?? AWS Wrangler for FinOps: Optimize AWS Costs & Data Processing with Python ??
AWS Wrangler (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.
?? FinOps and AWS Cost Optimization: FinOps and AWS Cost Optimization analysis require working with a lot of data, including price lists, consumption data, inventory data, and organization data ??. Python is a great language for interacting with AWS data services. AWS Wrangler (now officially AWS SDK for pandas—though I still prefer the old name! ??) makes this process even easier.
It's also a great way to incorporate Athena queries into your Python code. While there are edge cases where you might still need to use boto3 ???, in most cases, AWS Wrangler greatly simplifies and shortens your code. (Example at end.)
? What AWS Wrangler Does:
? Effortlessly Read & Write to S3 ?? → Supports Parquet, CSV, JSON, and integrates with Glue Catalog ??. (Iceberg support via Athena, but native coming soon.)
? Optimized Queries on Athena ?? → Pushes down filters to minimize scanned data ?? and supports query caching using CTAS (Create Table As Select), reducing Athena costs.
? Leverage Glue Catalog ?? → Streamline schema management with Glue Catalog integration, making your workflows both efficient and transparent.
Simplify DataFrame Operations ?? → Seamlessly read from and write to Parquet, CSV, or JSON files in S3?
? Works with any AWS Data service ?? → 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.
? Lowers Athena Query Costs ??: Minimizes scanned data, saving money.
? Saves Analyst’s Time ?: Streamlines data coding tasks
?? Pro Tip: Combine AWS Wrangler with tools like Boto3, Cost Explorer API, S3 Inventory, S3 Lens Metrics exports, Glue Data Catalog, and Cost Optimization Hub tools to analyze your cost data and find optimization opportunities in real-time. If you're already using pandas ?? or other dataframe libraries to work with data, AWS Wrangler feels like a natural extension—and if you're working with AWS resources, it can make your life a lot easier. ??
?? Have you used AWS Wrangler? ?? Drop a comment below and share! ?? Please reach out to me directly here on LinkedIn if you'd like to explore opportunities to work together on AWS cost optimization! ?? ??
#AWS #Athena #S3 #Python #FinOps #CloudOptimization #CloudOps #DataAnalysis #DataEngineering #Pandas #PySpark #Jupyter #Brandorr