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AWS Data Pipeline vs Glue: Choosing the Right Solution for Your Needs
Introduction:
It is crucial to select the right data management and integration tool to fully leverage the power of data in today’s cloud-centric environment. There are a variety of solutions available from Amazon Web Services (AWS) in this area, AWS Data Pipeline and AWS Glue being the front runners. Each option has its own distinct benefits and drawbacks, catering to different requirements.
In this blog, we will explore both options to assist you in determining which one is most suitable for your data management needs.
What is AWS Data Pipeline?
With AWS Data Pipeline, data movement and transformation tasks are made easy through a convenient web service. It assists with the processing and transfer of data between different AWS services and on-premises sources at specific intervals.
With AWS Data Pipeline, users can easily process data in its original location, perform large-scale transformations, and effortlessly transfer the results to various AWS platforms like Amazon RDS, Amazon DynamoDB, Amazon S3, and Amazon EMR.
What is AWS Glue?
On the other hand, AWS Glue is a fully managed extract, transform, and load (ETL) service that simplifies the preparation of data for analytics.
With a serverless architecture, there’s no need for you to worry about managing infrastructure. Plus, it seamlessly integrates with other AWS storage and analytics services. AWS Glue offers a powerful yet flexible data processing platform that comes with a range of useful features.
- AWS Glue Data Catalog: Provides a centralized metadata repository, ensuring convenient data discovery and access.
- AWS Glue Studio: A user-friendly tool that simplifies the process of creating, scheduling, and monitoring ETL workflows. It helps with dependency resolution, job monitoring, and retries.
- AWS Glue DataBrew: Simplifies data enrichment, cleaning, and normalization for data analysts and scientists. No coding is required.
AWS Glue Elastic Views: Simplifies data integration and replication across multiple data stores. Developers can effortlessly combine and replicate data using SQL.
When do I Use AWS Glue, and When do I Use AWS Data Pipeline?
When deciding between AWS Glue and AWS Data Pipeline, it’s important to consider your specific requirements:
AWS Data Pipeline is your go-to if you require flexibility and control over infrastructure, are dealing with a diverse range of data sources, and your ETL workflows are complex.
It offers a user-friendly approach to managing data transfer processes and allows for the creation of customized workflows. Use it for easy management of EC2 instances or EMR clusters for your tasks.
AWS Glue is the better choice if you’re looking for simplicity, serverless infrastructure management, want to quickly get started with ETL, and your data primarily resides in Amazon S3 or Amazon EMR.
Comparison : AWS Data Pipeline vs AWS Glue
Comparing AWS Data Pipeline and AWS Glue will help you make an informed decision and select the solution that best suits your needs.
Feature | AWS Data Pipeline | AWS Glue |
Nature | Managed service, managing underlying infrastructure | Serverless service, abstracts infrastructure management |
Usability | More complex, offers granular control over workflows | Easier to use, simpler setup |
Core Functionality | Automates data workflows; explicit pipeline definition required | Focuses on ETL processes; can generate ETL jobs automatically |
Flexibility vs. Ease of Use | Higher flexibility, suitable for complex setups | Streamlined and user-friendly, ideal for straightforward tasks |
Data Sources and Destinations | Supports a diverse range including S3, RDS, Hadoop Hive | Limited to S3 and Amazon EMR |
Scheduling ETL Jobs | Enables scheduling of ETL jobs | Also allows for automated ETL job scheduling |
Reusable Components | Facilitates creation of reusable components in workflows | Supports modular ETL workflow components |
Cost | Typically more expensive, priced based on tasks, data, and duration | Cost-effective, charges based on the amount of data processed |
Factors to consider between AWS Data Pipeline and AWS Glue
Infrastructure Management:
- AWS Data Pipeline: Operates as a managed service, not serverless, which involves launching and managing EMR clusters and EC2 instances. This provides users with granular control over the compute resources and the environment, suitable for complex workflows.
- AWS Glue: Utilizes a serverless framework within an Apache Spark environment, thus eliminating the need for developers to manage infrastructure. This automatic handling of scaling, provisioning, and configuration simplifies operations, allowing developers to focus solely on data manipulation and analytics.
Operational Methods:
- AWS Data Pipeline: Enables data transformations through APIs and JSON scripting, supporting a selective range of AWS services like DynamoDB, SQL, and Redshift. It’s tailored for users who prefer manual control and detailed configuration of their data workflows.
- AWS Glue: Integrates with multiple AWS services such as Amazon S3, RDS, Redshift, and SQL, along with DynamoDB, facilitating a range of data transformation tasks. It supports built-in transformations and allows additional customizations using Scala or Python, enhancing its flexibility within Apache Spark.
Compatibility / Compute Engine:
- AWS Data Pipeline: Provides a more flexible approach by supporting various computation engines such as Pig and Hive, in addition to Apache Spark. This versatility makes it a favorable choice for diverse and complex ETL jobs that do not strictly require Spark.
- AWS Glue: Executes ETL jobs using a virtual, serverless Apache Spark environment, which streamlines the data transformation process by abstracting hardware management.
Pricing:
- AWS Data Pipeline: Offers a straightforward pricing strategy where pipelines are charged monthly based on their execution frequency, with additional costs accruing from the consumption of EC2 and other AWS resources.
- AWS Glue: Implements a pricing model that charges per Data Processing Unit hour, which includes the time when crawlers or jobs are running. This model also involves additional fees based on the number of objects managed in the data catalog and the volume of requests it handles.
Use Cases:
- AWS Data Pipeline: Ideal for situations where detailed control over the infrastructure is necessary. It excels at transferring data across different AWS services and managing data workflows that require specific configurations, such as regional data movements or complex processing tasks involving EMR clusters or EC2 instances.
- AWS Glue: Best suited for scenarios that demand quick, efficient ETL operations without the overhead of managing physical servers. Typical use cases involve extracting data from supported sources, transforming it via Glue’s ETL capabilities, and loading it into destinations like JDBC platforms or S3.
Conclusion:
In the realm of ETL, AWS Data Pipeline and AWS Glue stand as powerful contenders, each catering to distinct requirements. Your choice hinges on factors such as the complexity of your ETL workflows, data source diversity, and budget considerations. Whether you opt for the managed infrastructure of AWS Data Pipeline or the serverless simplicity of AWS Glue, both tools empower you to transform raw data into valuable insights that drive your business forward.
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Parkar Digital, a Gold Certified Microsoft Azure partner, provides technology solutions for Digital Healthcare, Digital Retail & CPG. Our solutions are powered by the Parkar platforms built using Cloud, Opensource, and Customer experience technologies. Our goal is to empower a customer-first approach with digital technologies to deliver human-centric solutions for the clients.
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