AWS Glue ETL (original) (raw)

Last Updated : 23 Jul, 2025

The **Extract, Transform, Load(ETL) process has been designed specifically to transfer data from its source database to the data warehouse. However, the challenges and complexities of ETL can make it hard to implement them successfully for all our enterprise data. For this reason, Amazon has introduced AWS Glue.

**AWS Glue is a fully managed ETL service that makes it simple and cost-effective to categorize our data, clean it, enrich it, and move it reliably between various data stores. AWS Glue is used to prepare data from different sources and prepare that data for analytics, machine learning, and application development. It will reduce the manual effort by performing the automation of the jobs like data integration, data transformation, and data loading. AWS glue is a serverless data integration service which makes it more useful for the preparation of the data also the data that has been prepared will be maintained centrally in a catalog which makes it easy to find and understand the data.

How to Use AWS Glue ETL?

Step 1: Create and Attach an IAM Role for your ETL Job

Identity and Access Management (IAM) manages Amazon Web Services (AWS) users and their access to AWS accounts and services. It controls the level of access a user can have over an AWS account & sets users, grants permission, and allows a user to use different features of an AWS account.

Step 2: **Create a Crawler

AWS Glue's main job was to create a data catalog from the data it had collected from the different data sources. Crawler is the best program used to discover the data automatically and it will index the data source which can be further used by the AWS Glue.

Step 3: Create a Job

Create a job in AWS Glue to create a job follow the steps mentioned below.

Open AWS console and navigate to the AWS glue and click on the create job.
Make all the configuration required for the job and click on the create job.

Step 4: Run your job

After creating the job select the job that you want to run and Click Run job.

Step 5: Monitor your job

You can monitor the progress of the job in AWS Glue console.

Best Practices For AWS Glue ETL

Following are the some of the best practices that you can follow while implementing the AWS Glue ETL.

**Data Catalog: Use data catalog as an centralized metadata repository try to store all the metadata about the data sources, transformations, and targets.
**Crawlers: You need to keep you metadata uptodate for that you can use the crawler to to run the periodically which keeps the metadata up to date.
**Leverage Dynamic Allocators: Dynamic allocates are used to scale up and scale down the workers and executors based up on the load which will store lots of resources.
**Utilize Bulk Loading: Try to use the bulk loading teefforts of chnique which is more efficient educing the number of individual file writes and improving overall performance.
**Monitor and Analyze Job Metrics: WIth the help of cloudwatch you can monitor the performance of the Glue. You can monitor the job metrics such as execution time, resource utilization, and errors, to identify performance bottlenecks and potential issues.

Case Studies of AWS Glue ETL

Following are the some of the organization that are using the AWS glue ETL. To Know How to create AWS Account refer to the Amazon Web Services (AWS) – Free Tier Account Set up.

Media and Entertainment: Media company will produces lots of video content which need to be transferred and catalog their data efficeiently. In that AWS Glue for ETL process and organize the metadata, making it searchable and accessible for content delivery.
**Retail: The companies which are in the retail industry will consists of multiple online and offline sales they can use AWS Glue for ETL to consolidate and analyze customer data from various sources. You can gain more insights of overall coustmer experience.
**Healthcare: AWS Glue for ETL was used by a healthcare organisation with a variety of data sources, including IoT devices and electronic health records, to combine and analyse patient data. This enhanced patient care by streamlining data processing for medical research.
**Financial Services: You can analyze the patient data which can be further used for the medical research and improved patient care.
**Travel and Hospitality: Travel companies will manage there data like customer reviews and pricing of the bus ticket can be used AWS glue for ETL to centralize and harmonize their data.

Future of AWS Glue ETL

**Enhanced Machine Learning Integration: You can integrate with other service in the AWS like SageMaker, ML models in the amazon console. The AWS Glue can automate the data and feature engineering for machine learning models.
**Real-Time Data Processing: AWS glue can enhance the real time data which can be used for crucial requirements of the applications which requires immediate insights from data streams.
**Serverless Architecture Expansion: The serverless architecture of AWS Glue will keep growing, offering even more precise control over resource distribution and cost reduction. This will guarantee effective resource utilisation by enabling users to scale their ETL processes in accordance with exact requirements.
**Advanced Data Transformation: The feature is all about data AWS glue may introduce the features like data cleansing, enrichment and analysis to support increasingly complex ETL requirements.

