ETL Optimization Using AWS Glue Services | iCert Global

AWS Glue is a tool that assists you in migrating and organizing data in an easy and quick manner. It is a managed ETL service, i.e., it assists you in Extracting, Transforming, and Loading data from wherever it is, making it clean, and placing it where it needs to be.

AWS Glue consists of some key constituents:

• A Data Catalog, which keeps all your data in order.

• A data transformation engine capable of producing the code (in Python or Scala) required to transform your data.

• A task-scheduler that executes tasks, checks for errors, and retries if an error is found.

AWS Glue Features

AWS Glue is one of the wonderful Amazon tools that assists in collecting, cleaning, and moving data. It is capable of functioning independently and making data work much easier. Some of its finest features are as follows:

• Automatic Jobs: AWS Glue automatically initiates jobs whenever new data comes into Amazon S3. You save time and effort.

• Database Catalog: You can quickly search for and retrieve datasets without having to move them. AWS Glue Studio: No coding required! You can utilize a tool that allows you to drag and drop to build and execute data jobs.

• Maintains Multiple Approaches: AWS Glue maintains support for batch jobs, real-time (streaming) jobs, and both ETL and ELT approaches.

Elements of AWS Glue

To understand how AWS Glue works, it is useful to know its main parts:

• Console: This is the primary screen where you can schedule and plan your data tasks.

• Data Catalog: Contains critical metadata about your data, including where it resides, what type it is, and how it is being used. It makes other tools discover your data in an easy manner.

• Job Scheduler: Allows you to schedule times or triggers to run your data jobs automatically.

• Scripts: These are the small programs that alter the data. You can let AWS Glue create them or create your own.

• Connections: These enable AWS Glue to connect to where your data resides.

• Data Store: Where your data is stored, i.e., Amazon S3 or a database.

• Data Source: Where the data is located prior to modification.

Key Advantages of AWS Glue

1. Easy to Use

AWS Glue integrates well with most of the other AWS services. AWS Glue can also read from Amazon RDS, Amazon Aurora, Amazon Redshift, Amazon S3, and so on. AWS Glue can also read from private cloud servers that are hosted on Amazon EC2.

2. Saves Money

AWS Glue doesn't require special servers or hardware. It's a serverless offering, so AWS manages all of that like setup and scaling. You pay only as your data jobs are running, and that saves you money.

3. Very Powerful

AWS Glue does all the heavy lifting for you. It reads your data, figures out the schema, and assists you in structuring it. AWS Glue also generates the code to transfer and convert the data for you.

AWS Glue Concepts (Basic Overview)

In AWS Glue, you specify jobs to transfer and transform your data. These jobs read data from a location (source), transform it (transform), and write it to another location (target).

• You then build a crawler. It's a piece of software that traverses your data and creates a table to define it. This table is inserted into something called the Data Catalog, which stores the details of your data.

• The Data Catalog holds all the data that you require to do your data work.

• AWS Glue can generate a script for you (it is similar to a recipe to transform your data), or you can generate it yourself.

How does AWS Glue function?

Let's take a glance at a basic example of how AWS Glue enables the migration and cleaning of data using Python and Spark. We will also define some of the fundamental vocabulary like crawler, database, table, and job.

1. Build a Data Source

First, we must store our data somewhere. AWS Glue is able to read data from somewhere like Amazon S3 or a database.

We form two subfolders inside this folder:

• One named read

• One named write

2. Crawl the Data

Then, we use a tool called a crawler. A crawler scans through our data and views what it looks like—this is called metadata.

3. Refer to Table

Once the crawler runs, navigate to the Tables page on AWS Glue. You will notice the newly created table by the crawler. It recognizes:

• What the columns are (like rank, movie_title)

• What type of information is in each column (e.g., numbers or text)

4. Make a Glue Job (To Change the Data)

Now we get to define a job. A job is similar to a recipe that tells AWS Glue to read, transform, and save data.

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Conclusion

AWS Glue helps clean and move data quickly and easily using simple tools. It saves money and works well with many AWS services. To learn more, check out AWS Glue training at iCert Global.

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