Introduction
In the world of computing, AWS Glue is often the first choice for ETL pipelines. During AWS Glue Data Catalog is essential for integration with services such as Athena, there are challenges when using GLUE's own ETL environment with PySpark in certain projects. This post explains why the combination of Elastic Container Service (ECS), Polars, and Iceberg represents a powerful alternative and what advantages this approach offers in terms of flexibility, development speed and performance.
The role of ECS, Polars & Iceberg in the ETL setup
Before we jump into the comparison, it's important to understand which role ECS, Polars and Iceberg play in our ETL setup.
- Amazon ECS (Elastic Container Service) serves as a platform for running and scaling containers that manage our ETL processes. It enables us to run Python-based data processing tasks flexibly and efficiently in the cloud.
- Polars is a powerful DataFrame library in Python designed for fast and memory-efficient data manipulation. It provides excellent performance, particularly with medium to large data sets, and enables rapid iterations during development.
- Apache Iceberg is a modern spreadsheet format for data lakes that supports ACID transactions, schema evolution, and efficient queries. It ensures that the processed data is stored in a consistent and easily accessible format that is compatible with tools such as Amazon Athena.
Together, these three components form a flexible and scalable alternative to traditional ETL tools such as AWS Glue.
AWS Glue (PySpark) vs. ECS+ Polars
A comparison of flexibility and development speed
Development speed & developer experience
Working with AWS Glue often requires deep familiarization with PySpark and the specific characteristics of Glue. This can extend development cycles and make debugging difficult. In contrast, using Polars in an ECS environment allows development in pure Python, which speeds up implementation and makes debugging easier. Polars' intuitive API also contributes to an improved developer experience.
Flexibility
As a managed service, AWS Glue offers less scope for individual adjustments. The infrastructure is predetermined, and integrating additional tools or libraries can be challenging. With ECS, on the other hand, you have full control over the environment. Specific libraries can be added, the infrastructure can be scaled and optimized according to individual needs, and tailor-made solutions can be implemented.
Performance
An important aspect is the performance data processing. AWS Glue, based on Spark, is optimized to process very large, distributed amounts of data. This makes Glue a strong choice for massive data sets that need to be scaled across multiple nodes. However, Spark comes with overhead, which can affect efficiency with small or medium-sized amounts of data.
Polars, on the other hand, is specifically developed for fast data processing on single-node systems and makes efficient use of modern hardware architectures. In many cases, Polars achieves a comparable or even better performance as glue, especially for iterative development processes and medium to large data sets. However, when processing very large, distributed amounts of data, Glue can offer advantages due to its distributed architecture. That leaves the latency Lower for Polars, which has a positive effect on responsiveness and debugging.
Challenges of writing Iceberg tables with Python
One aspect that needs to be considered is the current complexity of writing Iceberg tables using Python. While Apache Iceberg is a powerful table format for data lakes, the PyIceberg library only implemented writing support since version 0.6.0. However, there are still restrictions, particularly when writing partitioned tables. Support for partitioned writes is not yet fully developed, which can affect efficiency when processing large data sets.
These restrictions can affect the efficiency of data processing with Polars affect because larger amounts of data must be loaded into RAM without effective partitioning. However, current problems can often be solved by targeted Athena Queries , which means that data can continue to be efficiently queried and processed.
Cost efficiency and benefits for our customers
In addition to flexibility and speed of development, the cost efficiency a decisive role in choosing an ETL architecture. As a managed service, AWS Glue offers easy scaling, but can incur high costs for longer or more complex processing processes because billing is based on processing time. Especially with on-demand jobs or iterative development cycles, these costs add up quickly. However, AWS Glue also offers cost-saving options, such as running Python shell jobs with reduced DPU, which can be cost-effective for small to medium-sized tasks.
The use of ECS and Polars enables targeted use of resources, which Reduced operating costs can be. Because Polars is resource-efficient and ECS offers flexible pricing options, customers benefit from lower infrastructure costs without sacrificing performance. These cost advantages make the approach particularly attractive for companies based on scalable yet budget-friendly solutions are needed.
Orchestration with AWS Step Functions
Regardless of whether you use AWS Glue or ECS with Polars, orchestrating ETL processes is critical for a stable and scalable data workflow. AWS Step Functions offers a powerful way to create, manage and monitor complex workflows.
With Step Functions, the various processing steps such as Data Extraction, Transformation, and Loading (ETL) be orchestrated. They support both AWS Glue jobs and ECS tasks, which makes a seamless integration of both approaches is possible. This makes it possible to combine Glue and Polars depending on requirements: For example, Glue can be used to process very large, distributed data sets, while Polars is used for rapid, iterative analyses.
By using Step Functions, companies benefit from:
- Automate complex processes with minimal manual intervention.
- Better fault tolerance and recovery mechanisms.
- combinability various tools and technologies in a consistent workflow.
Conclusion: When is ECS+ Polars the better choice?
Experience shows that the use of ECS and Polars is particularly beneficial when:
- Quick development and efficient debugging are required.
- High flexibility is required when it comes to infrastructure and integration of additional tools.
- Individual adjustments and optimizations are necessary.
- One comparable or better performance compared to glue, especially for non-distributed data processes.
- Data processed in batches can be, which further increases efficiency and control over processing.
- cost efficiency plays a decisive role and infrastructure costs should be minimized.
Although there are currently still challenges when writing partitioned Iceberg tables with Python, the benefits outweigh the flexibility, development speed, and performance. Companies should assess their specific requirements and resources to find the best solution for them. AWS Glue However, it may be a better choice if you need to process very large, distributed amounts of data, or if you prefer a fully managed, serverless solution that requires less maintenance.
Have you had any experience with Glue or alternative architectures? Which solutions have proven effective in your projects? If you have any questions about the approaches presented or need individual advice, feel free to contact us directly!
