AWS offers many tools for big data extraction, transformation, and loading (ETL) analytics and processes, which include AWS Glue and Amazon EMR Serverless. Even though these two tools have similarities, there are some major differences that define them both. Amazon EMR Serverless was designed by Amazon for analytics, while on the other hand, AWS Glue was designed to work alongside other tools for processes in ETL.
A new deployment option you should know about is Amazon Elastic MapReduce (EMR) Serverless, which is still being previewed by Amazon EMR. The tool has been designed to simplify the cost optimization and operations of running open-source frameworks such as Hive, Apache Spark, and applications that are Presto-built at Petabyte scales.
Meanwhile, AWS Glue is a data integration serverless service that is designed for making it easier for discovering properties (schema), transforming, preparing, and combining data for machine learning, analytics, and API and app development.
In this article, we will walk you through the differences and similarities between AWS Glue and Amazon EMR Serverless, their cost benefits, and when you should choose one over another.
Amazon EMR Serverless
Users mainly leveraged Amazon EMR on Outpost, Elastic Compute Cloud (EC2), and Elastic Kubernetes Service (EKS) before the time of Amazon EMR Serverless. These options demanded scaling, management, configuration, and planning of clusters.
Apart from the errors that you could experience with these deployments or the mandates for conforming to policies for business corporate security, there were always lopsided efficiency and finance costs for under or over-provisioning whenever there was a change in data sizes. The result of these inefficiencies was that operators had to be highly skilled for processing jobs. That was mainly because the clusters were complex to set up, and data engineers used to run them longer than required, which resulted in increased spending.
Amazon launched EMR Serverless in response to that in November 2021. The best part about EMR Serverless is that it provides a runtime environment that is serverless, which prevents direct interventions with cluster scaling, configuration, and management. You don’t need to scale, configure, or manage clusters, because it is handled by Amazon.
In addition, there’s no need for managing virtual machines (VMs) or maintaining and installing runtime software. It’s become easier as you can stop, start, and delete apps whenever you want, which eases operations, and reduces financial and labor costs.
EMR Serverless offers processing for petabyte analytics as it uses popular open-source big data software such as Presto, Apache Spark, and Apache Hive. Particularly for Hive and Spark, you can take advantage of processing speeds at runtimes that are Amazon optimized and are twice the speed of open-source editions. EMR Serverless also offers a lot of flexibility to execute jobs.
You are in control, as you can pre-initialize a set of executors and drivers, and can set the maximum workers that are scaling your application. You have the choice between four, one, or two virtual central processing units (vCPU) for every worker and from 30 to 2 GB in increments of 1 GB per worker with the default storage of 20 GB, which is available for every worker. You can also use the software development kit (SDK), and AWS command-line interface (CLI) for submitting jobs via the EMR Studio, APIs, and AWS console or via ODBC and JDBC.
You can expect that EMR Serverless will have staggered updates, which will include shared access for Jupyter Notebooks that use EMR Studio, making it easier for querying and analyzing the data interactively by using TensorFlow, MXNet, Tez UI, and Spark UI after the ETL processes.
If data pipelines interest you, then you can run any pipeline made by AWS Step Functions, SageMaker (for machine learning), and for Apache Airflow AWS Managed Workflows (Amazon MWAA). You can also find EMR notebooks, which are serverless notebooks perfect for running code and queries. These are not like traditional notebooks since EMR notebooks contain narrative texts, code, models, queries, and equations run on a client. Commands can be executed by using kernels on the EMR clusters.
Cost Benefits of AWS EMR Serverless
You can take advantage of substantial cost savings from EMR Serverless, since it supports sharing and has no upfront costs. The supported sharing ensures that there can be multi-tenants that have different IAM roles (identities and access management) for using the same applications. In addition, you will only be paying for the memory, storage computing resources, and aggregate virtual CPU (vCPU) used by you.
Ultimately, the best part about EMR Serverless is that it allows you to use the big data analysis and processing tools you’re familiar with already in an environment that is fully managed.
In its rawest form, big data generally needs ETL in any data warehouse to process, machine learn, and analyze. An easy-to-use ETL tool that is serverless is AWS Glue, which also has individual parts that work well with it.
Even though it uses technologies that are open-source such as Apache Spark, AWS Glue comes with proprietary metadata repositories called Data Catalogs and crawlers for traversing numerous data stores in a single running, using classifiers for reading the data, and populating with tables the Data Catalog. There is also a flexible scheduler that handles dependency resolutions, retries, and job monitoring.
For more complicated ETL situations that can be advantageous from automation, there are reusable blueprints used by Amazon Glue, which accepts parameters for creating workloads on schedule or on-demand. In general, the best feature of AWS Glue is the ETL engine that generates Scala or Python code. Most open-source tools can’t do this, as sometimes you are writing code to ensure seamless operations or connecting parts of the data pipeline.
