When AWS Glue launched in 2017, big data was already seen as a critical resource for a business. IT Firms have been using big data to drive success in various ways, and companies continue to adopt AWS Glue for data integration. Cloud platforms or hybrid clouds now make up 65% of organizations’ choices for data integration solutions. In this article, we explain what AWS Glue is, how it works, and when you might want to use it. We’ll also examine its advantages and disadvantages, explain some confusing terminology surrounding AWS Glue, and describe its core features.

AWS Glue: What is it?

AWS Glue is primarily a serverless ETL tool. Businesses use it to prepare data for analytics, application development, artificial intelligence, and machine learning: Extraction, Transformation, and Loading. Generally speaking, the ETL process collects raw data from sources, refines and aggregates it, and writes it to a repository or data warehouse for further processing and analysis,

AWS Glue simplifies all data integration procedures so you can use your data quickly. Rather than waiting around forever for a lengthy ETL process, you get to analyze and leverage the data in minutes. As a result of these capabilities, Glue is technically referred to as a fully-managed ETL solution for data integration. According to Amazon, the system is designed to provide an easy and cheap way to clean, enrich, and transfer data between various data streams. 

AWS Glue: Components

AWS Glue may look like magic at first glance, but a lot of complicated work goes on in the background. Thanks to Glue’s precise architecture, it can seamlessly handle the entire data integration process and communication between components. But to understand Glue’s architecture, we must first understand some of its essential components. 

AWS Glue Studio

tHE Studio is one of the primary components of Glue. Its principal function is a graphical interface for creating, executing, and monitoring data integration jobs in AWS Glue. Additionally, you can visually compose data transformation workflows and seamlessly run them on the Apache Spark-based serverless ETL engine. 

The Amazon Glue Studio helps developers extract, transform, load, and manage large-scale, datasets thanks to its intuitive interface. AWS Glue ETL workflows can be developed and managed using a boxes-and-arrows style visual interface that you can customize with code. 

It gives you a clear picture of your job runs and how they relate to each other in AWS Glue Studio. You can search and filter all job runs in one interface. With this view, you will always be aware of the ETL operations you are performing and the resources you are using. Additionally, Glue Studio’s real-time dashboard can be used to monitor and validate your job runs.

AWS Glue console

If the graphical user interface of Glue Studio isn’t your thing, you can fire up AWS Glue Console instead. With a full suite of tools to define and orchestrate ETL workflow, the Glue Console communicates with APIs in the Glue Data Catalog and Jobs database to automate your most mundane tasks.

You can define objects like jobs, tables, connections, and crawlers and handle every aspect of scheduling tasks and filtering object lists.

AWS Glue Data Catalog

As one of the most vital elements of any AWS Cloud account, the Glue Data Catalog stores your technical metadata. Since the Data Catalog is unique to your AWS account, it can integrate with your entire ecosystem to gather and analyze data. Generally speaking, Data Catalogs are simply collections of tables organized into databases

Glue Data Catalog allows disparate systems to store and find metadata in one place, enabling easier tracking. You can then use that data to query and transform data across various applications.

AWS Glue crawlers and classifiers

Another helpful feature of AWS Glue is the ability to set up crawlers and classifiers. A crawler is a component that crawls data sources and determines schema data using a set of predefined classifiers. A classifier is a term used to refer to the schema of your data. You can configure your own classifiers to manage relational databases and various file types like CSV, JSON, and more.

With the combined power of crawlers and classifiers, you can scan data in multiple repositories at once, classify it, and extract schema data from it to store in your Glue Data Catalog.

AWS Glue: When Should You Use It?

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Though AWS Glue serves different peole, it’s especially useful for organizations trying to build enterprise-class data warehouses. With AWS Glue, these companies can seamlessly move data from various sources into their data warehouse. 

In short, you use AWS Glue to validate, cleanse, organize, and format data, which is then stored in a central data warehouse. Also, enterprise users, in particular, benefit from being able to load data from both streaming and static sources. 

Many businesses use AWS Glue as a cataloging tool, praising its complete cloud support and the ability to be completely accessible from the web. Scanning network devices and preparing dashboards based on the data collected is another everyday use case for AWS Glue. Regardless of the specific case, Glue is one of the top choices for those looking for a straightforward and quick cloud-based ETL tool.

AWS Glue: Pricing Model

Amazon’s pricing model is very straightforward for AWS Glue. With a simple monthly fee, you can access all of the features like the Data Catalog and Studio. You’ll be billed per second for crawler usage, with a ten-minute minimum. 

