AWS Glue is a data integration service that prepares data for analytics, application development, and machine learning through what is known as an exact, transform, and load (ETL) process.
As a cloud service that is fully serverless, AWS Glue makes it easy to organize big data, collect it into data lakes and warehouses, and extract data for integration into other jobs and processes.
In short, Glue automates the data integration process for your business or enterprise. The cloud computing platform crawls your data, identifies data formats, provides schemas, and allows you to generate code to import your data into loading processes and other tasks.
In this article we will discuss AWS Glue in detail, including its key features, pros and cons, and more.
AWS Glue features
Similar to other ETL processes, Glue contains several features:
- Faster data integration
- A serverless environment
- Data automation for big data
Let’s take a closer look at these features.
Faster data integration
With a fully automated process, data integration is faster and more efficient. For example, you can extract, clean, normalize, combine, and load ETL workflows to reduce the time analyzing data with fewer errors.
Also, if you work in a team, your organization can work together to perform these tasks – separating the already reduced workflow for further efficiency.
A serverless environment
With no infrastructure to manage, as an AWS Glue customer you don’t have any additional servers or expenses to pay for. You only pay for the resources used while your data integration processes are running.
Data automation for big data
Enterprises dealing with big data often struggle with data automation. But AWS Glue automates the data integration process with the ability to crawl all kinds of data sources.
Teams can also use Glue to scan, manage, and run thousands of separate ETL jobs if needed. Furthermore, it can also generate code automatically for you to run your data and load other processes.
Pros and cons of AWS Glue
Now that we’ve discussed the main features, let’s highlight the pros and cons. Because let’s face it, there’s always something great and something not so great about all ETL services.
- Glue automatically generates code –
- Serverless design
- Logs can be debugged, and failed jobs retrieved
- Glue suggests data schemas
Glue automatically generates code
Unlike other ETL options, Glue automatically generates code for most cases – making it ideal for those with little to no coding experience.
If you prefer to write your own code, you can also do that using Apache Spark (which is built into AWS Glue).
We’ve covered this briefly in the features section already, but the serverless design means less time managing resources and more time running data organization jobs.
This also means that it’s typically cheaper than sever options, so that’s another bonus!
Logs can be debugged, and failed jobs retrieved
If an ETL task doesn’t go according to plan, failed jobs can not only be retrieved, but you can later debug them to prevent the issue(s) from happening again in the future.
Debugging allows you to keep your data integration operation running smoothly with minimal interruption and maximum efficiency.
Glue suggests data schemas
Data schemas describe how your data is stored in a database. You’ll use multiple schemas to arrange various datasets, and with other software options, you’re often left in the dark, made to create or choose schemas with no guidance.
With AWS Glue, though, data schemas are suggested to you – even if you don’t explicitly define what you’re looking for. This allows those with limited data knowledge to arrange, store, and interpret data with ease. It also saves those with experience even more time.
- Glue only accepts Python or Scala scripts
- Little control and customization of resources
- Restricted compatibility options
- It can be difficult to learn – those familiar with Apache Spark will find it much easier
Glue only accepts Python or Scala scripts
One main potential downside of AWS Glue is the inability to use other scripts aside from Python or Scala.
For most users, this won’t be an issue. But if you’re transferring your data integration processes from a more custom operation, this could be a problem. Or, at the very least, a slight inconvenience until you’ve got things set up and running.
Little control and customization of resources
AWS Glue provides little control and customization of resources. For example, these are typically memory-intensive and machine learning-focused.
But if you’re looking for something very niche and specific, then you may run into a few roadblocks along the way.
Restricted compatibility options
Glue works well and integrates nicely with most data sources, but unfortunately only functions with other services that use AWS.
This results in limited compatibility options, especially if you have various data sources from non-AWS systems.
It can be difficult to learn
Finally, Glue comes with a relatively steep learning curve. If you’re already familiar with Apache Spark, then the transition won’t be as difficult.
With Glue, Apache Spark runs in the background. But if this is the first time you’ve heard of the popular open-source analytics engine, it may take you a while to familiarize yourself with the cloud software.
Data integration that makes sense for most enterprises
AWS Glue is a serverless data integration service that simplifies the organization and transfer of data with custom code, schemas, data lakes, and other impressive features.
Glue is a suitable option for managing, interpreting, and storing big data for many users and enterprises – a feat that other software (cloud-compatible or not) cannot compete with. And if you’re already using AWS services (data-related or not), then it makes a whole lot more sense to try Glue than rival options.