AWS Glue works well for big data processing. This is a brief introduction to Glue including use cases, pricing and a detailed example.

Introduction to AWS Glue for big data ETL

AWS Glue works well for big data processing. This is a brief introduction to Glue including use cases, pricing and a detailed example.

AWS Glue is a serverless ETL tool in cloud. In brief ETL means extracting data from a source system, transforming it for analysis and other applications and then loading back to data warehouse for example.

In this blog post I will introduce the basic idea behind AWS Glue and present potential use cases.

The emphasis is in the big data processing. You can read more about Glue catalogs here and data catalogs in general here.

Why to use AWS Glue?

Replacing Hadoop. Hadoop can be expensive and a pain to configure. AWS Glue is simple. Some say that Glue is expensive, but it depends where you compare. Because of on demand pricing you only pay for what you use. This fact might make AWS Glue significantly cheaper than a fixed size on-premise Hadoop cluster.

AWS Lambda can not be used. A wise man said, use lambda functions in AWS whenever possible. Lambdas are simple, scalable and cost efficient. They can also be triggered by events. For big data lambda functions are not suitable because of the 3 GB memory limitation and 15 minute timeout. AWS Glue is specifically built to process large datasets.

Apply DataOps practices. Drag and drop ETL tools are easy for users, but from the DataOps perspective code based development is a superior approach. With AWS Glue both code and configuration can be stored in version control. The data development becomes similar to any other software development. For example the data transformation scripts written by scala or python are not limited to AWS cloud. Environment setup is easy to automate and parameterize when the code is scripted.

An example use case for AWS Glue

Now a practical example about how AWS Glue would work in practice.

A production machine in a factory produces multiple data files daily. Each file is a size of 10 GB. The server in the factory pushes the files to AWS S3 once a day.

The factory data is needed to predict machine breakdowns. For that, the raw data should be pre-processed for the data science team.

Lambda is not an option for the pre-processing because of the memory and timeout limitation. Glue seems to be reasonable option when work hours and costs are compared to alternative tools.

The simplest way of get started with the ETL process is to create a new Glue job and write code to the editor. The script can be either in scala or python programming language.

Extract. The script first reads all the files from the specified S3 bucket to a single data frame. You can think a data frame as a table in Excel. The reading can be just a one-liner.

Transform. This is the most of the code. Let’s say that the original data had 100 records per second. The data science team wants the data to be aggregated per each 1 minute with a specific logic. This could be just tens of code lines if the logic is simple.

Load. Write data back to another S3 bucket for the data science team. It’s possible that a single line of code will do.

The code runs on top of the spark framework which is configured automatically in Glue. Thanks to spark, data will be divided to small chunks and processed in parallel on multiple machines simultaneously.

What makes AWS Glue serverless?

Serverless means you don’t have machines to configure. AWS provisions and allocates the resources automatically.

The processing power is adjusted by the number of data processing units (DPU). You can do additional configuration, but it’s likely that a proof of concept works out of the box.

In an on-premise environment you would have to make a decision about the computation cluster size. A big cluster is expensive but fast. A small cluster would be cheaper but slow to run.

With AWS Glue your bill is the result the following equation:

[ETL job price] = [Processing time] * [Number of DPUs]


The on demand pricing means that the increase in processing power does not compromise with the price of the ETL job. At least in theory, as too many DPUs might cause overhead in processing time.

When is AWS Glue a wrong choice?

This is not an advertisement, so let’s give some critique for Glue as well.

Lots of small ETL jobs. Glue has a minimum billing of 10 minutes and 2 DPUs. With the price of 0.44$ per DPU hour, the minimum cost for a run becomes around 0.15$. The starting price makes Glue unappealing alternative to process small amount of data often.

Specific networking requirements. If you spin up a standard EC2 virtual machine, an IP address will be attached to it. The serverless nature of Glue means you have to put more effort on network planning in some cases. One such scenario would be whitelisting a Glue job in a firewall to extract data from an external system.

Summary about AWS Glue

The most common argument against Glue is “It’s expensive”. True, in a sense that the first few test runs can already cost a few dollars. In a nutshell, Glue is cost efficient for infrequent big data workloads.

In the big picture AWS Glue saves a lot of time and unnecessary hardware engineering. The costs should be compared against alternative options such as on-premise Hadoop cluster or development hours required for a custom solution.

As Glue pricing model is predictable, the business cases are straightforward to calculate. It might be enough to test just the critical parts of the ETL pipeline to become confident about the performance and costs.

I feel that optimizing the code for distributed computing has been more of a challenge than the Glue service itself. The next blog post will focus on how data developers get started with Glue using python and spark.