AWS Data Wrangler vs Pandas: What are the differences?

Introduction

In this markdown, we will discuss the key differences between AWS Data Wrangler and Pandas, with a specific focus on six main points.

1. Data Source Connectivity: AWS Data Wrangler provides out-of-the-box connectivity to various AWS data sources such as Amazon S3, Amazon Athena, Amazon Redshift, and more. It simplifies the process of reading and writing data from these sources, allowing seamless integration with AWS services. On the other hand, Pandas primarily focuses on reading and writing data from local or conventional file systems like CSV, Excel, or SQL databases.

2. Scaling Capabilities: One significant advantage of using AWS Data Wrangler is its ability to handle large-scale datasets without memory constraints. It leverages AWS Glue Data Catalog, an Apache Parquet-based metastore, allowing for distributed computing and efficient columnar storage. In contrast, Pandas operates in memory and might struggle with larger datasets that exceed available memory, resulting in slower processing times or even crashing.

3. Parallel Processing and Performance Optimization: AWS Data Wrangler is designed to take full advantage of distributed computing, enabling parallel processing of data. It provides optimized connectors that leverage Amazon Athena's query execution engine, Glue's PySpark runtime, and other services to deliver faster and more efficient data operations. Pandas, while powerful for single-threaded data processing, lacks the ability to scale horizontally, which can limit performance on large datasets.

4. Integration with AWS Ecosystem: Being an AWS-native library, AWS Data Wrangler seamlessly integrates with other AWS services, including AWS Glue, AWS Lambda, and Amazon EMR. This tight integration enables smooth workflows and allows users to take full advantage of the AWS ecosystem. In contrast, Pandas is a standalone library and does not have built-in integrations with AWS services.

5. Data Transformation and ETL Capabilities: AWS Data Wrangler provides comprehensive data transformation functions specifically tailored for handling Data Engineering and ETL workloads. It supports advanced features such as data type casting, data partitioning, schema evolution, and custom processing functions. While Pandas also offers data manipulation capabilities, it does not provide the same level of specialized functions for ETL tasks, making it less suitable for complex data engineering scenarios.

6. Serverless and Cloud-Native Architecture: AWS Data Wrangler is designed to embrace a serverless and cloud-native architecture. It can seamlessly interact with serverless AWS services like AWS Lambda, Amazon S3, and Amazon Glue, allowing users to build scalable and cost-effective data workflows. Pandas, on the other hand, is a Python library that runs on local or on-premises infrastructure and does not come with built-in serverless capabilities.

In Summary, AWS Data Wrangler offers enhanced data source connectivity, scaling capabilities, optimized performance, seamless AWS integration, specialized ETL features, and a serverless architecture compared to Pandas.