And one of the key differences lies in the technology. However, from a practical perspective, AWS Glue is more of a managed ETL service, while AWS Data Pipeline is more of a managed workflow service. assisting with your organization’s ETL tasks.deploying and managing long-running asynchronous tasks,.integrating natively with S3, DynamoDB, RDS, or Redshift,.moving and transforming data across different components in the AWS Cloud,.When do I Use AWS Glue, and When do I Use AWS Data Pipeline?ĪWS Glue and AWS Data Pipeline have a lot in common.
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Edit, debug, and test your Python or Scala Apache Spark ETL code using a familiar development environment. You can also use the AWS Glue API operations to interface with AWS Glue services. The console calls the underlying services to orchestrate the work required to transform your data. With the AWS Glue console, you can discover data, transform it, and make it available for search and querying. Thus, you can convert between dynamic frames and Spark data frames, taking advantage of both AWS Glue and Spark transformations to perform the analyses you need. Dynamic frames deliver schema flexibility and a set of advanced transformations specifically designed for dynamic frames. It also introduces a component called a dynamic frame.Ī dynamic frame is similar to an Apache Spark data frame – which is a data abstraction used to organize data into rows and columns – except that each record is self-describing so no schema is initially required. The core of AWS Glue are a central metadata repository (called the AWS Glue Data Catalog), an ETL engine automatically generating Scala or Python code, and a flexible scheduler handling dependency resolution, job monitoring, and retries.ĪWS Glue is designed to work with semi-structured data. That way – by storing information in a data warehouse or data lake – data from different parts of your business is integrated, providing a common source of data for making decisions.ĪWS Glue is serverless, so you don’t need to set up or manage any infrastructure. You can also load data from disparate static or streaming data sources into your data warehouse or data lake for regular reporting and analysis. With AWS Glue, you can transform and move AWS Cloud data into your data store.
Thus, you can set up potent custom pipelines to analyze and process information without dealing with the complexities of reliably scheduling and executing application logic.įinally, AWS Data Pipeline also enables you to move and process information previously locked up in on-premises data silos.ĪWS Glue is a fully managed ETL service (extract, transform, and load) that makes it easy and cost-effective to categorize data, clean and enrich it, and move it reliably between data stores and data streams. For example, you can configure an AWS Data Pipeline to take actions like run Amazon EMR jobs, execute SQL queries directly against databases, or execute custom applications running on Amazon EC2 or in your own datacenter. Specifically, AWS Data Pipeline enables you to rely on several flexibility features – like scheduling, dependency tracking, and error handling – by using pre-defined activities and preconditions or by creating your own. With the help of AWS Data Pipeline, you can create complex data processing workloads – repeatable, highly available, and fault-tolerant – to feel at ease about managing inter-task dependencies, retrying transient failures or timeouts in individual tasks, ensuring resource availability, or creating a failure notification system. With the use of AWS Data Pipeline, you can access your data where it’s stored, transform and process it at scale, and move the results efficiently to other AWS services – like Amazon RDS, Amazon DynamoDB, Amazon S3, or Amazon EMR. It helps to process and move information between different AWS compute and storage services, as well as on-premises data sources, at specified intervals. The AWS Data Pipeline web service enables you to easily automate the movement and transformation of data. In this entry, we're comparing both services to help you choose which is better suited to your needs. However, there are also fundamental differences. The primary goal of both solutions is to move data. AWS Glue and AWS Data Pipeline have a lot in common.
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