AWS Data Pipeline vs AWS Glue: What are the differences?

Introduction

AWS Data Pipeline and AWS Glue are both services provided by Amazon Web Services for managing and processing data. While both services offer similar features and capabilities, there are key differences between AWS Data Pipeline and AWS Glue. In this article, we will explore these differences and understand when to use each service.

1. Data Transformation Capabilities: AWS Data Pipeline provides a range of built-in activities and pre-defined templates for data transformation tasks. These activities allow for easy data manipulation, such as filtering, aggregating, and joining, through simple configuration settings. On the other hand, AWS Glue offers a more powerful and flexible approach to data transformation. With AWS Glue, you can define and create complex ETL (Extract, Transform, Load) jobs using the built-in Spark framework, enabling more sophisticated data transformations.

2. Dependency and Scheduling: AWS Data Pipeline is designed for orchestrating and managing the execution of batch workflows. It allows you to define dependencies between activities and schedule them accordingly. AWS Data Pipeline uses a visual interface to define and manage these workflows. In contrast, AWS Glue focuses on data cataloging and data preparation. While it does provide scheduling capabilities, it does not offer the same level of dependency management as AWS Data Pipeline.

3. Data Catalog and Metadata Management: AWS Glue includes a fully managed data catalog that automatically discovers and categorizes metadata about your data sources. This catalog allows you to search, query, and explore your data assets effortlessly. AWS Glue also provides the ability to create and manage custom metadata, making it easier to understand and manage your data. In comparison, AWS Data Pipeline does not have built-in capabilities for data cataloging and metadata management.

4. Data Format Support: AWS Glue supports a wide range of data formats out of the box, including popular file formats like JSON, CSV, Parquet, and Avro. It can automatically infer the schema of your data and generate code to perform data processing tasks. AWS Data Pipeline, on the other hand, has more limited support for data formats, primarily focusing on relational databases, Hadoop Distributed File System (HDFS), and Amazon S3.

5. Data Source Connectivity: AWS Glue offers seamless connectivity to various data sources, including Amazon S3, relational databases (RDS), and Redshift. It provides native connectors for these data sources, simplifying the process of data extraction and loading. AWS Data Pipeline also supports these data sources but requires additional configuration and setup to establish connections.

6. Pricing Model: AWS Glue follows a pay-as-you-go pricing model, where you are charged based on the number of compute units used and the amount of data processed. AWS Data Pipeline, on the other hand, is priced based on the number of pipeline executions and the duration of these executions. The pricing model for AWS Data Pipeline is more focused on the workflow orchestration and automation aspects.

In Summary, AWS Data Pipeline and AWS Glue differ in their data transformation capabilities, dependency and scheduling features, data catalog and metadata management, data format support, data source connectivity, and pricing model. Depending on your specific requirements and use case, you can choose the appropriate service to suit your needs.