Future of AWS Glue ETL
- Enhanced Machine Learning Integration: You can integrate with other service in the AWS like SageMaker, ML models in the amazon console. The AWS Glue can automate the data and feature engineering for machine learning models.
- Real-Time Data Processing: AWS glue can enhance the real time data which can be used for crucial requirements of the applications which requires immediate insights from data streams.
- Serverless Architecture Expansion: The serverless architecture of AWS Glue will keep growing, offering even more precise control over resource distribution and cost reduction. This will guarantee effective resource utilisation by enabling users to scale their ETL processes in accordance with exact requirements.
- Advanced Data Transformation: The feature is all about data AWS glue may introduce the features like data cleansing, enrichment and analysis to support increasingly complex ETL requirements.
Introduction To AWS Glue ETL
The Extract, Transform, Load(ETL) process has been designed specifically for the purpose of transferring data from its source database to the data warehouse. However, the challenges and complexities of ETL can make it hard to implement them successfully for all our enterprise data. For this reason, Amazon has introduced AWS Glue.
AWS Glue is a fully managed ETL(Extract, Transform, and Load) service that makes it simple and cost-effective to categorize our data, clean it, enrich it, and move it reliably between various data stores. It consists of a central metadata repository known as the AWS Glue data catalog an ETL engine that automatically generates Python code and a flexible scheduler that handles dependency resolution job monitoring. AWS Glue is serverless which means that there is no infrastructure to set or manage a setup.