7 Best Alternatives to AWS Glue in 2026

Why look beyond AWS Glue

AWS Glue serves as a managed extract, transform, and load (ETL) service within the Amazon Web Services ecosystem. It offers a serverless Apache Spark-based environment for data processing, a unified Data Catalog for metadata management, and various tools like Glue Studio for visual job authoring and Glue DataBrew for data preparation AWS Glue documentation. Despite its capabilities, there are several reasons why organizations might consider alternatives.

One primary factor is vendor lock-in; committing to AWS Glue deeply integrates data pipelines within the AWS ecosystem, potentially increasing migration costs if a multi-cloud strategy is pursued or if a different cloud provider offers more favorable terms. Additionally, while Glue is serverless, its cost model, based on Data Processing Units (DPU-hours), can become a significant factor for unpredictable or frequently running workloads AWS Glue pricing. Organizations already invested in other cloud platforms or on-premises infrastructure might find the overhead of integrating Glue with their existing systems more complex than using a native solution within their primary environment. Furthermore, specific feature sets, such as advanced data governance, real-time streaming capabilities, or specialized connectors, might be more robust or easier to implement with a dedicated alternative tailored to those needs.

Top alternatives ranked

1. 1. Google Cloud Dataflow — Unified stream and batch data processing

   Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines. It provides a serverless approach to both batch and stream data processing, enabling developers to write a single pipeline that can handle real-time and historical data. Dataflow automatically scales resources based on workload demands, eliminating the need for manual cluster management Google Cloud Dataflow overview. It integrates with other Google Cloud services like BigQuery, Cloud Storage, and Pub/Sub, making it suitable for building comprehensive data analytics solutions within the Google Cloud ecosystem.

   Dataflow is often chosen for its ability to unify batch and streaming processing, simplifying pipeline development and maintenance. Its autoscaling capabilities help optimize costs by only consuming resources when needed. Developers benefit from the Apache Beam SDK, which supports multiple programming languages, including Java, Python, and Go, providing flexibility in pipeline construction Google Cloud Dataflow concepts. This makes it a strong contender for organizations seeking a managed, scalable, and unified data processing platform.

   • Best for: Real-time analytics, unified batch and stream processing, serverless data pipelines in Google Cloud.

2. 2. Azure Data Factory — Cloud-native data integration for Azure

   Azure Data Factory (ADF) is a cloud-based data integration service that allows users to create, schedule, and orchestrate data workflows (ETL/ELT pipelines) across various data stores. It supports a wide range of connectors to both on-premises and cloud data sources, including Azure services, Amazon S3, Google Cloud Storage, and various databases Azure Data Factory product page. ADF provides a visual interface for pipeline development, along with support for code-based authoring using JSON.

   ADF is particularly well-suited for organizations heavily invested in the Microsoft Azure ecosystem, offering seamless integration with services like Azure Synapse Analytics, Azure Databricks, and Azure SQL Database. Its ability to orchestrate complex data flows, including data movement, transformation, and control flow activities, makes it a versatile tool for enterprise-grade data integration. The service also offers capabilities for monitoring pipeline executions and managing data lineage Azure Data Factory documentation.

   • Best for: Data integration and orchestration within the Azure ecosystem, hybrid data movement, complex ETL/ELT pipelines.

3. 3. Databricks — Unified data analytics platform with Apache Spark

   Databricks offers a unified data analytics platform built on Apache Spark, providing an environment for data engineering, machine learning, and data warehousing. It supports various data sources and allows users to process large datasets using Spark, SQL, Python, R, and Scala Databricks homepage. Databricks can be deployed on AWS, Azure, and Google Cloud, offering multi-cloud flexibility.

   The platform is known for its Delta Lake technology, which provides ACID transactions, schema enforcement, and unified streaming and batch operations on data lakes. This makes Databricks a powerful choice for building reliable data lakes and lakehouses. It also includes capabilities for collaborative notebooks, MLOps, and SQL analytics, catering to a broad range of data professionals Databricks platform overview. Databricks is often favored by organizations requiring advanced analytics, machine learning integration, and a highly scalable Spark-based processing engine.

   • Best for: Data lakehouses, advanced analytics, machine learning pipelines, collaborative data science, multi-cloud deployments.

4. 4. AWS Lambda — Event-driven serverless compute for custom ETL

   AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume. For ETL workflows, Lambda functions can be triggered by various events, such as new files uploaded to S3, messages in an SQS queue, or scheduled events AWS Lambda documentation. This makes it suitable for event-driven, small-scale, or highly specific data transformations.

   While not a full-fledged ETL service like Glue, Lambda can be a cost-effective and highly flexible alternative for custom ETL tasks that don't require the full power of Spark. Developers can write transformation logic in various languages supported by Lambda, including Python, Node.js, and Java. It integrates seamlessly with other AWS services, allowing for the construction of custom, event-driven data pipelines. For complex transformations or large data volumes, it might be combined with other services like AWS Step Functions for orchestration or AWS Fargate for containerized processing AWS Lambda product page.

   • Best for: Event-driven micro-ETL tasks, custom lightweight data transformations, serverless orchestration of data processes.

5. 5. Google Kubernetes Engine — Containerized data processing at scale

   Google Kubernetes Engine (GKE) is a managed service for deploying and managing containerized applications using Kubernetes. While not an ETL service itself, GKE provides a powerful platform for running custom ETL tools and frameworks, such as Apache Spark, Apache Flink, or custom Python scripts, within containers Google Kubernetes Engine documentation. This approach offers significant control over the environment, resource allocation, and software versions.