The Amazon Glue pay-per-use fee of $0.44 per DPU, or “data processing unit,” might seem reasonable initially. Still, organizations often find themselves saddled with huge bills after prolonged use since that figure adds up quickly. If you rely heavily on Glue, your bills can cost thousands.

Using AWS Glue: What it is Like

How you use AWS Glue comes down to what kind of data you are gathering and what you are doing with it. Either way, the main goal of using Glue is processing your metadata. AWS Glue stores metadata in the Glue Data Catalog. This metadata is used to orchestrate ETL jobs that transform data sources and load your data warehouse or data lake. There are some options and a general workflow involved in using AWS Glue.

Using Crawlers to Populate the Data Catalog

The console allows you to add crawlers to populate the Glue Data Catalog for persistent data stores. Crawlers and tables can be selected from the list of crawlers to start the “Add crawler” wizard. Next, you’ll select one or more data stores your crawler will access. Additionally, creating a schedule allows you to determine how often your crawler should run. One thing to remember is that you may have to provide authentication depending on the location and type of your data.

Your crawler will read your data source and name tables and create definitions according to your Data Catalog configuration. You can organize these tables into a database of your choice. Additionally, you can manually create tables and populate the Data Catalog. 

One thing to note is that in this method, you define tables in the Data Catalog by providing the schema and other metadata. In many cases, it’s better to have a crawler create table definitions since this method can be tedious and error-prone.

Using AWS Glue ETL Operations

Amazon’s built-in ETL operations make using Glue even easier. You can automatically generate Scala and Python scripts with Glue extensions that you can use and modify to perform ETL operations. With your scripts, you can automate the whole process. Data can be extracted, cleaned, and transformed. Finally, it can be stored in an external repository where you can analyze it further. 

Since your scripts can be scheduled and chained using the Jobs manager, you can automate tasks to execute with the arrival of new data. This makes your job even easier when handling a massive amount of data.

AWS Glue: Pros and Cons

As with everything else in big data computing, Glue has its strengths and weaknesses. Amazon has done a great job crafting the perfect ETL application. Let’s look at the upsides and downsides.

Pros of AWS Glue

  • Serverless: You don’t have to build or maintain infrastructure.
  • Automated ETL scripts – you can save time by automating your most repetitive tasks.
  • Metadata repository – the Glue Data Catalog acts as a metadata repository, allowing you to track all your data assets effortlessly.
  • Manually develop endpoints: Advanced users can create their own ETL scripts with custom endpoints.
  • Pay-as-you-go pricing: You don’t need to commit to a long-term subscription plan to use AWS Glue, allowing you to only pay for what you need.

Cons of AWS Glue

  • AWS Glue is not entirely beginner friendly, and you’ll need to be competent in Spark to modify ETL jobs. Having Python and Scala expertise also comes in handy for working with scripts.
  • Only two programming languages are supported. You can only use Python or Scala in AWS Glue.
  • Integrations are limited. If you want to integrate with platforms outside of Amazon, it is very tricky with AWS Glue. You’ll mostly be limited to working within the AWS ecosystem.

AWS Glue Guide: Features, Benefits, and Pros and Cons FAQs (Frequently Asked Questions) 

What is AWS Glue used for?

AWS Glue is an ETL utility for extracting, transforming, and loading data. Whenever you need to cleanse and transform large amounts of data, such as for machine learning models or analytics, you can use AWS Glue.

Is AWS Glue difficult to learn? 

Since AWS Glue has Studio, a simple and easy graphical user interface, it is easy to get started. However, you’ll need to be able to read the documentation thoroughly to figure out more complex tasks.

What languages does AWS Glue use?

Currently, AWS Glue relies on two programming languages: Python and Scala.

When did AWS Glue come out?

AWS Glue was launched in 2017.

What is the alternative for AWS Glue? 

You can opt for competitors like Fivetran, Informatica, and Talend Data Integration.

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More from History-Computer

  • Amazon Available here: https://aws.amazon.com/glue/
  • Wikipedia Available here: https://en.wikipedia.org/wiki/AWS_Glue
  • Gartner Available here: https://www.gartner.com/reviews/market/data-integration-tools/vendor/amazon-web-services/product/aws-glue/reviews?marketSeoName=data-integration-tools&vendorSeoName=amazon-web-services&productSeoName=aws-glue
  • Keboola Available here: https://www.keboola.com/blog/etl-process-overview
  • CloudZero Available here: https://www.cloudzero.com/blog/aws-glue