AWS Wrangler for FinOps: Optimize AWS Costs & Data Processing with Python

AWS Wrangler (now officially AWS SDK for pandas) is an open-source Python library that simplifies working with AWS services like S3, Glue, Athena, Redshift, DynamoDB, and more. While it integrates seamlessly with pandas, it also supports other Python data libraries such as Apache Arrow, Modin, Ray, and PySpark, making it a versatile tool for AWS data processing.

---

AWS Wrangler for FinOps and Cost Optimization
FinOps and AWS cost optimization require working with vast amounts of data, including price lists, consumption data, inventory data, and organization data. Python is an excellent language for interacting with AWS data services, and AWS Wrangler makes this process even more efficient.

While there are edge cases where boto3 is still useful, in most cases, AWS Wrangler greatly simplifies and shortens your code—especially when incorporating Athena queries into Python workflows.

---

Key Features of AWS Wrangler

• Effortless Read & Write to S3 – Supports Parquet, CSV, JSON, and integrates with Glue Catalog.
• Optimized Queries on Athena – Pushes down filters to minimize scanned data and reduces Athena costs using query caching with CTAS (Create Table As Select).
• Leverage Glue Catalog – Streamlines schema management for better efficiency and transparency.
• Simplify DataFrame Operations – Read from and write to Parquet, CSV, or JSON files in S3 effortlessly.
• Works with Multiple AWS Data Services – Compatible with S3, Glue, Athena, Redshift, Timestream, OpenSearch, Neptune, QuickSight, CloudWatch Logs, DynamoDB, EMR, Secrets Manager, and RDS (Aurora, PostgreSQL, MySQL, SQL Server, Oracle).

The result? Optimized storage, faster queries, and lower costs.

---

​Why AWS Wrangler is Great for Cost Optimization

• Reduces Data Transfer Costs – Enables in-place filtering and processing in S3, cutting unnecessary data movement.
• Lowers Athena Query Costs – Minimizes scanned data, directly reducing query expenses.
• Saves Analyst Time – Simplifies data coding tasks, allowing teams to focus on higher-value work.

---

Pro Tip for Maximum Cost Efficiency
Combine AWS Wrangler with tools like:

• Boto3
• Cost Explorer API
• S3 Inventory & S3 Lens Metrics exports
• Glue Data Catalog
• AWS Cost Optimization Hub

Using these together helps you analyze cost data and uncover optimization opportunities in real time. If you're already using pandas or other DataFrame libraries, AWS Wrangler feels like a natural extension—and if you’re managing AWS resources, it can make your workflow significantly more efficient.

---

Want to Optimize AWS Costs with AWS Wrangler?
Have you used AWS Wrangler in your cost optimization efforts? Whether you're just exploring its capabilities or actively using it, join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page to discuss how it can streamline your workflows and reduce costs. Connect with us to explore AWS cost optimization strategies tailored to your needs!