AWS Glue Architecture

We define jobs in AWS Glue to accomplish the work that is required to extract, transform and load data from a data source to a data target. So if we talk about the workflow, the first step here is we define a crawler to populate our AWS data catalog with metadata and table definitions. We point our crawler at a data source post and the crawler creates table definitions in the data catalog. In addition to table definitions, the data catalog contains other metadata that is required to define ETL jobs. we use this metadata when we define a job to transform our data in the second step. AWS Glue can generate a script to transform our data or we can also provide the script in the AWS Glue console. In the third step, we can run our job on demand or we can set it up to start when a specified trigger occurs. The trigger can be a time-based schedule or an event. Finally, when our job runs, a script extracts data from our data source, transforms the data, and loads it into our target. The script runs in an Apache Spark environment in AWS Glue.

**Data Catalog: It is the persistent metadata store in AWS Glue. It contains table definitions, job definitions, etc. AWS Glue has one data catalog per region.
**Database: It is a set of associated data catalog table definitions organized into a logical group in the AWS group.
**Crawler: It is a program that connects to our data store. Maybe a source or a target progresses through a prioritized list of classifiers to determine the schema for our data and then it creates metadata tables in the data catalog.
**Connection: AWS Glue Connection is the data catalog that holds the information needed to connect to a certain data storage.
**Classifier: It determines the schema of our data. AWS Glue provides classifiers for common file types such as CSV, JSON, etc. It also provides classifiers for common relational database management systems using a JDBC connection.
**Data Store: It is a repository for persistently storing our data. Examples include Amazon S3, buckets, and relational databases.

AWS Glue

**Data Source: It is a target data store that is used as an input to process or transform.
**Data Target: It is a data store where the transformed data is written.
**Development Endpoint: It is an environment where we can develop and test our AWS Glue ETL scripts.
**Job: It is a business logic required to perform the ETL work It is composed of a transformation script data sources and data targets. They can be initiated by triggers that can be scheduled or triggered by events.
**Trigger: It initiates an ETL job. We can define triggers based on a scheduled time or an event.
**Notebook Server: It is a web-based environment that we can use to run our PySpark statements, which is a Python dialect used for ETL programming.
**Script: It contains the code that extracts data from sources transforms it and loads it into the targets.
**Table: It contains the name of columns, data types, definitions, and other metadata about a base dataset.
**Transform: We use the code logic to manipulate our data into different formats using the transform.

Use Cases of AWS Glue

**To build Data Warehouse to Organize, Cleanse, Validate, and Format Data: We can transform and move AWS cloud data into our data store. We can also load data from different sources into our data warehouse for regular reporting and analysis. By storing it in the warehouse, we integrate information from different parts of our business and form a common source of data for decision-making.
**When we run Serverless Queries against our Amazon S3 Data Link: S3 here means simple storage service. AWS Glue can catalog our simple storage service that is Amazon S3 data making it available for querying with Amazon Athena and Amazon RedShift Spectrum. With crawlers, our metadata stays in synchronization with the underlying data. AWS RedShift Spectrum can access and analyze data through one unified interface without loading it into multiple data.
**Creating event-driven ETL Pipelines: We can run our ETL jobs as soon as new data becomes available in Amazon S3 by invoking our AWS Glue ETL jobs from an AWS Lambda function. We can also register this new data in the AWS load data catalog as a part of our details.
**To understand our Data Assets: We can store our data using various AWS services and still maintain a unique, unified view of our data using the AWS Glue data catalog. We can view the data catalog to quickly search and discover the datasets that we own and maintain the relative data in one central location.

