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Table of contents
1.
Introduction
1.1.
How It Works
1.2.
The following are the many components of the AWS Glue Architecture:
1.2.1.
1) Amazon Web Services Management Console
1.2.2.
2) AWS Glue Data Catalog
1.2.3.
3) AWS crawlers
1.2.4.
4)AWS ETL Operations
1.2.5.
5) Jobs Scheduling System
2.
AWS Glue Concepts
3.
Features of AWS Glue
3.1.
1. Build event-driven ETL pipelines
3.2.
2. Create a unified catalog to find data across multiple data stores
3.3.
3. Create, run, and monitor ETL jobs without coding
3.4.
4. Explore data with self-service visual data preparation
3.5.
5. Build materialized views to combine and replicate data
4.
Benefits of AWS Glue
5.
Limitation of AWS Glue
6.
Frequently Asked Questions
6.1.
What is AWS Glue?
6.2.
Why do we use AWS Glue?
6.3.
What are the key features of AWS glue?
6.4.
What are the limitations of AWS glue?
7.
Conclusion
Last Updated: Mar 27, 2024

AWS Glue

Author Avinash Pandey
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Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

Introduction

In this blog, we will learn about AWS Glue in-depth, so let's begin with a brief introduction to AWS glue. AWS Glue is a serverless ETL solution. ETL stands for extract, transform, and load, and it refers to three procedures often used in Data Analytics and Machine Learning. It gives businesses a data integration tool that prepares data from multiple sources and organizes it in a central repository where it can be used to make business decisions. Data analysis and categorization is one of its primary capabilities.

How It Works

AWS Glue manages all ETL data transfer and transformation into Data Lakes like Amazon S3 and Data Warehouses like Amazon Redshift using other AWS services. It uses APIs to collect data from many sources and then transform it to complete Data Integration tasks.

ETL jobs can be scheduled or have trigger events configured to launch them. It generates code depending on the user's input and then extracts and transforms data automatically using the code. The code in the scripts can be changed as needed. The metadata from the job is then written to Data Catalog, which serves as a metadata repository.

Since we have discussed AWS glue and how it works, let's move on to its architecture.

The following are the many components of the AWS Glue Architecture:

1) Amazon Web Services Management Console

The AWS Management Console is a web application for managing AWS resources in your browser. It includes the following features:

  • Crawlers, jobs, tables, and connections are examples of AWS Glue objects.
  • Creates a working layout for crawlers.
  • Job trigger events and timelines are created.
  • Searches and filters Glue objects to Amazon Web Services.
  • Scripts for transformation scenarios are edited. 

2) AWS Glue Data Catalog

AWS Glue Data Catalog provides a centralized standard metadata storage solution for data tracking, querying, and transformation.

3) AWS crawlers

Crawlers and classifiers scan data from various sources, classify it, detect schema information, and store it in the AWS Glue Data Catalog.

4)AWS ETL Operations

The ETL program generates Python or Scala code for data cleaning, enrichment, duplicate removal, and other complex data transformation tasks.

5) Jobs Scheduling System

A flexible scheduling system is in charge of starting jobs based on different events or timetables.

AWS Glue Concepts

In AWS Glue, you define tasks to extract, transform, and load (ETL) data from a data source to a target. The following are typical actions you take:

  1. You construct a crawler to populate your AWS Glue Data Catalog with metadata table definitions for data storage sources. When you aim your crawler at a data store, the crawler generates table definitions in the data catalog. You define Data Catalog tables, and data stream attributes manually for streaming sources.
    The AWS Glue Data Catalog also contains other metadata that is required to define ETL operations, in addition to table definitions. When you describe a job to alter your data, you use this metadata.
     
  2. AWS Glue may build a script for data transformation. You can also use the AWS Glue console or API to provide the script.
     
  3. You can run your job on-demand or schedule it to begin when a specific event occurs. A timer or an event can be used as the trigger. A script extracts data from your data source, alters it, and loads it to your data target when your job executes. In AWS Glue, the script runs in an Apache Spark environment.

Source

Now that we have seen the concepts of AWS glue let's look at some key features.

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Features of AWS Glue

1. Build event-driven ETL pipelines

As new data comes in, AWS Glue can perform your ETL processes. For example, you can utilize an AWS Lambda function to run your ETL operations every time new data in Amazon S3 becomes available. You may also register this new dataset in the AWS Glue Data Catalog as part of your ETL processes.

