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Rookie Mistakes to Avoid when Using AWS Glue

The corporate world is dynamic and highly competitive. Therefore, improve your decision-making skills to ensure your business competes with an edge. You can take the lead and tap opportunities before others by making quick and smart business decisions without delay.

Big Data is a reliable resource when it comes to making quick business decisions. Over the years, Big Data has proven to be a crucial tool for insight discovery. It helps businesses and teams smoothly and efficiently perform multiple tasks on one platform, pre-process stored data, analyze, and visualize it. However, managed Amazon service like AWS Glue is often the preferred choice for handling big data.

AWS Glue- What Is It?

It is a serverless data integration service that seamlessly prepares and combines data for machine learning, analytics, and app development. It offers all the necessary capabilities for smooth data integration. This service provides code and visual-based interfaces, making data integration easier and more convenient.

AWS Glue is a fully managed Extract, Transform and Load solution that helps users a lot of features like:

  • Data categorization
  • Cleaning, enriching, and efficiently transferring data between different data streams and stores.

Moreover, this multifaceted system has three crucial components for added performance and functionality. These include:

  • AWS Data Catalog designed to hold information
  • Job Scheduling System for automating and assembling ETL pipelines
  • ETL Engine helps with customized code generation

These components allow ETL developers and data engineers to perform a wide range of tasks such as:

  • Create and monitor workflows
  • Recognize database schema changes
  • Generate ETL scripts
  • Scale resources automatically to manage ever-changing needs
  • Execute ETL tasks based on schedules, triggers, and specific events

Overall, AWS Glue results in an accelerated timeframe for operational ETL pipelines. However, as there is so much that this service offers that many users are often unaware or overlook specific caveats of AWS Glue and end up making rookie mistakes.

In this post, we share a list of rookie mistakes to help you avoid them when using AWS Glue. Please note that paying special attention to the source data can avoid many errors and issues across the data pipeline.

Mistake#1- Using the Wrong File Format

AWS Glue is a service based on Apache Spark. One of the key fundamentals of Spark is to input data in splittable files. Splittable formats include Parquet, ORC, and CSV, while non-splittable file formats include JSON and XML. Selecting the wrong file format can lower your performance and efficiency.

Therefore, the best way to avoid this mistake is to focus on the codec type. The dataset you want to input will influence whether you should select splittable or non-splittable file type. However, it is advisable to use the Parquet format for maximum efficiency and performance.

Mistake# 2 – Failure to Verify Data Quality

Failure to verify the quality of your data can result in misinterpretation. Moreover, you may face more issues if your file is not encoded with UTF8. So, if you want to avoid this mistake, consider using AWS Glue DataBrew. You can use this data preparation tool to explore dataset quality and include data cleansing and verification. Using this data will help you handle and manage bad records in your ETL pipeline and reconcile them successfully.

Mistake#3- Failure to Crawl Data Subsets

AWS Glue crawlers perform discovery over datasets. This tool crawls the data stores, keeping track of the previously crawled and changed data. It further helps update the data catalog and record the latest changes. However, failure to enable the crawler to make incremental crawls can cause problems.

This mistake will cause the crawlers to perform data discovery on the files it has already crawled. Not only will this issue cause the organization to lose more time, but it will also lead to errors.

So, make sure to enable crawler by marking the option ‘crawl only new folders’. Also, use small crawlers for data subsets instead of large crawlers that point at the parent location.

Mistake #4- Not Optimizing the Configuration of Crawler Discovery

If you don’t optimally configure crawler discovery, it can lead to confusion. You may end up making more tables than needed. The pro tip here is to select the option ‘create single schema for S3 path’.

Upon selection, the crawler will assess the similarities between schema and data compatibility and group the data into one table. This organization will ensure a clean and crisp representation of data, making it easier for you to understand and interpret for better and quality decision-making.

Mistake# 5- Incorrect Use of Dynamic Frames

Another common mistake is the incorrect use of Dynamic Frames. A Dynamic Frame is a feature included in the AWS Glue service that offers several optimizations. However, to use it, you need to specify a schema. Make two passes over your data to specify — one is required to infer the schema, while the second is for loading data. With Dynamic Frames, you can handle large values in the dataset with the utmost ease.

However, you can’t perform some operations by Dynamic Frames. For them, you will need Data Frames. Therefore, make sure to use Dynamic Frames carefully. Your goal should be to start with Dynamic Frames and then convert them into Data Frame. This method works for complex methods.

Mistake #6- Incorrect Partition

One of the most significant contributors to the superior performance of your ETL pipeline is the correct partition of data. Here it is essential to understand that problems with partition may occur if you are a new user relying on generated scripts. Generated scripts don’t partition job output by default. Hence, be cautious. When writing job output and generating scripts, use the partition pruning feature to create a partition and avoid going wrong.


AWS Glue is a value-adding, robust, and powerful service that can benefit you only if you follow the best practices and avoid rookie mistakes. Use it to your advantage for quick data integration and better decision-making.

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