Part of the feature set of Amazon Glue is the AWS Glue DataBrew that is mainly used for normalizing and cleaning data and has a visual interface that doesn’t require any code. The feature known as FineMatches utilizes machine learning for finding duplicates or imperfect record matches, and AWS Glue Elastic Views helps replicate and combine that data in different data stores. As a unit, these tools help in automating the ETL processes, ensuring that you’re able to spend more time on data analysis.
Cost Benefits of AWS Glue
As expected, you can safely say that every tool used by you will incur some change in the system. There is an hourly rate charged by AWS Glue, and paying separately for ETL jobs and crawlers every second. You will be paying a fee monthly for the AWS Glue Data Catalog to store and access metadata, but you must consider that the first million accesses and objects will be free.
All provisioned development endpoints for interactive developments of ETL codes will be charged on an hourly basis and will be billed per second. That can be expensive, especially if you’re used to running longer sessions. If you’re wondering about the costs of Glue DataBrew, it will be billed separately by AWS for jobs and sessions every minute.
Even though AWS Glue might be the more affordable option, your costs can be increased if you are using a different suite of tools for prolonged time periods. Also, various groups in your organization may be using AWS Glue to collaborate on data integration tasks, which can include running, loading, combining, normalization, cleaning, and extracting scalable workflows in ETL. By ensuring that you collaborate, you can reduce the time taken to analyze your data and take it to the markets.
Differences, Similarities, and When You Should Be Using Either
AWS Glue and Amazon EMR Serverless are both the same in many ways, as they are serverless and can theoretically execute processing and ETL tasks in the same way that an RDS (relational database service) and an EC2 can run databases. There is only one major difference between them and that comes in the form of the recommended use by Amazon for each, which is AWS EMR Serverless for analytics and data processing, and AWS Glue for ETL.
When You Should Be Using Which?
If you’re looking to use data analysis and processing tools that are well-known but aren’t specific to AWS necessarily, then Amazon EMR is a cost-effective and fast solution. For example, the Apache Spark framework is the one that is most popular among all the main big data analytics and processing industries, and the codebase has been actively maintained and contributed towards. When you use Amazon EMR, you will gain the ability to run an open-source mainstream processing software for big data like Apache Hive or Spark at costs and runtime speeds that are scaled and optimized.
However, you should think about using Amazon Glue if you want to use a more cohesive and simpler data pipeline. The best part about AWS Glue is that it can be connected to different tools for data discovery, ingestion, loading, query, and visualization tasks.
Most organizations that are ingesting streaming data prefer using AWS Glue. A success story that you can look towards for inspiration is the BMW Group. AWS Glue helps the BMW Group save on costs and utility by supporting sources such as Apache Kafka and Amazon Kinesis, facilitating the transformation and cleaning of in-flight data streams, and continuously loading the results into Data Lake, AWS S3, and multiple data stores.
When you’re running a company that has raw data, requires ETL, and relies on a plethora of tools for prolonged time periods, then your best option is AWS Glue. It has plenty of documentation and has been used widely by organizations all over the world to good effect. You should remember that AWS Glue will be more expensive compared to EMR Serverless for the same computing resources.
When your data is in your preferred format, it’s better that you use EMR Serverless because then your petabyte-scale processing is going to be super-fast and batched. Choosing to use this tool will offer you multi-tenanted, cost-efficient, and simple operations with clusters having managed scaling and computing resources having commensurate aggregated billing.
The Bottom Line for AWS Glue vs. Amazon EMR Serverless
AWS Glue and Amazon EMR Serverless are both excellent tools and come with capabilities that overlap. It can be difficult to choose one over the other, especially because both of them do an excellent job at processing and ETL. However, the best tool for you will be the one that determines your circumstances and suits your specific preferences.
There is no right or wrong way to test the tools because both of them come with plenty of capabilities and will ensure that you take all the benefits home with you. Data processing and containering is made easier with AWS Glue and Amazon EMR Serverless and the final decision mainly comes down to whichever tool has the best-placed resources to help you.
There are numerous companies that have benefitted from the use of these tools, and that specifically goes on towards the job that Amazon is doing. By allowing these tools to be used in data processing and analysis, it has allowed organizations to take the next step in their evolution and transform their operations. Data is gold, but it doesn’t work if you don’t have the correct data processing and analysis tools and that is exactly where AWS Glue and Amazon EMR Serverless come into the picture. With that, we have finally come to the conclusion of this article, where we looked at both the best case use scenario and the benefits of both AWS Glue and Amazon EMR Serverless. The final decision as to which tool is the better rests with you as each has its own strengths and weaknesses. It is best that you study both of them and then use them as they are advertised to get the best results.
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