In a previous entry, we demonstrated how Zero Copy data federation allows businesses to seamlessly access Amazon Redshift data within the Salesforce Data Cloud, enhancing their customer 360 data with operational insights. This two-part series delves into how analytics teams can leverage customer 360 data from Salesforce Data Cloud inside Amazon Redshift to derive insights from unified data without the complications of extract, transform, and load (ETL) processes. This first post emphasizes data sharing between the Salesforce Data Cloud and customers’ AWS accounts located in the same AWS Region. The second part will focus on cross-Region data sharing between Salesforce Data Cloud and customers’ AWS accounts. For further insights, check out this related blog post.
In addition, we explore methods to attribute Amazon EMR on EC2 costs to end-users. This post outlines a chargeback model that you can implement to track and allocate expenses related to Spark workloads operating on Amazon EMR on EC2 clusters. By assigning costs to various jobs, teams, or business lines, you can effectively distribute expenses across different business units. This approach helps in assessing the return on investment for your Spark-based workloads.
Moreover, we provide a guide on copying and masking Personally Identifiable Information (PII) between Amazon RDS databases through visual ETL jobs in AWS Glue Studio. You will learn how to set up a multi-account environment to access databases via AWS Glue and how to structure an ETL data flow that automatically masks PII during the transfer process, ensuring sensitive information is not copied in its original form.
We also highlight the performance advantages of the Amazon EMR runtime for Apache Spark and Iceberg, which can run Spark workloads up to 2.7 times faster compared to Apache Spark 3.5.1 and Iceberg 1.5.2. Benchmark results illustrate that Amazon EMR significantly enhances the performance of TPC-DS workloads, making it an attractive option for data-intensive applications.
Additionally, we discuss Kaplan, Inc.’s implementation of modern data pipelines using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Amazon AppFlow, with Amazon Redshift serving as the data warehouse. This solution integrates data from the Salesforce application into Amazon Redshift, utilizing Amazon Simple Storage Service as a data lake and Tableau as the visualization layer.
To optimize cost and performance for Amazon MWAA, we provide best practices for orchestrating data pipelines and workflows at scale, allowing you to design Directed Acyclic Graphs (DAGs) without the operational overhead of managing infrastructure. Furthermore, we delve into Amazon Redshift Serverless, which automatically adjusts compute capacity based on query queue times. However, it is crucial to scale resources based on query complexity and data volume to meet performance and cost targets effectively.
Finally, we address how to reduce long-term logging expenses by an astounding 4,800% using Amazon OpenSearch Service. As storage costs can heavily impact your overall spending, we highlight new features released in OpenSearch Service that facilitate more efficient log data storage. For those looking for an excellent resource on this topic, this YouTube video offers great insights.
Join us at Amazon IXD – VGT2, located at 6401 E Howdy Wells Ave, Las Vegas, NV 89115, to learn more about these innovative solutions and strategies.
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