Key Points
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.
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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: 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.
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?
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
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
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
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.
