AWS Glue: Meaning, Cost, Uses, Features, Components

Safalta Expert Published by: Aryan Rana Updated Sat, 17 Sep 2022 12:26 AM IST


The largest provider of cloud services globally is Amazon Web Services (AWS). One of its best products, AWS Glue, provides the best serverless cloud computing. You will discover what AWS Glue is, its architecture, features, how it functions, and more through this course.

Table of Content

1. Describe AWS Glue
2. AWS Glue Cost
3. When Should I Use AWS Glue?
4. AWS Glue's characteristics
5. AWS Components Glue
6. AWS Glue Data Catalogue
7. Classifier
8. Connection
9.  Crawler
10. Database
11. Data Center
12. Data source
13. Data Target
14. Transform
15. Development Endpoint
16. Dynamic Frame
17. Job
18. Trigger
19. Laptop Server
20. Script
21. Table
22. AWS Glue Architecture
23. Benefits and Drawbacks of AWS Glue





The use of managed data integration services by enterprises has increased demand for Amazon Glue. Glue is primarily used by data engineers and ETL developers to design, implement, and track ETL operations.  If you are interested in AWS Cloud or Cloud Computing and want to learn these interesting courses then click on the links mentioned AWS Cloud Practitioner Course and Cloud Computing Essentials

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Describe AWS Glue

Discovering, preparing, and integrating data for data analysis, machine learning, and application development is easy with AWS Glue, a serverless data integration and ETL service. Both visual and code-based tools are provided by Glue to make the data integration process more seamless.

The AWS Glue Data Catalog, an ETL engine that generates Python or Scala code automatically, and a customizable scheduler that handles dependencies, task monitoring, and restarts make up Amazon Glue.

Users may find and access data fast using the Glue Data Catalog.


The Glue service also offers customization, orchestration, and monitoring of complex data streams.

AWS Glue Cost

The cost of Amazon Glue starts at $0.44. There are four unique plans offered here:

  • ETL tasks and development endpoints are offered for $0.44.
  • Crawlers and DataBrew interactive sessions are offered for $0.44.
  • At DataBrew, salaries begin at $0.48.
  • The cost of the Data Catalog's requests and monthly storage is $1.00.
  • For the Glue service in AWS, there is no free plan. Each hour will cost roughly $0.44 per DPU.

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    Therefore, you would need to spend $21 every day on average. However, costs can differ regionally.

When Should I Use AWS Glue?

Knowing everything there is to know about Amazon Glue is not sufficient; you also need to know how and when to use it. You should think about the following AWS Glue use scenarios.
  • Utilizing Glue, you can conduct serverless queries across the Amazon S3 data lake. With no need to move your data, Amazon Glue can help you get going right away by making it all accessible at one interface for analysis.
  • Utilize Amazon Glue to understand your data assets. Finding different AWS data sets is simple with the Data Catalog. Additionally, you can preserve your data using this Data Catalog across many AWS services while keeping a consistent view of your data.
  • Glue is helpful when creating event-driven ETL procedures. You can carry out your ETL operations as soon as new data becomes available in Amazon S3 by calling your Glue ETL activities from an AWS Lambda service.
  • In order to prepare data for storage in a data warehouse or data lake, AWS Glue is also helpful for organizing, cleaning, verifying, and formatting data.

AWS Glue's characteristics

All of the tools you'll need for data integration are provided by Amazon Glue, allowing you to gain insights and use your expertise to make new developments in minutes as opposed to months. You should be aware of the features listed below.

Drag & Drop Interface: You may define the ETL process using a drag-and-drop job editor, and AWS Glue will immediately develop the code to extract, convert, and upload the data.
Automatic Schema Discovery: You can build crawlers that connect to numerous data sources using the Glue service. It effectively organizes the data, extracts information about the scheme, and puts it in the data catalogue. Then, ETL tasks could use this information to keep an eye on the ETL procedures.
Job Scheduling: Glue can be applied on-demand, in accordance with a schedule, or in response to an occasion. The scheduler can also be used to build complex ETL pipelines by setting up dependencies between activities.
Code generation: Glue Elastic Views makes it simple to develop materialized views that aggregate and replicate data across several data stores without the need to write custom code.
Built-in machine learning: The "FindMatches" function of Glue is a built-in machine learning capability. It finds duplicate records and removes the inaccuracies from them.
Developer Endpoints: If you want to actively build your ETL code, Glue provides developer endpoints that you may use to alter, debug, and test the code it has written.
DataBrew Glue: It is a solution for data preparation that helps users like data analysts and data scientists clean and normalize data using the active and visible interface of Glue DataBrew.

