More enterprises continue to adopt managed data integration services like AWS Glue. According to the Data Pipelines Market Study Report, 65% of organizations now prefer cloud-based or hybrid cloud data integration solutions.
And how exactly do they stand to benefit?
This article explains the craze by covering AWS Glue in detail, which happens to be one of the most popular cloud data integration services today.
Table Of Contents
What Is AWS Glue?
AWS Glue provides a serverless solution that simplifies the entire process of discovering, preparing, and combining data for application development, machine learning, and analytics.
To adequately define what AWS Glue is, you’ll first need to understand how data integration works.
In essence, you can think of data integration as the process of setting up and putting together data for analytics, application development, and machine learning.
It’s made up of multiple procedures – such as identifying and generating data from different sources, which is then followed by enriching, cleaning, normalizing, and merging data. In the end, the data is loaded and organized in data warehouses, databases, and data lakes.
AWS Glue facilitates all the data integration procedures so you can quickly put your merged data to good use. That means you get to analyze and leverage the data in minutes, instead of waiting around forever.
Considering these capabilities, AWS Glue is technically described as a fully-managed ETL (Extract, Transform, and Load) data integration solution.
Amazon even goes on to explain that the whole system is designed to provide an easy and cheap way to not only categorize your data, but also clean, enrich, and transfer it efficiently between different data streams and data stores.
It doesn’t run across one front, though. Rather, AWS Glue comes as a multi-faceted system that powers data integration through three core components.
AWS Glue features: What can the AWS data integration service do?
AWS Glue provides the following capabilities:
- Run ETL jobs as newly collected data arrives – AWS Glue, for instance, lets you automatically run ETL jobs when new data arrives in your Amazon Simple Storage Service (S3) buckets.
- Data Catalog – Use it to rapidly browse and search multiple AWS datasets without needing to move the data. The cataloged data is immediately searchable and queryable with Amazon Athena, Amazon Redshift Spectrum, and Amazon EMR.
- AWS Glue Studio -It supports no-code ETL jobs. AWS Glue Studio enables you to visually build, run, and monitor AWS Glue ETL jobs. Your ETL jobs can move and transform data with a drag-and-drop editor. AWS Glue auto-generates the code.
- Multi-method support – Supports a variety of data processing approaches and workloads, such as ETL, ELT, batch, and streaming. You can also use your favorite method, from drag and drop or writing code to connecting with your notebook.
- AWS Glue Data Quality – Creates, manages, and monitors data quality rules automatically. This ensures high-quality data throughout your data lakes and pipelines.
- AWS Glue DataBrew – This enables you to discover and interact with data directly from your data lake, data warehouses, and databases. You can do that with Amazon S3, Amazon Redshift, AWS Lake Formation, Amazon Aurora, and Amazon Relational Database Service (RDS).
Also, DataBrew has over 250 prebuilt transformations to automate data preparation operations including filtering anomalies, correcting invalid values, and standardizing formats.
What are the components of AWS Glue?
Under the hood, you’ll find these three AWS Glue components:
- The console – The AWS Glue console is where you define and orchestrate your workflow. There are several API operations you can call from here to perform tasks, such as defining AWS Glue objects, editing transformation scripts, and defining events or job schedules for job triggers.
- AWS Glue Data Catalog – This is basically a central repository for your metadata, built to hold information in metadata tables — with each table pointing to a single data store. In other words, it acts as an index to your data schema, location, and runtime metrics, which are then used to identify the targets and sources of your ETL (Extract, Transform, Load) jobs.
- Job Scheduling System – The job scheduling system, on the other hand, is intended to help you automate and chain your ETL pipelines. It comes in the form of a flexible scheduler that’s capable of setting up event-based triggers and job execution schedules.
- Script – The AWS Glue service generates a script to transform your data. Alternatively, you can upload your script via the AWS Glue API or console. Scripts extract data from your data source, transform it, and load it into your data target. In AWS Glue, the scripts run in an Apache Spark environment.
- Connection – This refers to a Data Catalog object that comprises properties for connecting to a given data store.
- Data store – This is where your data is stored persistently, such as relational databases and Amazon S3.
- Data source – This refers to a data store that serves as input to a transform or process.
- Data target – This is the data store that a process writes to.
- Transform – Refers to the code logic used to change the format of your data.
- ETL Engine – AWS Glue’s ETL engine is the one component that handles ETL code generation. It automatically provides this in Python or Scala, and then proceeds to even give you the option of customizing the code.
