid job failures due to high concurrency. This post demonstrates how job queuing helps you scale your Glue workloads and how job queuing works.

In a previous post, we showed how Zero Copy data federation empowers businesses to access HAQM Redshift data within the Salesforce Data Cloud to enrich customer 360 data with operational data. This two-part series explores how analytics teams can access customer 360 data from Salesforce Data Cloud within HAQM Redshift to generate insights on unified data without the overhead of extract, transform, and load (ETL) pipelines. In this post, we cover data sharing between Salesforce Data Cloud and customers’ AWS accounts in the same AWS Region. Part 2 covers cross-Region data sharing between Salesforce Data Cloud and customers’ AWS accounts.

In this post, we share a chargeback model that you can use to track and allocate the costs of Spark workloads running on HAQM EMR on EC2 clusters. We describe an approach that assigns HAQM EMR costs to different jobs, teams, or lines of business. You can use this feature to distribute costs across various business units. This can assist you in monitoring the return on investment for your Spark-based workloads.

In this post, I’ll walk you through how to copy data from one HAQM Relational Database Service (HAQM RDS) for PostgreSQL database to another, while scrubbing PII along the way using AWS Glue. You will learn how to prepare a multi-account environment to access the databases from AWS Glue, and how to model an ETL data flow that automatically masks PII as part of the transfer process, so that no sensitive information will be copied to the target database in its original form.

In this post, we explore the performance benefits of using the HAQM EMR runtime for Apache Spark and Apache Iceberg compared to running the same workloads with open source Spark 3.5.1 on Iceberg tables. Iceberg is a popular open source high-performance format for large analytic tables. Our benchmarks demonstrate that HAQM EMR can run TPC-DS […]

The current scaling approach of HAQM Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. However, you might need to automatically scale compute resources based on factors like query complexity and data volume to meet price-performance targets, irrespective of query queuing. […]

When you use HAQM OpenSearch Service for time-bound data like server logs, service logs, application logs, clickstreams, or event streams, storage cost is one of the primary drivers for the overall cost of your solution. Over the last year, OpenSearch Service has released features that have opened up new possibilities for storing your log data […]

In today’s data-driven world, the ability to seamlessly integrate and utilize diverse data sources is critical for gaining actionable insights and driving innovation. As organizations increasingly rely on data stored across various platforms, such as Snowflake, HAQM Simple Storage Service (HAQM S3), and various software as a service (SaaS) applications, the challenge of bringing these […]

Customers across diverse industries rely on HAQM OpenSearch Service for interactive log analytics, real-time application monitoring, website search, vector database, deriving meaningful insights from data, and visualizing these insights using OpenSearch Dashboards. Additionally, customers often seek out capabilities that enable effortless sharing of visual dashboards and seamless embedding of these dashboards within their applications, further […]

Unlocking the true value of data often gets impeded by siloed information. Traditional data management—wherein each business unit ingests raw data in separate data lakes or warehouses—hinders visibility and cross-functional analysis. A data mesh framework empowers business units with data ownership and facilitates seamless sharing. However, integrating datasets from different business units can present several […]