With the vast sea of information that is growing every day, most organizations are looking toward Cloud-based solutions to collect, store and work on their data. Furthermore, the ETL process is necessary to convert the data collected from numerous sources into a common format that is accepted by the Data Warehouse. AWS Glue is one such ETL tool.
Are you looking to learn more about Amazon Glue and its fitment for your ETL needs? If yes, you have landed on the right post. This blog helps you decode some of the key aspects to consider while evaluating this tool (or any ETL solution for that matter): Glue’s Features, Pricing, Use Cases, and Limitations. Before we dive in, let us try to understand a little about the ETL process itself.
AWS Glue Overview
AWS Glue is a fully managed service provided by Amazon for deploying ETL jobs. It reduces the cost, lowers the complexity, and decreases the time spent creating ETL jobs. For companies that are price-sensitive, but need a tool that can work with different ETL use cases, Amazon Glue might be a decent choice to consider.
Launched sometime around August 2017, Glue AWS has come a long way in adding value to its users. Here are some of the noteworthy pointers about Glue:
- Glue is a “Serverless” service. Hence, you do not need to provision or manage any resources or services.
- You only pay for resources when Glue is actively running.
- Amazon Glue ETL comes with “crawlers” that can create metadata to view the data stored in S3. This metadata comes very handy while authoring ETL jobs.
- With the use of Python scripts, Glue can translate one source format to another source format.
- Glue allows you to create a development endpoint by yourself. This gives you the power to construct your ETL scripts swiftly and easily.
What are the Features of AWS Glue?
Since Amazon Glue is completely managed by AWS, deployment, and maintenance are super simple. Below are some important features of Glue:
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1) Integrated Data Catalog
The Data Catalog is a persistent metadata store for all kinds of data assets in your AWS account. Your AWS account will have one Glue Data Catalog. This is the place where multiple disjoint systems can store their metadata. In turn, they can also use this metadata to query and transform the data. The catalog can store table definitions, job definitions, and other control information that help manage the ETL environment inside Glue.
2) Automatic Schema Discovery
It allows you to set up crawlers that connect to different data sources. It classifies the data, obtains the scheme-related info, and automatically stores it in the data catalog. ETL jobs can then use this information to manage ETL operations.
3) Code Generation
Amazon Glue comes with an exceptional feature that can automatically generate code to extract, transform and load your data. The only input Glue would need is the path/location where the data is stored. From there, Glue creates ETL scripts by itself to transform, flatten and enrich data. Normally, Scala and Python code is generated for Apache Spark.
4) Developer Endpoints
This is one of the best features of Amazon Glue and helps interactively develop ETL code. When Glue automatically generates a code for you, you will need to debug, edit and test the same. The Developer Endpoints provide you with this service. Using this, custom readers, writers or transformations can be created. These can further be imported into Glue ETL jobs as custom libraries.
5) Flexible Job Scheduler
One of the most important features of Glue is that it can be invoked as per schedule, on-demand, or on an event-trigger basis. Also, you can simply start multiple jobs in parallel. Using the scheduler, you can also build complex ETL pipelines by specifying dependencies across jobs. Amazon Glue ETL always retries the jobs in case they fail. They also automatically initiate filtering for infected or bad data. All kinds of inter-job dependencies will be handled by Glue.
How AWS Glue Works?
Amazon Glue organizes all the ETL data transfer and transformation using other AWS services into Data Lakes such as Amazon S3 and Data Warehouses i.e., Amazon Redshift. It uses APIs support to extract data from sources and then transform it to perform Data Integration jobs.
One can schedule the ETL jobs or set the trigger events for the jobs to start. It generates code based on the input given by the user, and then automatically extracts data and transforms it based on the code. One can change the code in the scripts as per need. After that, it writes the metadata from the job into Data Catalog, which acts as a metadata repository.
Glue provides a console management service that allows users to get notifications for every job, create and monitor ETL jobs. Once a user supplies valid credentials to AWS Glue to access data from sources, the console allows users to perform administrative tasks.
What is AWS Glue Architecture?
AWS Glue Architecture
The different parts of AWS Glue Architecture are as follows:
1) AWS Glue Console
The AWS Management Console is a browser-based web application for managing AWS resources. It has the following functionalities:
- Defines Glue objects such as crawlers, jobs, tables, and connections.
- Creates a layout for crawlers to work in.
- Creates job trigger events and timetables.
- Filters and searches Glue objects on AWS.
- Edits transformation scenario scripts.
2) AWS Glue Data Catalog
Glue Data Catalog offers centralized uniform metadata storage for data tracking, querying, and transformation using saved metadata.
3) AWS Crawlers and Classifiers
Crawlers and classifiers automatically scan data from various sources, classify data, detect schema information, and store metadata in Glue Data Catalog.