Benefits of AWS Glue

**Less Hassle: AWS Glue is integrated across a wide range of AWS services. AWS Glue natively supports data stored in Amazon Aurora and other Amazon Relational Database Service engines, Amazon RedShift and Amazon S3 along with common database engines and databases in our virtual private cloud running on Amazon EC2.
**Cost Effective: AWS Glue is serverless. There is no infrastructure to provision or manage AWS Glue handles, provisioning, configuration, and scaling of the resources required to run our ETL jobs. We only pay for the resources that we use while our jobs are running.
**More Power: AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. It identifies data formats and suggests schemas and transformations. Glue automatically generates the code to execute our data transformations and loading processes.

Disadvantages of AWS Glue

**Amount of Work Involved: It is not a full-fledged ETL service. Hence in order to customize the services as per our requirements, we need experienced and skillful candidates. And it involves a huge amount of work to be done as well.
**Platform Compatibility: AWS Glue is specifically made for the AWS console and its subsidiaries. And hence it isn’t compatible with other technologies.
**Limited Data Sources: It only supports limited data sources like S3 and JDBC
**High Skillset Requirement: AWS Glue is a serverless application, and it is still a new technology. Hence, the skillset required to implement and operate the AWS Glue is high.

AWS Glue Pricing

To measure AWS Glue costs, you need to focus on several key factors that influence pricing:

1. Data Processing Unit (DPU) Usage

AWS Glue charges are based on the amount of processing power you use, measured in Data Processing Units (DPUs). A DPU includes 4 vCPUs and 16 GB of memory.
The cost is calculated per DPU-hour, but you’re billed by the second. So, if you run a job with 10 DPUs for 30 minutes, you'll be charged for 5 DPU-hours.
In the US East region, for example, AWS Glue costs around ****$0.44 per DPU-hour.**

2. Crawlers

AWS Glue crawlers scan your data to detect its structure and add the schema to the Glue Data Catalog. You pay based on how long it takes the crawler to process the data, which is also measured in DPU-hours.

3. Data Catalog

The AWS Glue Data Catalog is free for the first 1 million objects you store in it. After that, there’s a charge for every additional million objects.
You may also be charged for API requests to interact with the Data Catalog.

4. Glue Studio

If you use AWS Glue Studio to visually create jobs, the costs are similar to regular jobs. The price depends on how many DPUs are needed for your job.

5. DataBrew

AWS Glue DataBrew charges based on "node-hours." A node is a computing resource with 4 vCPUs and 16 GB of memory, and costs are based on how long the data transformation takes.

6. Other Factors

Job Duration: The longer a job runs, the more DPU hours you use.
**Number of Jobs: Running multiple jobs at once will increase the DPU usage, which increases costs.
**Region: AWS Glue pricing can vary depending on the AWS region you're using.

7. Managing Costs

**AWS Cost Explorer: Use this tool to monitor your Glue costs over time.
AWS Budgets: Set up spending alerts to stay within your budget.
**Pricing Calculator: AWS provides a calculator that helps you estimate costs based on your expected usage.

AWS Glue vs. EMR

The following table will tell you about the major difference between AWS Glue and EMR

Feature	AWS Glue	AWS EMR
Type of Service	Serverless ETL (Extract, Transform, Load) service.	Managed big data platform for processing large data sets.
Pricing Model	Pay-as-you-go; no need for extensive infrastructure.	Requires custom EC2 instance clusters; involves setting up infrastructure.
Server Requirement	No servers needed, serverless by design.	Requires setting up and managing EC2 instances.
Flexibility	Less flexibility compared to EMR. Simplified for ETL jobs.	Offers high flexibility with custom configurations and support for Hadoop ecosystem.
Cost	Generally more expensive for similar configurations. Costs around 21perDPUforafullday.∣Lessexpensive.Costsaround21 per DPU for a full day.	Less expensive. Costs around 21perDPUforafullday.∣Lessexpensive.Costsaround14-$16 for similar configurations per day.
Automation	Automates ETL job writing, monitoring, and execution.	Provides flexibility to configure and control data processing clusters.
Setup Complexity	Easy to get started; minimal setup required.	Requires extensive infrastructure setup, which can be costly.
Power and Capability	Suitable for ETL tasks but lacks the raw power and flexibility of EMR.	More powerful and flexible, suited for heavy data processing and machine learning tasks.
Common Use Cases	Ideal for automating ETL tasks without the need for custom infrastructure.	Best for advanced data processing tasks, SQL queries, and machine learning on large datasets.
Cluster Customization	No custom cluster setup required.	Customizable clusters for tailored performance
Replacement Possibility	Cannot be replaced by EMR directly.	Can replace AWS Glue in certain use cases.