   Organizations with existing Kubernetes expertise or a desire for greater portability and control over their data processing infrastructure may prefer GKE. It allows for fine-grained resource management, custom scaling policies, and the ability to integrate with various open-source data tools. Running ETL workloads on GKE can provide flexibility in choosing specific versions of Spark or other frameworks, and it supports hybrid and multi-cloud strategies by allowing consistent deployment patterns across different environments Google Kubernetes Engine overview. However, it requires more operational overhead compared to fully managed ETL services.

   • Best for: Custom ETL workloads requiring specific frameworks, containerized data processing, advanced infrastructure control, multi-cloud strategies.

6. 6. DigitalOcean Droplets + Managed Databases — Self-managed ETL infrastructure

   DigitalOcean Droplets are virtual machines (VMs) that provide flexible, scalable compute resources, while DigitalOcean Managed Databases offer fully managed PostgreSQL, MySQL, Redis, and MongoDB services. Together, these components can form a self-managed infrastructure for ETL processes. Developers can provision Droplets to run custom ETL scripts written in Python, Java, or other languages, and connect them to Managed Databases for source and target data DigitalOcean Droplets documentation.

   This alternative provides a high degree of control over the ETL environment, allowing for the installation of specific libraries, tools, and custom configurations not always available in managed services. It is often a cost-effective solution for small to medium-sized datasets or for organizations that prefer to manage their own infrastructure for compliance or control reasons. However, it requires significant operational effort for provisioning, scaling, monitoring, and maintaining the underlying VMs and database instances, contrasting with the serverless nature of AWS Glue DigitalOcean Managed Databases overview.

   • Best for: Cost-sensitive projects, custom ETL logic with full environment control, small to medium data volumes, organizations preferring self-managed infrastructure.

7. 7. Hetzner Cloud Servers + Managed Databases — Budget-friendly self-managed ETL

   Hetzner Cloud offers a range of virtual servers (Cloud Servers) and managed database services (PostgreSQL, MySQL). Similar to DigitalOcean, this combination allows for building a self-managed ETL infrastructure. Cloud Servers provide competitive pricing for compute resources, which can be used to host custom ETL applications, Apache Spark clusters, or other data processing engines. Managed Databases simplify the operational burden of running database instances for data sources and targets Hetzner Cloud documentation.

   Hetzner is often chosen for its aggressive pricing model, making it an attractive option for budget-conscious projects or startups. It provides robust performance for its cost, allowing users to run data transformations on dedicated or virtualized hardware. The flexibility to install any software on Cloud Servers means developers have complete control over their ETL stack. However, like other self-managed solutions, it requires the user to handle all aspects of infrastructure management, including scaling, patching, and backups, which can be time-consuming compared to fully managed services like AWS Glue Hetzner Cloud Managed Databases.

   • Best for: Budget-constrained ETL projects, full control over the tech stack, small to medium-sized data workloads, European data residency requirements.

Side-by-side

How to pick

Selecting an alternative to AWS Glue involves evaluating several factors, including your existing cloud infrastructure, specific data processing requirements, budget constraints, and team expertise.

Consider your cloud ecosystem:

• If your organization is primarily on Google Cloud, Google Cloud Dataflow is a strong contender. Its unified batch and streaming capabilities, combined with deep integration across Google Cloud services like BigQuery and Cloud Storage, make it a natural fit for building robust data pipelines within that environment.
• For those operating within the Microsoft Azure ecosystem, Azure Data Factory provides seamless integration with Azure Synapse Analytics, Azure Databricks, and other Azure data services. Its visual interface and extensive connector library simplify building complex data integration workflows.
• If you need multi-cloud flexibility or advanced data lakehouse capabilities, Databricks offers a powerful platform spanning AWS, Azure, and Google Cloud. Its focus on Apache Spark, Delta Lake, and integrated machine learning tools makes it suitable for sophisticated analytics and AI workloads, regardless of your primary cloud provider.

Evaluate your data processing needs:

• For event-driven, lightweight transformations or custom logic triggered by specific events (e.g., new file uploads), AWS Lambda can be a cost-effective and highly responsive option, especially for micro-ETL tasks.
• If you require significant control over your processing environment, want to use specific open-source frameworks, or pursue a container-first strategy, Google Kubernetes Engine (GKE) provides a robust platform for deploying and managing custom ETL applications. This approach demands more operational expertise but offers unparalleled flexibility.
• For organizations with smaller budgets or a preference for self-managed infrastructure, DigitalOcean Droplets + Managed Databases or Hetzner Cloud Servers + Managed Databases offer cost-effective virtual machines and managed database services. These options give you full control over your stack but require more manual effort for setup, scaling, and maintenance.

Consider team expertise and operational overhead:

• Fully managed services like Google Cloud Dataflow and Azure Data Factory abstract away infrastructure management, allowing your team to focus on data logic.
• Platforms like Databricks offer a managed Spark environment but still provide significant control and require Spark expertise.
• Solutions built on AWS Lambda or Google Kubernetes Engine require development and operational expertise in those respective services and frameworks.
• Self-managed options on DigitalOcean or Hetzner demand comprehensive infrastructure management skills from your team.

Ultimately, the best alternative aligns with your technical requirements, budget, team's skill set, and long-term strategic goals regarding vendor lock-in and multi-cloud portability.