- Crawler and Classifier – A crawler helps retrieve data from the source through integrated or custom classifiers. This AWS Glue component creates or uses metadata tables pre-defined in the data catalog.
- Job – This is the business logic that performs an ETL task in AWS Glue. Internally, Apache Spark with Python or Scala writes the business logic.
- Trigger – This starts ETL job execution at a specific time or on-demand.
- Development endpoint – This creates a development environment in which your ETL job script can be developed, tested, and debugged.
- Database – It creates or accesses source and target databases.
- Table – You can create one or more tables in the database for use by the source and target.
- Notebook server – An online environment for running PySpark statements, a Python dialect for ETL programming. With AWS Glue extensions, you can run PySpark statements on a notebook server.
Together, these components enable you to streamline your ETL workflow. Here’s an image illustrating how AWS Glue components work:
AWS Glue components
That’s one way to look at how AWS Glue works at the architectural level. Here’s a quick look at a reference architecture when building a data pipeline using the AWS Glue product family.
When Would You Want To Use AWS Glue?
While AWS Glue continues to serve different types of users, it’s particularly popular among organizations that are trying to put up an enterprise-class data warehouse.
They benefit from the fact that AWS Glue seamlessly facilitates the movement of data from various sources into their data warehouse.
The process itself is quite simple and straightforward — you use AWS Glue to validate, cleanse, organize, and format data, which is ultimately stored in a centrally accessible data warehouse. You’ll further notice that the platform enables you to load such data from both data streaming and static sources.
Now, the point of this whole approach is to bring in critical data from various parts of your business, and then consolidate it all into a central data warehouse.
As such, you should be able to conveniently access and compute all your business information from a common source. You can also:
- Automatically scaling resources to cover the current needs of your situation.
- Error handling and retrying so as to avoid stalling issues.
- Gathering KPIs (Key Performance Indicators), metrics, and logs of your ETL procedures for the sake of monitoring and reporting.
- Executing ETL jobs based on specific events, schedules, or triggers.
- Automatically recognizing database schema changes, and subsequently tweaking the service to respond accordingly.
- Generating ETL scripts with the aim of enriching, denormalizing, and transforming the data while it’s transferring from its source to the target.
- Identifying your data stores’ and databases’ metadata, and then proceeding to archive them in AWS Glue Data Catalog.
What Are The Benefits And Limitations Of Using AWS Glue?
As with everything else in the world of big data computing, AWS Glue has its strengths and weaknesses. Although Amazon has, admittedly, done a fairly good job on it, there are still a couple of things about it that you might find a bit limiting.
Here’s a breakdown of both sides:
Pros of AWS Glue
- Serverless – As a serverless data integration service, AWS Glue saves you the trouble of building and maintaining infrastructure. It is Amazon that provides and manages the servers.
- Automatic ETL code – AWS Glue is capable of automatically generating ETL pipeline code in Scala or Python — based on your data sources and destination. This not only streamlines the data integration operations but also gives you the privilege of parallelizing heavy workloads.
- Increased data visibility – By acting as the metadata repository for information on your data sources and stores, the AWS Glue Data Catalog helps you keep tabs on all your data assets.
- Developer endpoints – For users who prefer to manually create and test their own custom ETL scripts, AWS Glue facilitates the whole development process through what it calls “developer endpoints.”
- Job scheduling – AWS Glue provides easy-to-use tools for creating and following up job tasks based on schedule and event triggers, or perhaps on-demand.
- Pay-as-you-go – The service doesn’t force you to commit to long-term subscription plans. Instead, you can minimize your usage costs by paying only when you need to use it.
Cons of AWS Glue
- Requires technical knowledge – Some aspects of AWS Glue are not very friendly to non-technical beginners. For instance, since all the tasks run in Apache Spark, you need to be well-versed in Spark to tweak the generated ETL jobs. What’s more, the ETL code itself can only be worked on by developers who understand Python or Scala.
- Only two languages – When it comes to customizing ETL codes, AWS Glue only supports two programming languages, Python and Scala.
- Limited integrations – AWS Glue is only built to work with other AWS services. That means you won’t be able to integrate it with platforms outside the Amazon ecosystem.
AWS Glue Vs. EMR: How Does It Compare?
While they have varying functionalities, some of them can, understandably, seem a bit confusing when you try to compare their capabilities. You might, for instance, find yourself torn between AWS Glue and AWS EMR — as they share quite a number of similarities.
It’s worth noting, they also happen to have a couple of stark differences in the way they operate.
AWS Glue, for example, exists as a serverless ETL system that offers its services on a pay-as-you-go basis.
