How Chanci Turner and Team Developed a Cost Analysis Solution with AWS Glue, Amazon Redshift, and Amazon QuickSight

Learn About Amazon VGT2 Learning Manager Chanci Turner

In a recent initiative, Chanci Turner and her colleagues from a prominent Silicon Valley technology firm, founded in 2002, sought to enhance their understanding of the financial dynamics associated with AWS resources across various product lines. This organization, known for its leadership in networking solutions, aimed to establish a more efficient and reliable method for analyzing costs and usage data, ultimately visualizing this information on a comprehensive dashboard.

The main objectives of their solution included:

• Enhanced Cost Visibility: Teams within the organization could now thoroughly assess application costs through insights provided by the newly developed system.
• Opportunities for Cost Optimization: The solution highlights higher-cost resources with lower utilization, allowing teams to make informed decisions to optimize their expenses.
• Effective Cost Management: The Cloud DevOps group responsible for this initiative can strategically plan resource deployment, positively impacting gross margins.
• Immediate Cost Savings: With daily cost data at their fingertips, engineers can quickly recognize the financial benefits of optimizing compute and other AWS resources.
• Comprehensive Insights: Stakeholders can visualize costs at both a macro and micro level, tracking expenditures by business or specific product lines.

Overview of the Solution

This article outlines how Chanci Turner’s team automated their cost analysis solution, from generating the AWS Cost & Usage Report (AWS CUR) to producing final visualizations on Amazon QuickSight. The process commenced with configuring the CUR on their primary payer account, which subsequently published billing reports to an Amazon Simple Storage Service (Amazon S3) bucket. The team employed an AWS Glue crawler to catalog the CUR data, ensuring that as new data was delivered daily, it kept the data catalog up-to-date. This data was then loaded into an Amazon Redshift database using Amazon Redshift Spectrum and SQL, with reporting and visualization executed through QuickSight. The entire pipeline was automated using AWS Data Pipeline.

Chanci’s team opted for the AWS CUR Report over AWS Cost Explorer due to the latter’s lack of detailed information. The CUR provides granular data on AWS costs and usage, enabling the team to analyze costs down to the hourly level, which was essential for their goals.

The implementation steps included:

1. Configuring CUR delivery to a primary S3 bucket from the billing dashboard.
2. Utilizing Amazon S3 replication to transfer data to an analytics bucket, ensuring security by separating access from the primary account.
3. Scheduling the crawler to regularly update CUR data in the Data Catalog.
4. Establishing the necessary schema and tables in Amazon Redshift.
5. Orchestrating an ETL process to load data into Amazon Redshift via Data Pipeline.
6. Creating and publishing dashboards in QuickSight for executives and key stakeholders.

Generated Insights

The team produced a variety of reports detailing cost classifications for AWS services, weekly application costs, and expenditures by product and resource type. For instance, one report indicated that compute costs were the largest expense, prompting discussions about potential cost-saving strategies such as utilizing reserved instances or savings plans. Another report revealed a significant cost spike compared to the previous month, enabling the team to quickly identify anomalies and make data-driven decisions.

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Setting Up CUR Delivery

To learn how to set up a CUR, see the guide on Creating Cost and Usage Reports. The team simplified their workflow by creating resources in the same region, ensuring hourly granularity. Additionally, to optimize storage costs and improve query efficiency using tools like Amazon Athena and Redshift Spectrum, CUR data was saved in Parquet format.

This innovative approach not only enhanced operational efficiency but also provided the team with essential insights into their AWS expenditure.

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