Airbyte and dltHub are two data integration / ETL platforms. Compare supported data sources and destinations, features, pricing, and more. Understand their differences along with key pros and cons.
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Airbyte is the open standard in data movement, and can be deployed self-hosted, cloud, or hybrid. Airbyte is used by 18% of the F500 and has over 25,000 community members.
Qlik (which acquired Talend) offers enterprise data integration and analytics solutions. Using enterprise licensing and focusing on real-time replication, Qlik delivers powerful capabilities but with significant complexity and cost.
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Airbyte gives you complete control over your data infrastructure with flexible deployment options that adapt to your security and compliance requirements. Whether you need to keep sensitive data on-premise for sovereignty requirements, leverage cloud scalability, or implement a hybrid approach, Airbyte's single codebase architecture ensures consistent functionality across all deployment models. This flexibility helps organizations meet strict compliance standards like GDPR and HIPAA while maintaining full ownership of their data pipeline infrastructure.
With over 600 pre-built connectors and an AI-powered connector builder, Airbyte removes the traditional barriers to data integration. The platform's extensive connector library covers everything from modern SaaS applications to legacy databases and unstructured data sources. When you need a custom connector, the no-code Connector Builder and low-code CDK enable rapid development in hours instead of weeks. This is amplified by a vibrant community of over 1000 contributors who continuously expand the ecosystem, ensuring you're never blocked by connector availability.
Airbyte's predictable capacity-based pricing model means you can scale your data operations without worrying about surprise bills or budget overruns. Unlike consumption-based models that penalize growth, Airbyte's transparent pricing grows predictably with your infrastructure needs. Combined with enterprise-grade reliability featuring 99.9% uptime SLAs and the freedom to choose between deployment options, organizations can confidently scale their data operations without vendor lock-in concerns.
As a Python library rather than a platform, dlt requires writing code for every aspect of your data pipeline. There's no visual interface, no point-and-click configuration, and no pre-built pipelines to leverage. Every data source requires custom development, even for common integrations like Salesforce or Google Analytics that other platforms provide out-of-the-box.
This code-only approach means non-technical team members cannot contribute to or modify data pipelines, creating bottlenecks and increasing the burden on engineering teams.
dlt provides only the most basic building blocks for data movement, lacking the comprehensive features expected in modern data integration platforms. There's no built-in monitoring dashboard, no alerting system, and no orchestration capabilities – these must all be implemented separately.
The library doesn't include data quality checks, transformation engines, or error handling mechanisms beyond basic Python exception handling. Teams often find themselves building extensive wrapper code and supplementary systems just to achieve functionality that comes standard in platform solutions.
With only community support available, organizations using dlt bear full responsibility for troubleshooting issues, optimizing performance, and ensuring reliability. There are no enterprise features like role-based access control, audit logs, or compliance certifications.
The DIY infrastructure approach means your team must handle everything from deployment and scaling to security and monitoring. This lack of managed services and enterprise support makes dlt unsuitable for mission-critical data pipelines where downtime has significant business impact.
How difficult is it to migrate from my current data integration platform to Airbyte?
Migration is straightforward. Airbyte supports the same sources and destinations as other platforms, so you can recreate your pipelines quickly. Our team provides migration assistance for Enterprise customers, and our community has created guides for switching from specific competitors. Most customers complete migration in days, not weeks.
Will I lose my custom connectors when switching to Airbyte?
No. If you've built custom connectors on platforms like Singer (used by Stitch), they'll work with Airbyte. For proprietary connectors, our AI-powered Connector Builder lets you recreate them in hours. Plus, with 600+ pre-built connectors, you may find we already support your custom sources.
How does Airbyte's open source model affect security and reliability?
Open source enhances security through transparency - you can audit every line of code. Airbyte maintains SOC 2 Type II, GDPR, and HIPAA compliance. Enterprise customers get SLAs, dedicated support, and the option to self-host for maximum control. Our code is battle-tested by thousands of companies worldwide.
What happens to my costs when switching from row-based or consumption pricing?
Most customers see significant cost savings with our predictable capacity-based pricing. No more surprise bills from data spikes or seasonal variations. You'll know exactly what you'll pay each month, and you can scale without fear.
Can Airbyte handle near real-time data syncs or is it limited like some batch-only platforms?
Airbyte excels at high-frequency batch workloads. We support log-based CDC for database replication and can sync as frequently as every 5 minutes for APIs. While we're optimized for reliable batch processing rather than streaming, our performance meets the freshness requirements of most modern analytics and AI applications.
Do I need engineering resources to manage Airbyte, or can my analysts handle it?
Airbyte is designed for both technical and non-technical users. Our UI makes pipeline creation point-and-click simple. The Connector Builder requires little coding knowledge. However, having technical resources unlocks advanced features like custom transformations, API deployment, and infrastructure optimization.
