AWS Glue

AWS Glue is a completely overseen ETL (extract, transform, and load) service that makes it straightforward and cost-effective to arrange your data, clean it, improve it, and move it reliably between different data stores and data streams. AWS Glue comprises of a central metadata storehouse known as the AWS Glue Data Catalog, an ETL engine that automatically creates Python or Scala code, and an adaptable scheduler that handles dependency resolution, job monitoring, and retries. AWS Glue is serverless, so there's no framework to set up or manage.

AWS Glue is intended to work with semi-organized data. It presents a component called a dynamic frame, which you can use in your ETL scripts. A dynamic frame is like an Apache Spark data frame, which is a data abstraction used to sort out data into rows and columns, then again, except that each record is self-defining so no schema is required in the starting. With dynamic frames, you get schema flexibility and a set of advanced transformations explicitly intended for dynamic frames. You can change over between dynamic frames and Spark data frames, with the goal so that you can take advantage of both AWS Glue and Spark transformations to do the sorts of reviews that you need.

You can utilize the AWS Glue console to find data, change it, and make it accessible for search and querying. The console calls the basic services to organize the work required to reconstruct your data. You can similarly utilize the AWS Glue API operations to interface with AWS Glue services. Edit, debug and test your Python or Scala Apache Spark ETL code using a familiar development environment.

When Should I Use AWS Glue?

You can utilize AWS Glue to organize, cleanse, validate, and format data for storage in a data warehouse center or data lake. You can change and move AWS Cloud data into your data store. You can similarly stack data from disparate static or streaming data sources into your data warehouse or data lake for regular reporting and analysis. By saving data in a data warehouse or data lake, you incorporate information from various parts of your business and contribute a common source of data for decision making.

AWS Glue simplifies various tasks when you are building a data warehouse or a data lake:

• Finds and lists metadata about your data stores into a central catalog. You can process semi-organized data, like clickstream or process logs.
• Populates the AWS Glue Data Catalog with table definitions from scheduled crawler programs. Crawlers call classifier reasoning to understand the schema, format, and data types of your data. This metadata is collected as tables in the AWS Glue Data Catalog and is utilized in the authoring process of your ETL jobs.
• Creates ETL scripts to change, flatten, and enrich your data from source to target.
• Detects schema changes and adapts based on your preferences.
• Triggers your ETL jobs dependent on a schedule or event. You can automatically start your jobs to move your data into your data warehouse or data lake. Triggers can be utilized to make a dependency flow between jobs.
• Gathers runtime metrics to monitor the activities of your data warehouse or data lake.
• Handles errors and automatically re-tries.
• Scales resources, as needed, to run your jobs.

You can use AWS Glue when you run serverless queries against your Amazon S3 data lake.

AWS Glue can classify your Amazon Simple Storage Service (Amazon S3) data, making it accessible for querying with Amazon Athena and Amazon Redshift Spectrum. With crawlers, your metadata remains in synchronization with the hidden data. Athena and Redshift Spectrum can straightforwardly query your Amazon S3 data lake utilizing the AWS Glue Data Catalog. With AWS Glue, you get to access and analyze data through a unified interface without loading it into various data storehouses.

You can make event-driven ETL pipelines with AWS Glue.

You can run your ETL jobs when new data becomes accessible in Amazon S3 by invoking your AWS Glue ETL jobs from an AWS Lambda function. You can similarly register this new dataset in the AWS Glue Data Catalog as a component of your ETL jobs.

AWS Glue can be used to comprehend your data assets.

You can store your data using different AWS services and still keep up a unified perspective of your data using the AWS Glue Data Catalog. View the Data Catalog to quickly look and discover the datasets that you own, and keep up the relevant metadata in one central repository. The Data Catalog also fills in as a drop-in replacement for your external Apache Hive Metastore.

Benefits

• Less hassle
  AWS Glue is integrated over a wide variety of AWS services, which means less hassle for you while onboarding. AWS Glue locally supports data stored in Amazon Aurora and all other Amazon RDS engines, Amazon Redshift, and Amazon S3, also common database engines and databases in your Virtual Private Cloud (Amazon VPC) running on Amazon EC2.
  
  
• More Power
  AWS Glue mechanizes a significant effort in building, maintaining, and running ETL jobs. AWS Glue drags your data sources, recognizes data formats, and recommends schemas and transformation. AWS Glue creates the code automatically to execute your data changes and loading processes.
  
  
• Cost-effective
  AWS Glue is serverless. There is no foundation for the arrangement or to manage. AWS Glue handles provisioning, setup, and scaling of the resources required to run your ETL jobs on a completely managed, scale-out Apache Spark environment. You pay just for the resources utilized while your jobs are running.

Some of its Use cases

Queries against an Amazon S3 data lake

Data lakes are an undeniably well-known approach to store and analyze both structured and unstructured data. If you wish to build your own custom Amazon S3 data lake, AWS Glue can make every one of your data available immediately for analytics without moving the data.

Analyze log data in your data warehouse

Set up your clickstream or process log data for analytics by cleaning, normalizing, and enriching your data sets using AWS Glue. AWS Glue produces the schema for your semi-structured data, makes ETL code to transform, flatten, and enrich your data, and loads your data warehouse center on a recurring basis.

A unified view of your data across multiple data stores

AWS Glue Data Catalog can be used to quickly discover and search over numerous AWS data sets without moving the data. When the data is recorded, it is accessible immediately for search and query using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

Event-driven ETL pipelines

AWS Glue can run your ETL jobs based on an event, like getting a new data set. For instance, you can utilize an AWS Lambda function to trigger your ETL jobs to run at earliest when new data becomes available in Amazon S3. You can also enlist this new dataset in the AWS Glue Data Catalog as a part of your ETL jobs.