Glue is AWS’s fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load data for analytics. At a high level, a Glue ETL job defines and coordinates the process of extracting data from one or more sources, transforming the data (such as filtering, joining, aggregating etc.), and loading the transformed data into target data stores.
Glue ETL jobs are defined using a visual, code-free interface or Apache Spark scripts written in Scala or Python. The job definition includes specification of the data sources, transformations to apply, and targets. When the job runs, Glue orchestrates all the required steps and ensures the data is extracted from sources, transformed as defined, and loaded to targets. Glue also handles resource provisioning, scheduling, monitoring and managing dependencies between jobs.
Data extraction is one of the key stages in a Glue ETL job. Users define the sources where the raw input data resides such as Amazon S3, JDBC-compliant databases etc. Glue uses connectors to extract the data from these sources. For example, the S3 connector allows Glue to crawl folders in S3 buckets, understand file formats, and read data from files during job execution. Database connectors like JDBC connectors allow Glue to issue SQL queries to extract data from databases. Users can also write custom extractors using libraries supported by Glue such as Python to programmatically extract data from other sources.
During extraction, Glue leverages various capabilities to optimize performance and handle large volumes of data. It uses column projections to extract only the required columns from databases which improves performance especially for wide tables. For S3, it implements multi-threaded extraction using asynchronous IO operations. It also supports checkpointing so that extraction resumes from the point of failure in case of job interruptions.
After extraction, the next stage is data transformation where the extracted raw data is cleaned, filtered, joined and aggregated to derive the transformed output. Glue provides a visual workflow editor and Apache Spark programming model to define transformations. In the visual editor, users can visually link extract and transform steps without writing code. For complex transformations, users can write Scala or Python scripts using Spark and Glue libraries to implement custom logic.
Some common transformation capabilities provided by Glue out of the box include – Filter to remove unnecessary or unwanted records; Join datasets on common keys; Aggregate data using functions like count, sum, average etc.; Enrich data through lookups; Validate and cleanse noisy or invalid data. Glue also allows creating temporary views of datasets to perform SQL style transformations. Transformations are Spark jobs so Glue leverages Spark’s distributed processing capabilities. It runs transformations in parallel across partitions of the dataset for highly scalable and efficient processing of large volumes of data.
Once data is extracted and transformed, the final stage is loading it to target data stores. Glue supports loading transformed data to many popular data targets like S3, Redshift, DynamoDB, RDS etc. Users specify the targets in the job definition. During runtime, Glue uses connectors for these targets to coordinate writing the processed data. For example, it utilizes the S3 connector to write partitioned/indexed output data to S3 for further analytics. Redshift and RDS connectors allow writing transformed data into analytical tables in these databases. Glue also provides options to catalog and register output data with Glue Data Catalog for governance and reuse across other downstream jobs/applications.
A Glue ETL job orchestrates all the data engineering tasks across the extract-transform-load pipeline. During runtime, Glue provisions and manages necessary Apache Spark resources, coordinates execution by optimally parallelizing across partitions, handles failures with robust checkpointing and retries. It provides end-to-end monitoring of jobs and integrates with other AWS services as needed at each stage for fully managed execution of ETL workflows. Glue automates most operational aspects of ETL so that data teams can focus on data preparation logic rather than worrying about infrastructure operations. The scalable and robust execution engine of Glue makes it ideal for continuous processing of vast volumes of data across cloud infrastructure.