4) AWS Glue ETL Operations
The core of the ETL program generates Python or Scala code for data cleaning, enrichment, duplicate removal, and other complex data transformation tasks.
5) Job Scheduling System
A versatile scheduling system has the responsibility of starting jobs based on various events or a timetable.
What are the Benefits of AWS Glue?
By performing Glue ETL, you can leverage the following benefits:
- Various teams within your organization can employ Glue to collaborate on data consolidation tasks such as extracting, cleansing, normalizing, joining, loading, and executing scalable ETL workflows. This reduces the time it takes to analyze and use your data from months to just a matter of minutes.
- You can also automate many of the repetitive tasks associated with data integration. It provides recommendations for a schema for searching data sources, identifying data formats, and storing data. Code is automatically generated to perform the data conversion and loading process. With Glue, you can execute and manage thousands of ETL jobs, and use Standard SQL commands to integrate and replicate data across several data stores.
- Since it works in a serverless ecosystem, you aren’t required to manage any infrastructure. Glue AWS manages, configures and scales the resources needed to perform the data consolidation tasks. You only have to pay for the resources that the job consumes while it is running.
What is the Pricing of AWS Glue?
Amazon Glue always charges an hourly rate, billed by the second. The pricing will depend on crawlers – that discover the data and ETL Jobs – that process and load your data. In addition to this, a simple monthly fee is involved to store and access metadata from the Data Catalog. As per the free tier rules of Glue, Amazon does not charge for the first million objects stored and the first million objects accessed/requested. In addition to this, if you create a developer endpoint to generate develop your ETL code, you will have to pay an hourly rate, billed per second. Yes, the pricing structure can be slightly overwhelming. Let us look at some scenarios to understand this better:
- ETL Jobs – For this example, consider Apache Spark as a Glue job type that runs for 10 minutes and consumes 6 DPUs. The Price of 1 Data Processing Unit (DPU) – Hour is $0.44. Since your job ran for 10 Minutes of an hour and consumed 6 DPUs, you will be billed 6 DPUs X 10 minutes at $0.44 per DPU-hour or $0.44.
- Development Endpoint – Now let’s assume that you will provide a development endpoint to direct connect to your computer to interactively develop your ETL code. If 5 DPUs have been provisioned for your endpoint, then you need to run this development endpoint for 24 minutes. Hence you will be charged for 5 DPUs X 24 Minutes at $0.44 per DPU-Hour or $0.88.
- AWS Glue Data Catalog billing Example – As per AWS Glue Data Catalog, the first 1 million objects stored and access requests are free. In case you store more than 1 million objects and place more than 1 million access requests, then you will be charged. Let’s assume that you will use 330 minutes of crawlers and they hardly use 2 data processing units (DPU). The number of objects stored is < 1 million. Hence the storage cost is 0. Assume that your access requests exceed 1 million requests. In this case, you will be charged $1. Crawlers will be charged at $0.44 per Data Processing Unit (DPU)-Hour, and you will pay for 2 Data Processing Unit (DPU)s X 30 minutes at $0.44 or $0.44. Hence the monthly total bill will be $1.44.
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API for Developers
The AWS Glue API gives developers more tools to deal with the AWS service efficiently. It can be accessed using a variety of computer languages via the Command Line Interface (CLI). This API allows you to create, delete, and list databases, as well as perform table operations, define crawler and classifier schedules, manage jobs and triggers, control workflows, test custom development endpoints, and run ML transformation tasks. The API also offers an exception section where you can find out where the problem is coming from and how to fix it.
Ability to Work with Data Streams
Amazon Glue performs ETL operations on data streams from Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK) and writes the output to Amazon S3 data lakes or JDBC data stores. Simply add data streams to the Data Catalog and use it as the data source for ETL processes.
Supported Data Sources
Data saved in Amazon Aurora, Amazon RDS for MySQL, Amazon RDS for Oracle, Amazon RDS for PostgreSQL, Amazon RDS for SQL Server, Amazon Redshift, DynamoDB, and Amazon S3 is supported natively by AWS Glue. Amazon Virtual Private Cloud, it also supports MySQL, Oracle, Microsoft SQL Server, and PostgreSQL databases (Amazon VPC). It also works with Amazon MSK, Amazon Kinesis Data Streams, and Apache Kafka data streams. To gain access to data sources that don’t have built-in compatibility, you can write scripts in Python or Scala and import custom libraries and Jar files into Glue ETL operations.
What are the Use Cases of AWS Glue?
This section highlights the most common use cases of Glue. You can use Glue with some of the famous tools and applications listed below:
1) AWS Glue with Athena
In Athena, you can easily use AWS Glue Catalog to create databases and tables, which can later be queried. Alternatively, you can use Athena in AWS Glue ETL to create the schema and related services in Glue.
