AWS Glue – All you need to Simplify the ETL process

The ETL process might be a familiar term for those always working with data warehouses or databases. For those who don’t know, the ETL process is designed to transfer data from the source database to the data warehouse. The issue here is the challenges and other complexities when users try to implement it. It is to make the full implementation as seamless as possible that Amazon introduced the AWS Glue. In this article, we will learn everything there is to know about AWS Glue and its importance in cloud infrastructure.

AWS Glue: An Overview

AWS Glue can be defined as Amazon’s fully managed ETL service. It makes it simple and easier for users to categorise, clean, enrich or move the data as reliably as possible between various data stores. Being serverless, there is no need for an infrastructure to manage it or even set it up.

• Benefits Of AWS Glue

1. AWS Glue is integrated across various AWS services, so there’s less hassle when implementing or getting familiar with the service.

2. Due to a lack of infrastructure needed as the service is serverless, there are no extra expenses or additional bills that need to be paid. Hence, it is a cost-effective solution for entrepreneurs and businesses alike.

3. Since AWS Glue automates a significant amount of effort in building, running and maintaining ETL jobs, it automatically generates code to execute the various loading processes or other requirements for data transformation.

All benefits make it one of the most widely used services for ETL processes.

• When Is AWS Glue Used?

1. When users need to build a data warehouse to clean, organise, validate or format any of the data involved. By storing it in a data warehouse, companies can integrate information from various parts of their businesses and get a common data source for all decision-making processes.

2. When companies need to run serverless queries against Amazon storage services, with AWS Glue, not only can they access data, but they can also analyse it using one unified interface. This is way better than loading it through multiple data slots.

3. When users want to make event-driven pipelines for ETL services, they can use AWS Glue.

4. Companies and individual users can store data using the various AWS services and, even then, maintain a unified view of the whole data and understand the various data assets using AWS Glue.

• Various Components Involved In The AWS Glue Architecture

1. The data catalogue is the first component that holds the structure of the data. It also holds the metadata needed for the system to work to its maximum effectiveness. Data catalogue stores information related to queries, schemas, destinations or data sources,  partitions and other variables.

2. The job scheduling system is the next component that is responsible for starting various jobs depending upon the events that follow.

3. Next, crawlers retrieve the data from the source and build the necessary metadata tables for the data catalogue.

4. Then there’s the ETL engine, which handles all the code generation in either Scala or Python and allows code customisation.

5. Finally, we have the data store. It helps keep users’ data stored in a denoted data repository for long periods.

Conclusion

AWS Glue is a boon for many companies and users looking for a serverless mechanism to make integration and implementation processes as hassle-free as possible. We hope that by reading this article, you have understood the basics of AWS Glue and its advantages for users. To learn more about AWS Glue or the ETL processes, you can check out our website, Education Nest, for loads of free resources and learning materials on these topics.