Airbyte vs AWS Glue

Working with Salesforce, PostgreSQL, and S3?

Understanding how Airbyte and AWS Glue approach ETL can save you hours of deployment time and simplify data pipeline management.

While Airbyte focuses on flexibility to support diverse data sources, AWS Glue has a robust data integration product suite within the AWS ecosystem. Both tools are leading choices, but the difference in their data integration approach makes it difficult to choose between the two.

In this article, we compare Airbyte vs AWS Glue based on their key features and real-world use cases to help you choose the one that best aligns with your operational goals.

What Is Airbyte?

Airbyte is one of the most acclaimed open-source data integration ETL/ELT tools that follows a standardized open protocol based on Docker and JSON schemas. The customizable open protocol enables connectors to deploy independently from the core platform, accelerating iteration and custom dev cycles.

Airbyte enables you to develop custom connectors using its no-code connector builder to address specific integration needs. Following this, the platform’s modular architecture and dbt integration allow users to run dbt transformations as part of the pipeline orchestration. Finally, the data is loaded using direct-load tables, which load incoming data directly into the destination table.

Open-source ETL tools offer flexible deployment and support integration with existing workflows through their UI, Python SDK, and API, catering to a wide range of technical needs. This functionality detects schema upgrades in source systems to prevent pipeline breakage. If you’re evaluating integration tools, especially in comparison to managed platforms, see our breakdown of Airbyte vs Fivetran for a closer look at how they differ.

Key features of Airbyte

Connector catalog: Airbyte offers over 600 pre-built connectors for a wide range of sources and destinations. The connector’s support levels are divided into four levels:

• Airbyte: Backed by Airbyte.
• Enterprise: Premium connectors for enterprise-grade data integration.
• Marketplace: Managed by the open-source community.
• Custom: You can maintain it individually.

AI workflows: The tool is capable of loading unstructured and semi-structured data into vector destinations to facilitate AI workflows through Retrieval-Augmented Generation (RAG). This enhances pipeline accuracy and boosts AI applications.

Schema propagation: Airbyte streamlines data workflows by allowing users to select specific columns and propagate schema changes automatically, reducing manual intervention.

Use cases:

The key use cases of Airbyte include:

• GitHub & Jira developer analytics: Teams use Airbyte to pull data from GitHub, Jira, and CI/CD pipeline logs into a data warehouse for engineering productivity analytics. Airbyte handles API integrations to eliminate custom scripts and third-party ETL maintenance.
• Multi-channel attribution modeling: Airbyte extracts campaign data from Facebook Ads and HubSpot to merge it with web analytics data from Google Analytics 4. This unified data ingestion enables data teams to build custom attribution models using SQL or dbt.
• Gen AI and LLM workflows: Airbyte extracts data from documents like PDFs, Word files, and Google Docs to convert it into a structured markdown format for downstream processing. By grounding LLM’s output into source content, Airbyte creates reliable and transparent AI systems.

Limitations:

• The platform doesn’t offer detailed documentation about its connectors.
• Users struggle to fully control job executions and retries.
• Limited support for data ingestion across diverse sources.
  

What Is AWS Glue?

AWS Glue is a serverless data integration platform offered by AWS to streamline pipeline orchestration through a central metadata catalog. You can use the catalog across S3, Redshift, Athena, Lake Formation, and IAM systems. This facilitates quick data discovery, centralized cataloging, and automated schema management.

AWS Glue ETL leverages API operations to transform ingested data, develop runtime logs, and load job logic. The platform aggregates these services into a fully managed application to monitor ETL workflows. It automatically discovers and catalogs datasets using crawlers to save metadata in the AWS Glue Data Catalog. This makes it an optimal solution for data engineers and developers looking to scale ETL jobs without managing the infrastructure.

The platform’s uniqueness lies in its deeply embedded design within the AWS ecosystem that provides built-in support for ML-based transformations, real-time orchestration of complex data pipelines, and ensures compliance with industry-standard privacy regulations. It is a tightly integrated, metadata-aware, and scalable data integration platform with detailed documentation available through the AWS Glue tutorial.

Key features of AWS Glue

• Salesforce integration: AWS Glue features enable users to integrate their Salesforce cloud account with AWS Glue as both a source and a destination. Users can run ETL jobs to move data from Salesforce to AWS Glue Data Catalog for further analytics.
• Data discovery: Glue Crawlers automatically scan data sources (such as S3, RDS, Redshift, and JDBC databases) to infer schema and structure. Crawlers support both built-in and custom classifiers for flexible schema inference and can be scheduled for continuous updates.
• Debugging: The platform features built-in monitoring and debugging tools through Amazon CloudWatch. These tools track job status, resource usage, and troubleshoot issues.

Use cases:

• Automated migration: Service providers can replace their legacy on-premise ETL solutions with AWS Glue to process millions of records in a serverless environment. This serverless architecture improves accuracy and scalability while reducing operational overhead.
• Incremental data processing: Companies with mixed data ingestion patterns use AWS Glue’s job bookmarks to process only new or changed data during ETL runs. This feature keeps track of past ETL jobs and makes it easier to process only new or changed data.
• Personalization at scale: AWS Glue processes streaming clickstream data from Amazon Kinesis Data Streams, enriching it with user attributes from Amazon DynamoDB or S3-based customer profiles. This enables e-commerce platforms to serve personalized product recommendations in near real-time.