After discussing about difference between AWS Glue and AWS EMR let's discuss about the key differences between AWS Glue, AWS Batch and AWS Data Pipeline.

AWS Glue vs Batch vs Data Pipeline

The following table illustrates about the key differences between AWS Glue, AWS Batch and AWS Data Pipeline

Feature	AWS Glue	AWS Batch	AWS Data Pipeline
Service Type	Serverless service that handles data processing tasks (ETL).	Manages and runs large-scale batch processing jobs.	Orchestrates and automates data workflows across systems.
Main Use Case	Ideal for transforming, preparing, and moving data.	Best for running compute-heavy jobs like scientific calculations or data analysis.	Moves and processes data between AWS and on-premise resources.
Automation	Automates ETL jobs, including scheduling and monitoring.	Automatically allocates resources for running batch jobs.	Automatically triggers workflows based on time or data changes.
Infrastructure	You don't need to manage servers; AWS handles it.	Requires setting up and managing EC2 instances.	Can run on both AWS and on-premise infrastructure.
Processing Capabilities	Built for large-scale data transformations.	Suited for running high-performance, large-scale computing jobs.	Handles data transfers and complex workflows.
Flexibility	Easier to use but more limited to ETL tasks.	Very flexible for running different types of batch jobs.	Flexible for building complex workflows with custom dependencies.
Pricing	Pay for the amount of data processed (billed by the hour).	Pay for the computing resources used (like EC2 instances).	You pay based on the AWS services used and the number of tasks executed.
Scheduling Jobs	Jobs are scheduled based on data arrival or a set time.	Jobs are scheduled in queues, and run when resources are available.	Jobs can be triggered by time schedules or data events.
Ease of Use	Simple to set up for ETL tasks, no servers to manage.	Requires more setup for defining jobs and resources.	Requires defining workflows and job dependencies.
Scaling	Automatically scales based on job needs.	Scales resources like EC2 instances depending on job requirements.	Scales resources to handle data movement and workflow tasks.
Who Uses It	Data engineers and analysts who need to process and move data.	Developers or researchers running large-scale computing tasks.	Data engineers managing complex data workflows.

Features of AWS Glue

AWS Glue provides several key features designed to simplify and enhance data management and processing:

**Automated ETL Jobs: AWS Glue automatically runs ETL (Extract, Transform, Load) jobs when new data is added to your Amazon S3 buckets, ensuring that the latest data is processed without manual intervention.
**Data Catalog: With AWS Glue's Data Catalog, you can quickly search and browse data from various AWS sources without moving it. The data is ready to be queried immediately using services like Amazon Athena, Redshift Spectrum, and EMR.
**AWS Glue Studio: AWS Glue Studio offers a no-code option for creating and managing ETL jobs. Its visual editor allows users to build and monitor jobs with a simple drag-and-drop interface, while AWS Glue generates the underlying code to perform the tasks.
**Support for Multiple Data Processing Methods: Whether you're handling ETL, ELT, batch processing, or streaming data, AWS Glue supports a range of methods to suit your workflow. Users can choose between writing code, using the drag-and-drop interface, or integrating with notebooks.
**Data Quality Management: AWS Glue automatically creates and monitors data quality rules, helping maintain high data standards throughout your data lakes and pipelines.
**AWS Glue DataBrew: DataBrew enables users to explore and interact with their data from sources such as Amazon S3, Redshift, AWS Lake Formation, Aurora, and RDS. It includes more than 250 pre-built transformations to simplify data preparation tasks, like removing anomalies, fixing invalid values, and standardizing formats.