AWS Components Glue

We must first grasp a few components in order to comprehend the architecture of Glue. The interaction of many components is how AWS Glue designs and maintains your ETL pipeline. The foundational elements of the Glue architecture are listed below.

AWS Glue Data Catalogue

Permanent metadata is kept in the Glue Data Catalog. It offers table, job, and other control data to keep your Glue environment up to date. Each account on AWS is given access to one Glue Data Catalog across all regions.


Your data's schema is determined by a classifier, which is known as a classifier. For popular relational database management systems and file types like CSV, JSON, AVRO, XML, and others, AWS Glue offers classifiers.


The Data Catalog object called AWS Glue Connection contains the properties required to connect to a certain data storage.


It is a component that browses multiple data sources at once. It uses a prioritized set of classifiers to determine the schema for your data before generating metadata tables for the Glue Data Catalog.


A database is a structured collection of connected Data Catalog table definitions.

Data Center

You can store your data in a data storage for a long period of time. Two examples are relational databases and Amazon S3 buckets.

Data source

An assortment of data used as input in a process or transformation is known as a data source.

Data Target

The location where the operation writes the changed data is known as a data target.


The logic in the code called "transform" is used to alter the data's format.

Development Endpoint

You can create and test your AWS Glue ETL programs using the development endpoint environment.

Dynamic Frame

In contrast to a DataFrame, a DynamicFrame has self-descriptive entries for each row. Consequently, a schema is not required at first. Furthermore, Dynamic Frame has a variety of complex ETL and data cleansing procedures.


For ETL work, an AWS Glue Job is a piece of business logic that is required. The parts of a task are a transformation script, data sources, and data targets.


A trigger initiates an ETL procedure. Triggers can be configured to take place at a predetermined time or in reaction to an event.

Laptop Server

It is a web-based environment where PySpark instructions can be executed. A notebook enables the active writing and testing of ETL scripts on a development endpoint.


A piece of code known as a script is used to extract data from sources, modify it, and load it into target locations. Scripts written in PySpark or Scala are produced via AWS Glue. Amazon Glue provides notebooks and Apache Zeppelin notebook servers.


A table is the metadata specification for the data in data storage. A table is used to contain various pieces of metadata about a base dataset, including column names, data type definitions, partition details, and other information.

AWS Glue Architecture

You create tasks in AWS Glue to perform the data extraction, transformation, and loading (ETL) process from a data source to a data destination. You must take the actions listed below:
  • You must first choose the data source you will be using.
  • If you're utilizing a data storage source, you'll need to create a crawler to upload metadata table definitions to the AWS Glue Data Catalog.
  • Your crawler adds metadata to the Data Catalog when you point it to a data store.
  • Alternatively, you must explicitly establish Data Catalog tables and data stream characteristics if you're using streaming sources.
  • The data will be instantly accessible, queryable, and available for ETL after the Data Catalog has been categorized.
  • The data is then converted by creating a script using AWS Glue. To provide the script, you may either utilize the Glue console or API. The script runs in an Apache Spark environment in AWS Glue.
  • You have the option to schedule the job to start when a specific event occurs or run it immediately after creating the script. The trigger can be an event or a time-based schedule.
  • As you execute the task, the script will load the data to the data target as depicted in the above graphic after extracting it from the data source, transforming it, and loading it. In this manner, the AWS Glue ETL(Extract, Transform, Load) task is successful.

Benefits and Drawbacks of AWS Glue

AWS Glue is no different from other big data computing tools in that it offers both benefits and drawbacks.

These are a few advantages of AWS Glue:
  • A serverless data integration solution called Glue does away with the need to build and maintain infrastructure.
  • It offers straightforward tools for creating and managing work tasks that are activated on demand, by events and schedules, or both.
  • It is a financially sensible choice. Only the resources you use during the course of running jobs must be paid for.
  • Glue generates ETL pipeline code in Scala or Python automatically based on your data sources and destinations.
  • Several departments within the corporation may work together on various data integration initiatives using AWS Glue. The time it takes to examine the data is decreased as a result.
Glue has a lot of unique features, but it also has several shortcomings. So let's investigate some of AWS Glue's restrictions.
  • Integration Limitations exist with Glue. Glue only functions successfully with ETL from JDBC and S3 (CSV) data sources. Glue is unable to help if you wish to load data from other cloud services like File Storage Base.
  • The glue is not used to regulate individual table tasks. Only the entire database is processed using the ETL procedure.
  • AWS Glue only supports a small number of data sources, including S3. This makes gradual synchronization with the data source impossible. This means that for intricate procedures, real-time data won't be possible.
  • Only two programming languages, Python and Scala, are supported by AWS Glue for editing ETL scripts.


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