AWS Architecture Blog

Building a Cloud-based OLAP Cube and ETL Architecture with AWS Managed Services

For decades, enterprises used online analytical processing (OLAP) workloads to answer complex questions about their business by filtering and aggregating their data. These complex queries were compute and memory-intensive. 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.

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:

  • Connecting your on-premises database to the cloud for data profiling, discovery, and transformation
  • Running OLAP workloads without costly third-party software licenses, dedicated infrastructure, or the need to migrate data
  • Using AWS Glue Data Catalog, Amazon Athena, Amazon QuickSight, and Amazon SageMaker to catalog and visualize data with machine learning (ML)

Data analytics pipeline with AWS Managed Services

The proposed architecture in Figure 1 relies on AWS Managed Services. AWS Glue DataBrew is a no-code data transformation service that you can use to quickly build your transformation jobs. AWS Glue crawlers collect metadata from the transformed data and catalogs it for analytics and visualization using Athena and QuickSight. SageMaker will build, train, and deploy ML models.

This architecture will help you get answers from your data to your users as fast as possible without needing to migrate your data to AWS. There is no coding required, so you can leverage data transformation, cataloging, analytics, and ML quickly.

Figure 1. Example architecture using AWS Managed Services

Figure 1. Example architecture using AWS Managed Services

Benefits of AWS Managed Services for data analytics

Immediate connectivity to on-premises databases

The example architecture in Figure 1 begins with an online transaction processing (OLTP) database running in your corporate data center. Figure 2 shows how you can establish a Java database connectivity (JDBC) connection from the OLTP database to DataBrew running in AWS to run OLAP workloads. DataBrew supports data sources using JDBC for common data stores such as Microsoft SQL Server, MySQL, Oracle, and PostgreSQL.

DataBrew - JDBC connection to data source

Figure 2. DataBrew – JDBC connection to data source

Automatic data discovery

Figures 3 through 6 show how DataBrew summarizes your data for discovery. You can profile your data to understand patterns and detect anomalies. You can also run transformations called “jobs” in DataBrew without writing any code using over 250 built-in transforms.

DataBrew - dataset profiling overview

Figure 3. DataBrew – dataset profiling overview


DataBrew - data correlation patterns

Figure 4. DataBrew – data correlation patterns


DataBrew - data points distribution

Figure 5. DataBrew – data points distribution

No-code data transformation and cataloging

To run OLAP-type transactions, you can create jobs based on the transformation steps shown in Figure 6. These steps collectively are referred to as DataBrew recipes. These recipe results can be run as a job and outputted to an Amazon Simple Storage Service (Amazon S3) bucket.

A DataBrew project user interface view with sample data and transformation functions

Figure 6. A DataBrew project user interface view with sample data and transformation functions

Scheduled DataBrew jobs act similarly to scheduled ETL pipelines in OLAP. Based on data refresh and business requirements, DataBrew can run a job on a recurring basis (for example, every 12 hours). This can be run at a particular time of day, or as defined by a valid CRON expression. This helps you automate your transformation workflows.

The OLAP catalog is a set of metadata that sits between the actual OLAP data stored and applications. To create a Data Catalog, you can use AWS Glue crawlers to automatically classify your data to determine the data’s format, schema, and associated properties. Figure 7 shows the results of a crawler’s results written to Data Catalog as metadata to help data users find the data they need.

AWS Glue crawler metadata table output of column names and data types

Figure 7. AWS Glue crawler metadata table output of column names and data types

Data analytics without third-party software licenses

You can run analytics on your data by referring to the metadata definitions in the Data Catalog as references to the actual data in Amazon S3 using Athena. Athena is well suited for running one-time queries using standard SQL to query the transformed data directly in Amazon S3 without having to move data around. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Enterprises often supplement their OLAP workloads with separate visualization and business intelligence (BI) tools. These tools often come with their own licensing, server management, and security considerations.

You can visualize curated data using QuickSight, a scalable, serverless, embeddable, ML-powered BI service. QuickSight lets you easily create and publish interactive BI dashboards that include ML-powered insights, as shown in Figure 8. These dashboards can be shared with other users and embedded within your own applications.

A sample of data visualization options with Amazon QuickSight

Figure 8. A sample of data visualization options with Amazon QuickSight

Finally, you can incorporate ML workloads to OLAP workloads using SageMaker. In the past, ML workloads were often expensive, resource-intensive, and inaccessible. SageMaker provides a fully managed ML service to quickly and easily build and train ML models and directly deploy them into a production-ready hosted environment.


In this post, we show you how to connect your on-premises database using a JDBC connection to DataBrew for data profiling, discovery, and transformation. We looked at how you can use DataBrew recipes and jobs to run OLAP workloads without costly third-party software licenses, dedicated infrastructure, or the need to migrate any data. We also looked at AWS capabilities in data cataloging, visualization, and machine learning using Data Catalog, Athena, QuickSight, and SageMaker without having to manage any servers.

Laying the foundation to modernize an analytics workflow is critical for many enterprises that are looking to reduce the time it takes to understand their business. With AWS, you can perform enterprise-scale analytics with our portfolio of analytics services.


Peter Chung

Peter Chung

Peter Chung is a Solutions Architect for AWS. He is passionate about helping customers uncover insights from their data. He has been building solutions to help organizations make data-driven decisions in both the public and private sectors. He holds all AWS certifications and two GCP certifications.

Mugdha Pradeep Vartak

Mugdha Pradeep Vartak

Mugdha Vartak is a Solutions Architect with AWS, she works closely and helps guide mid-large enterprise customers in designing, building, and migrating scalable and resilient architectures within the AWS cloud and to maximize the value of their data.