Breathe in and dive into the world of AWS Glue, where the magic of automated data preparation and integration comes to life. In this essential introduction, we invite you to embark on a journey that will revolutionize the way you handle data engineering tasks. AWS Glue, a powerful service provided by Amazon Web Services (AWS), enables you to effortlessly transform, cleanse, and catalog your data, paving the way for efficient analytics and insights. Whether you’re a data engineer, a data scientist, or a business professional seeking to harness the full potential of your data, this guide is designed to equip you with the foundational knowledge needed to dive into AWS Glue’s capabilities. Join us as we unravel the magic of AWS Glue and unlock the doors to a world where data transformation and integration become an enchanting and streamlined experience. Introduced in August 2017 AWS Glue is part of one of the platforms provided by Amazon Web Services which integrates and works with AWS infrastructure to provide excellent and efficient data services.
What is AWS Glue?:
If you are a fan of coding and machine learning then you might know what AWS Glue is exactly. To understand it in simple words it is a serverless data integration service that is developed by Amazon to prepare, combine and discover data for machine learning, data for analytics, and other application development. Basically, AWS Glue provides data in such a way that you can start using your data in just a few minutes, unlike in old times when you need to wait for months to use your data. What’s interesting is that AWS can provide both data integration services that are code-based interfaces and visual interfaces.
What is AWS Glue Built on:
When we talk about AWS Glue it’s important to learn about what exactly it is based on. Basically, it is based on the Apache Spark Structured Streaming engine(Spark 3.1). It can also work on streams from:
1) Amazon Kinesis Data Streams,
2) Apache Kafka,
3) Amazon Managed Streaming for Apache Kafka.
Because of Spark 3.1, you can use scripts in Python and Scala language(you will read about it further in this article).
As AWS Glue is based on this engine it makes it easier to control big data on the job. Many companies find it difficult to maintain big data but with the help of AWS Glue, it becomes easier to maintain.
When it comes to the uses of AWS Glue it has many uses that can be listed but some of them are mentioned below
1) As mentioned in this article it is used for ETL jobs-related things like events, notifications, or schedules.
2) AWS Glue is used for scaling resources automatically as per the need of the situation
3) As it is an ETL Job-based application, it maintains KPIs, data, and metrics, and logs and monitors it.
4) AWS Glue is also useful for handling any errors related to ETL job data so that it doesn’t create further confusion in job data.
How does it work?:
Let’s shed some light on how AWS Glue works. Well, in simple words it is like a warehouse of data. Ir stores and generates data whenever you need it. In earlier times when one used to store data, they cannot use it for months because systems were slow at that time. But now because of AWS Glue and development in technologies, it has become easier to access the data. It is responsible to give you notification of the job, creating data for the job, monitoring your job runs, and helping you in every step of the job. AWS Glue connects all the data into a management application. This way you can manage your ETL operations(you can learn more about it further in the article). You will know how AWS Glue works once you start using it.
AWS GLUE Features:
Now we will talk about some features that are provided by AWS Glue:
1) Automatic ETL Code Generation.
(You can learn more about ETL further in the article.)
2) Endpoints For Developers
3) Data Cleaning and DeDuplication
4) AWS Glue Data Catalog
5) Automated Data Schema Recognition
6) Streaming Support
7) Running Schedules For AWS Glue Job
These are some of the Features of AWS Glue. There are many other features in this that are developed by Amazon. As it contains more data related to jobs it has many features related to that. You can learn about all of these features in detail once you start using them.
How do you implement AWS Glue?:
Let’s learn how to set up your AWS Glue step by step:
1) You need to create your account in AWS Glue and then Log In to your account.
2) then create an IAM Policy(A policy that defines your permission on who can see your data)for the AWS Glue service.
3) Create an Environment to access data stores
These are some simple 3 steps through which you can create or implement your AWS Glue account. If you don’t understand these steps then you can take a look at the guide provided by Amazon that will guide you on every step of implementing AWS Glue.
Is AWS Glue Expensive?
AWS Glue is something that will help you in managing your data in the best way. Basically, it is a serverless platform but it can extract, transform and load your data in an easy manner. When it comes to its expenses it will cost you around $0.44 per hour. When it counts on a one-day basis roughly it will cost you $21 per day. But you know what? This cost is nothing compared to what AWS Glue provides. If you want to get easy access to data then this is one of the best platforms you can opt for. Besides, it will help you in managing data with easy understanding.
