Category: Learning Levels

Organizations are increasingly using a multi-cloud strategy to run their production workloads. We often see requests from customers who have started their data journey by building data lakes on Microsoft Azure, to extend access to the data to AWS services. Customers want to use a variety of AWS analytics, data, AI, and machine learning (ML) […]

In this post, we walk you through the process of testing workload isolation architecture using Amazon Redshift Data Sharing and Test Drive utility. We demonstrate how you can use SQL for advanced price performance analysis and compare different workloads on different target Redshift cluster configurations.

In this post, we demonstrate how you can publish an enriched real-time data feed on AWS using Amazon Managed Streaming for Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink. You can apply this architecture pattern to various use cases within the capital markets industry; we discuss some of those use cases in this post.

Amazon Redshift, a warehousing service, offers a variety of options for ingesting data from diverse sources into its high-performance, scalable environment. Whether your data resides in operational databases, data lakes, on-premises systems, Amazon Elastic Compute Cloud (Amazon EC2), or other AWS services, Amazon Redshift provides multiple ingestion methods to meet your specific needs. The currently […]

In this post, we demonstrate how to use the AWS CDK and DSF to create a multi-data warehouse platform based on Amazon Redshift Serverless. DSF simplifies the provisioning of Redshift Serverless, initialization and cataloging of data, and data sharing between different data warehouse deployments.

To meet the demands of diverse data integration use cases, AWS Glue now supports SaaS connectivity for Salesforce. This enables users to quickly preview and transfer their customer relationship management (CRM) data, fetch the schema dynamically on request, and query the data. This post explores the new Salesforce connector for AWS Glue and demonstrates how to build a modern extract, transform, and load (ETL) pipeline with AWS Glue ETL scripts.

This blog post provides a step-by-step guide to integrating IAM Identity Center with Microsoft Entra ID as the IdP and configuring Amazon Redshift as an AWS managed application. Additionally, you’ll learn how to set up the Amazon Redshift driver in Tableau, enabling SSO directly within Tableau Desktop.

In this post, we share a chargeback model that you can use to track and allocate the costs of Spark workloads running on Amazon EMR on EC2 clusters. We describe an approach that assigns Amazon 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 Amazon Relational Database Service (Amazon 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.

Kaplan, Inc. provides individuals, educational institutions, and businesses with a broad array of services, supporting our students and partners to meet their diverse and evolving needs throughout their educational and professional journeys. In this post, we discuss how the Kaplan data engineering team implemented data integration from the Salesforce application to Amazon Redshift. The solution uses Amazon Simple Storage Service as a data lake, Amazon Redshift as a data warehouse, Amazon Managed Workflows for Apache Airflow (Amazon MWAA) as an orchestrator, and Tableau as the presentation layer.