2) AWS Glue for Non-native JDBC Data Sources
AWS Glue by default has native connectors to data stores that will be connected via JDBC. This can be used in AWS or anywhere else on the cloud as long as they are reachable via an IP. It natively supports the following data stores- Amazon Redshift, and Amazon RDS ( Amazon Aurora, MariaDB, MSSQL Server, MySQL, Oracle, PL/pgSQL).
To learn more about Amazon Redshift.
3) AWS Glue Integrated with AWS Data Lake
It can be integrated with AWS Data Lake. Further, ETL processes can be run to ingest, clean, transform and structure data that is important to you.
4) Snowflake with AWS Glue
Snowflake has great plugins that seamlessly gel with Amazon Glue. Snowflake data warehouse customers can manage their programmatic data integration process without worrying about physically maintaining it or maintaining any kind of servers and spark clusters. This allows you to get the benefits of Snowflake’s query pushdown, SQL translation into Snowflake, and Spark workloads.
To know more about Snowflake Data Warehouse.
What are the Limitations and Challenges of AWS Glue?
While there are many noteworthy features of Glue, there are some serious limitations as well.
- In comparison to the other ETL tools available today, Glue has only a few pre-built components. Also, given it is developed by and for the AWS Console, it is not open to match all kinds of environments.
- Glue works well only with ETL from JDBC and S3 (CSV) data sources. In case you are looking to load data from other cloud applications, File Storage Base, etc. Glue would not be able to support.
- Using Glue, all data is first staged on S3. This sync has no option for incremental sync from your data source. This can be limiting if you are looking at ETL data in real-time.
- Glue is a managed AWS Service for Apache Spark and not a full-fledged ETL solution. Tons of new work is required to optimize PySpark and Scala for Glue.
- Glue does not give any control over individual table jobs. ETL is applicable to the complete database.
- While Glue provides support to writing transformations in Scala and Python, it does not provide an environment to test the transformation. You are forced to deploy your transformation on parts of real data, thereby making the process slow and painful.
- Glue does not have good support for traditional relational database types of queries. Only SQL types of queries are supported that too through some complicated virtual table.
- The learning curve for Glue is steep. If you are looking to use Glue for your ETL needs, then you would have to ensure that your team comprises engineering resources that have a strong knowledge of spark concepts.
- The soft limit of handling concurrent jobs is 3 only, though it can be increased by building a queue for handling limits. You will have to write a script to handle a smart auto-DPU to adjust the input data size.
As discussed above, you will encounter quite a few challenges while working with Amazon Glue, especially outside the AWS environment. Also, it requires your engineering team to spend a lot of time learning, building, monitoring & maintaining all the AWS pipelines. To remedy this, you can use a more effortless Cloud-based ETL Tool like Hevo Data. To find out how Hevo can be an effective & economical choice for you, check out the detailed comparison between Amazon Glue vs Hevo Data.
AWS Glue vs. Hevo
You can get a better understanding of Hevo’s Data Pipeline as compared to AWS Glue using the following table:
| S.no | Parameter | AWS Glue | Hevo Data |
| 1) | Specialization | ETL, Data catalog | ETL, Data Replication,
Data Ingestion |
| 2) | Pricing | AWS Data Catalog charges monthly for storage while AWS glue ETL charges on per hourly basis. | Hevo Pricing follows a flexible & transparent model where you pay as you grow. Hevo offers 3 tiers of pricing, Free, Starter & Business.
|
| 3) | Data Replication | Full table; Incremental via Change Data Capture (CDC) through AWS Database Migration Service (DMS). | Full table; Incremental via SELECT/Replication key, Timestamp & Change Data Capture (CDC). |
| 4) | Connector Availability | Glue caters to Amazon platforms such as Redshift, S3, RDS, and DynamoDB — and AWS destinations, and other databases via JDBC | Hevo has native connectors with 150+ data sources and integrates with Redshift, BigQuery, Snowflake, and other Data Warehouses & BI tools. |
Conclusion
The article discussed AWS Glue in great detail. It introduced you to the concept of the ETL process. Furthermore, it explained the 4 essential aspects of this ETL tool: Features, Pricing, Use Cases, and Limitations. However, Amazon Glue although is an efficient tool, still faces certain limitations which were discussed above.
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Share your experience of this blog in understanding Amazon Glue in the comments section!
As a Customer Experience Engineer at Hevo, Anmol plays a crucial role in providing dedicated support to clients worldwide. His proficiency in SQL and SQLite, PostgreSQL coupled with a customer-centric approach, ensures that clients receive optimal solutions to their data integration challenges.