AWS Glue and Resource Policy
In simple terms, AWS Glue’s resource policy is something that controls access to data catalog resources. Moreover, these resources include data catalog APIs that can interact with tables, databases, connections, and user-defined functions. One thing about resource policy is that you can’t use it on the resources like notifications, jobs, triggers, development endpoints, etc. Catalog and resource policy is interconnected because their resource policy is connected with a catalog that contains all types of Data. You can learn more about resource policy here.
Languages Supported In AWS Glue:
When we talk about AWS Glue of course you might wonder what type of languages it might support. Well, it uses 2 languages, one is Python and the other is Scala. In AWS Glue Python uses a language that is different from its own, a language called PySpark Python. In simple words, we as humans use many languages to communicate but we all have different dialects. In the same way, Python uses PySpark Python for extracting, transforming, and loading the job. In simple words, we can say it is ETL. Moreover, all this work is based on a script. These two languages are used in the script and you can change the script according to your preference.
These are some important informational points that you might not know about AWS Glue. These points will help you in learning about AWS Glue and its resources.
Price structure of AWS Glue
The Pricing structure of AWS Glue is based on how you would like to use it and for what features. AWS glue is charged differently according to the jobs you need to complete the task. AWS Glue charges an hourly rate billed per unit second and minute. Based on the description of jobs it could either take 10 to 30 minutes to run the task and then will stop automatically unlike Lambda.
We have divided the AWS Glue price structure as per their Jobs.
- ETL Jobs and endpoints development – here you are only going to pay for the time ETL jobs take to run. AWS charges an hourly rate based on how many DPUs it will take to run the task. For development endpoints, it charges an hourly rate, billing per second.
There are mainly 3 AWS glue jobs- Apache spark, spark streaming, and Python shell.
- Apache Spark – require10 DPUs Minimum, takes 10 minutes approximately to run the job
- Spark Streaming – requires 2 DPUs minimum, takes 10 minutes minimum to run the job
- Python Shell- requires 1 DPU or 0.0625 DPU by default, takes 1-minute minimum billing duration to run
If you are using the latest Glue version like 2.0 then the timing would be reduced significantly and charges will be lower. However, it doesn’t charge anything for startups and shutdown time. There is no upfront cost as well because no resources need to be managed.
- Data catalog storage and Requests- AWS glue allows you to store a million objects for free. Once you cross that line you will be charged monthly for every additional 100,000 objects.
Also, note that you have free access to one million objects every month. You only need to pay for extra storage on the months you crossed that limit, not for all.
- Crawlers- There is an hourly rate for the AWS glue crawlers, billed per second. You are also charged on the basis of the number of DPUs you will require. Each crawler you used will take a minimum of 10 minutes. However, this option is not mandatory. You can use the data catalog directly through API as well.
- DataBrew Sessions- here we are talking about the DataBrew interactive sessions and not jobs. This will be only charged after the data is loaded in the DataBrew Project. AWS Glue billed you for the number of sessions used to load the data.
For first-time users, the first 40 sessions are free of cost. DataBrew charges are calculated in 30-minute increments. The session will automatically disconnect after 30-minutes if not active.
- DataBrew Jobs – AWS Glues charges for DataBrew Jobs based on the number of nodes you used to run a job. Although, AWS Glue allocates 5 nodes per job by default and 1 DataBrew job can 1-minute duration minimum.
It doesn’t charge any upfront, startup, or shutdown costs as there is no data to manage at that time. For a more detailed description of the pricing structure, please check Amazon AWS Glue price structure.
Difference between AWS Glue and Lambda
Here are the few major differences between AWS Glue and Lambda :
|Glue only uses languages like Python and Scala to run back-end functions and data management
|Lamda uses multiple languages for coding like Nodes.js, Python, Go, Java, etc. Therefore you don’t have to learn any language.
|Glue is beneficial to run large workloads faster due to its distributing processing system
|Lambda is quicker in running smaller tasks as it doesn’t take much time in initializing and run its own tables with new variables
|Glue is more compatible with another database if the codes are triggered by creating Glue jobs manually or from the schedule.
|Lambda is directly compatible with other data units and servers as it can execute codes and triggers by other services such as DynamoDB, CloudWatch, SQS etc.
|Glue is easy to use
|Lambda requires complex codes to integrate with data sources
|Glue has additional functions like a Data catalog, a flexible scheduler, DataBrew, etc
|Lambda is more complex while writing the code and sequences but once done it could integrate with other AWS services like Redshift, ECS Instances, RDS, DynamoDB etc.
To know more about their functions in detail you can check their differences here.