2. Create a unified catalog to find data across multiple data stores

The AWS Glue Data Catalog allows you to quickly find and search across numerous AWS data sets without moving the data. Once the data has been cataloged, it may be searched and queried immediately using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

3. Create, run, and monitor ETL jobs without coding

AWS Glue provides both visual and code-based interfaces to make data integration easier. AWS Glue Studio makes visualizing, running, and monitoring AWS Glue ETL tasks simple. You can use a drag-and-drop editor to create ETL jobs that move and convert data, and AWS Glue will build the code for you. The AWS Glue Studio job run dashboard may then monitor ETL execution and confirm that your jobs are running as expected.

4. Explore data with self-service visual data preparation

AWS Glue DataBrew is a new visual data preparation tool that cleans and normalizes data for analytics and machine learning easy for data analysts and data scientists. You can choose from over 250 pre-built transformations to automate data preparation tasks without writing any code. Filtering anomalies, translating data to standard formats, and fixing erroneous numbers, among other duties, can all be automated.

5. Build materialized views to combine and replicate data

Using familiar SQL, you may create materialized views with AWS Glue Elastic Views. Use these views to mix data from many sources and maintain it current and available from a target data store. Amazon DynamoDB is presently supported by the AWS Glue Elastic Views preview, with support for Amazon Aurora and Amazon RDS to follow.

 

Benefits of AWS Glue

  • ETL task execution does not require any infrastructure setup or maintenance. All of the low-level specifics are handled by Amazon. AWS Glue also automates many processes. You can identify the data schema fast, write code, and begin modifying it. Logging, monitoring, alerting, and restarting in failure circumstances are made easier using AWS Glue.
  • It complements Amazon's other services. As a result, Amazon Kinesis, Amazon Redshift, Amazon S3, and Amazon MSK are all simple to integrate with AWS Glue. It's also compatible with other popular data storage deployed on Amazon EC2 instances.
  • You don't need to manage any infrastructure because it works in a serverless environment. The resources required to accomplish data consolidation operations are managed, configured, and scaled by AWS Glue.
  • It is cheaper because you only have to pay for the resources that the job consumes while it is running.

 

Although Amazon Glue is an efficient tool, we have seen it, as discussed above, still faces certain limitations, which we will discuss below.

Limitation of AWS Glue

While AWS glue has several outstanding features, it also has some significant drawbacks.

  • Glue has only a few pre-built components compared to other ETL alternatives available today. It is also not open to fit all kinds of environments because it was designed by and for the AWS Console.
  • AWS Glue uses Apache Spark to conduct jobs. This means that engineers who need to modify the generated ETL task should be very familiar with Spark. The code will be written in Scala or Python; therefore, developers should be familiar with those languages in addition to Spark. This means that not all data professionals will be able to customize ETL jobs for their specific needs.
  • Only ETL from JDBC and S3 (CSV) data sources works properly with Glue. If you want to load data from other cloud services, such as File Storage Base, Glue will not be able to support it.

Frequently Asked Questions

What is AWS Glue?

AWS Glue is a serverless and event-driven ETL solution. ETL stands for extract, transform and load; it refers to three procedures often used in Data Analytics and Machine Learning. It automates much of the effort required for data integration.

Why do we use AWS Glue?

AWS Glue crawls your data sources, identifies data formats, and suggests schemas to store your data. Data analysis and categorization is one of its primary capabilities. It generates the code needed to perform your data transformations and loading operations automatically.

What are the key features of AWS glue?

Integrated data catalog, automatic discovery of schema, automatic code generation, building event-driven ETL pipelines, and self-service visual data preparation are some of the key features of AWS glue.

What are the limitations of AWS glue?

It is also not open to fit all kinds of environments because it was designed by and for the AWS Console. One should be very familiar with Spark to modify ETL tasks because AWS Glue uses Apache Spark to conduct jobs.

Conclusion

In this blog, we extensively learned about what AWS glue is, how it works, the main components of its architecture, and its concepts, features, benefits, and limitations.

Refer to AWS glue to know more about AWS glue and how it works. You can also refer to Best practices when using Athena with Glue to learn how to integrate AWS Athena with AWS glue.

I hope you have enjoyed reading it.

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