Limitations:

• AWS Glue only supports two languages, Python and Scala.
• Not ideal for XML file formats and loses connectivity.
• Some transformation components are not resourceful.

Airbyte vs AWS Glue vs Hevo: Detailed Comparison Table

Below is a detailed comparison table of Airbyte vs AWS Glue vs Hevo to help you make an informed choice:

Airbyte vs AWS Glue: In-Depth Feature & Use Case Comparison

Both Airbyte and AWS Glue are remarkable data integration tools, but their performance often differs in different use case scenarios and features. Let’s compare both tools across key factors:

1. Extensibility

Airbyte features one of the broadest connector libraries that spans complex file formats, SaaS applications, databases, and APIs. Their library is still growing and receives regular contributions from Airbyte’s strong open-source community of developers and programmers. For niche data sources, users can even build custom connectors using its Python-led Connector Development Kit (CDK).

On the other hand, AWS Glue’s connectivity majorly focuses on integrating with tools within the AWS ecosystem. Users can connect external data sources through Spark and JDBC, which is complex to manage and decreases flexibility to work with complex and niche data sources.

Choose Airbyte for a broad connector coverage and AWS Glue for teams deeply embedded in the AWS ecosystem seeking seamless native integration.

2. Data transformation and processing

Airbyte mainly focuses on extracting and loading datasets into destinations where transformations are performed. The transformations happen through DBT for SQL-based transformations without any native transformation engines. While it integrates with external transformation tools, it falls short in providing versatile transformation support.

The AWS Glue architecture features a Spark-based ETL engine that supports complex transformations via PySpark and Scala scripts. Moreover, it features visual tools like Glue Studio and a drag-and-drop interface for no-code data preparation. Glue’s native transformation capabilities are more advanced and integrated compared to Airbyte’s reliance on external tools.

While Airbyte is ideal for dbt-based SQL transformations, AWS Glue is well-suited for teams needing native transformation support and full compute control.

3. Deployment

Airbyte is designed to support diverse deployments, including self-hosting, cloud-based, on-premise, and SaaS applications. This flexibility allows organizations to control data residency, compliance, and operational management, making it ideal for multi-cloud, hybrid, or regulated environments.

The serverless framework of AWS Glue handles the entire underlying infrastructure. This automatically provisions, scales compute resources, and allows teams to focus on integration logic. However, its deployment is limited to the AWS cloud with no support for on-premise and multi-cloud deployments.

Choose Airbyte for flexible deployment across diverse environments, and AWS Glue for deep AWS integration and zero infrastructure management.

When to Choose Airbyte?

Airbyte is a good option when you prioritize flexibility and want to scale across complex data pipelines. Here’s when to choose it:

For long-tail data integration

If your organization works with niche data sources, you can use Airbyte to develop customized connectors and deploy them within the pipeline. For teams with specialized requirements, Airbyte’s flexibility provides a significant advantage over closed, SaaS-only platforms.

For AI/ML workflows

Airbyte is designed to support data integrations where pipelines process large-scale volumes of structured, semi-structured, and unstructured datasets for analytics and AI/ML applications. Its robust compatibility with orchestration tools like Airflow, Prefect, and Dagster enables production-grade data pipelines.

For data residency and compliance needs

Choose Airbyte when you require control over deployment and data residency, such as running pipelines on-premises, in cloud environments, or in fully-managed SaaS APIs. This is ideal for organizations with strict compliance and data sovereignty requirements.

When to Choose AWS Glue?

AWS excels in scaling across the AWS ecosystem and building serverless data integration pipelines. Here’s when to choose it:

For metadata management

The AWS Glue Data Catalog serves as a unified metastore for diverse data systems to support seamless integration with Amazon S3, Redshift, RDS, and EMR clusters. Organizations can keep metadata up-to-date using crawlers to make data discovery easy for analysts and BI tools.

For event-driven data processing

Glue can process streaming data from sources like Amazon Kinesis, AWS MSK, or Kafka, and load it into data lakes or warehouses for immediate analysis. Its native integration with AWS EventBridge and S3 events triggers ETL jobs automatically as new data arrives to facilitate fraud detection, IoT analytics, or dynamic reporting.

For hybrid data integration

AWS Glue is ideal for organizations migrating from on-premises deployment to AWS. It supports the migration of data stores to Amazon S3 or Redshift, automating the ETL process and minimizing the need to rewrite existing jobs.

Why Does Hevo Stand Out?

Among Hevo vs Airbyte vs AWS Glue, Hevo stands out as a remarkable choice due to its intuitive web interface and streaming capabilities with minimum latency. The platform scales with evolving data needs to ensure consistent performance and reliability.

In addition, Hevo provides built-in real-time monitoring dashboards to track pipeline health, throughput, latency, and errors. Users can configure custom alerts for pipeline anomalies for immediate troubleshooting.

The pre-built data models and templates accelerated deployment for common analytics and BI use cases with industry-standard security certifications.
Sign up for Hevo’s 14-day free trial today and experience seamless data integration with advanced features.

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