SageMaker will build, train, and deploy ML models. AWS Glue crawlers collect metadata from the transformed data and catalogs it for analytics and visualization using Athena and QuickSight. AWS Glue DataBrew is a no-code data transformation service that you can use to quickly build your transformation jobs. The proposed architecture in Figure 1 relies on AWS Managed Services. Using AWS Glue Data Catalog, Amazon Athena, Amazon QuickSight, and Amazon SageMaker to catalog and visualize data with machine learning (ML)ĭata analytics pipeline with AWS Managed Services.Running OLAP workloads without costly third-party software licenses, dedicated infrastructure, or the need to migrate data.Connecting your on-premises database to the cloud for data profiling, discovery, and transformation.In this post, we discuss building a cloud-based OLAP cube and ETL architecture that will yield faster results at lower costs without sacrificing performance by: This required teams to build and maintain complex extract, transform, and load (ETL) pipelines to model and organize data, oftentimes with commercial-grade analytics tools. These complex queries were compute and memory-intensive. For decades, enterprises used online analytical processing (OLAP) workloads to answer complex questions about their business by filtering and aggregating their data.
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