In essence, you only need basic infrastructure, and voila! Even without a server, you can count on AWS Glue to automate the bulk of the tasks in monitoring, executing, and writing ETL jobs.
The rules, however, change slightly on the side of AWS EMR. Amazon Elastic MapReduce, as it’s known in full, reduces the costs of analyzing and processing huge volumes of data through a managed big data platform.
Instead of restricting your configuration options, it allows you to set up custom EC2 (Amazon Elastic Computing) instance clusters, as well as create Hadoop ecosystem elements.
There’s just one caveat. Apparently, AWS EMR requires you to have your own extensive infrastructure if you intend to leverage it for big data operations. This, of course, makes getting started a costly affair.
On a brighter note, though, once you set up the infrastructure, you’ll have an easy time deploying AWS EMR — plus capitalizing on its power and flexibility. Data analysts can, for example, use it to perform SQL queries on Presto, while data scientists might have a field day running machine learning tasks.
As for AWS Glue, it turns out you don’t get as much power and flexibility — which translates into a rather interesting relationship between the two. While organizations can proceed to replace AWS Glue with AWS EMR, the alternative is not possible.
Also, you should note that since AWS Glue is serverless, it tends to be a bit costlier than AWS EMR. If you compare similar cluster configurations across the two, you’ll notice that the former is more expensive.
But, not by a huge margin. While AWS Glue would charge you around $21 per DPU (Data Processing Unit) for an entire day, Amazon EMR will bill you about $14 -16 for a similar configuration.
How Can You Measure And Monitor Your AWS Glue Costs?
While Amazon Glue’s pay-as-you-go rate of $0.44 per DPU might seem reasonable at first, organizations commonly find themselves with bloated bills after prolonged use — which often run into thousands of dollars per month in extra or unnecessary costs.
Such cost overruns are mostly due to poor AWS cost management practices.
Something as simple as keeping tabs on your AWS Glue spend can be a challenge — since Amazon doesn’t readily provide comprehensive insights (like what you’re spending and why or how specific services drive your product and feature costs).
With CloudZero, however, you gain complete insight into your AWS cloud spend. CloudZero’s cloud cost intelligence maps costs to your products, features, dev teams, and more. The platform also automatically detects cost issues and alerts you before they run for days or weeks.
With cloud cost intelligence, you’ll be able to drill into cost data from a high level down to the individual components that drive your cloud spend — and see exactly how services drive your cloud costs and why.
Frequently Asked Questions About AWS Glue
This AWS Glue FAQs section answers common questions about the serverless data integration service.
Is AWS Glue good for ETL?
AWS Glue delivers a fully managed ETL service which simplifies data preparation and loading for analytics. AWS Management Console enables you to create and execute ETL jobs in a few clicks.
What is AWS Glue used for?
Using AWS Glue, you can discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development.
Does AWS Glue use SQL?
The AWS Glue Data Catalog is compatible with Apache Hive metastore. You can specify your development endpoints and AWS Glue jobs to access the Data Catalog as your external Apache Hive metastore. Then you can directly execute Apache Spark SQL queries against the tables you have stored in the Data Catalog.
Why use AWS Glue over Lambda?
Glue handles large workloads faster than Lambda by using parallel processing.
Also, Lambda requires more complexity or code to work with data sources (such as Amazon Redshift, Amazon RDS, Amazon S3, databases running on Amazon ECS instances, Amazon DynamoDB, and more). In addition, Lambda has a 15-minute timeout maximum while you can configure AWS Glue to run for a lot longer.
What language does AWS Glue use?
The ETL scripts in AWS Glue are written in Python or Scala.
Can AWS Glue write to S3?
Yes. AWS Glue for Spark reads and writes to Amazon S3. By default, AWS Glue for Spark supports data formats such as CSV, Avro, JSON, ORC, and Parquet.
What database does AWS Glue use?
Various. AWS Glue automatically identifies structured and semi-structured data hosted on an Amazon S3 data lake, a data warehouse in Amazon Redshift, and a variety of databases running on the AWS cloud.
Can AWS Glue connect to Azure?
Yes through the Azure Data Lake Storage Connector for AWS Glue. The connector eases connecting AWS Glue jobs for extracting data from Azure Data Lake Storage Gen2 (ADLS). It also simplifies loading data into Azure ADLS.
Can AWS Glue replace Amazon EMR?
No. Both have a role to play and excel in different areas. See the AWS Glue vs Amazon EMR section to compare the two services — and decide which one is right for you and when.
When should you not use AWS Glue?
AWS Glue does not support job bookmarks and grouping small files, among other limitations.