Feature Comparison between Airbyte & Azure Data Factory
Here’s a tabular comparison that helps you understand the Airbyte vs Azure Data Factory differences:
Compare Airbyte and Azure Data Factory in this blog. Discover which data integration tool offers the best features for your ETL needs and workflows.
Here’s a tabular comparison that helps you understand the Airbyte vs Azure Data Factory differences:
Let’s take a look at the critical differences between Airbyte and Azure Data Factory. This will enable you to choose the right one that meets your migration criteria.
Besides Airbyte connectors, Airbyte offers no-code connector builder and low-code CDKs, enabling you to develop custom data pipelines for unsupported sources or destinations. To further enhance this process, Airbyte offers an AI assistant within the connector builder to automate this process by prefilling the configuration fields.
The newer version also launched a connector marketplace featuring hundreds of connectors contributed by a large community. All marketplace connectors are built using Airbyte’s low-code CDKs, allowing you to use them as they are or customize them based on your needs. Although the Airbyte team does not maintain these connectors or offer SLA support, marketplace connectors with high success rates may be upgraded as official ones.
In contrast, ADF does not provide a direct way to build custom connectors. Instead, you must perform multiple steps using Azure Functions or REST API integration. This increases the development time and adds complexity to ongoing maintenance and troubleshooting. While custom solutions can be built, ADF lacks the flexibility of AI-powered connector builder features to speed up personalized connector development.
Using trusted and up-to-date data, your AI models can perform better. However, without an efficient pipeline, large language models (LLMs) can suffer from delays, redundancies, and resource wastage. With Airbyte, you can easily load heterogeneous types of data from disparate sources into a vector database such as Pinecone, Milvus, and Weavite.
Before data transfer, you can apply RAG-based transformations, like LangChain-powered chunking, to split the large datasets into small units. After partitioning the datasets, you can generate embeddings using OpenAI or Cohere embedding models. Then, index these embeddings to store them in the vector databases for optimized searching and retrieval.
On the other hand, ADF supports AI and RAG workflows but depends on integrations with Azure Machine Learning, Cognitive Services, and Azure Synapse. These solutions can help simplify AI application development, enabling you to create data pipelines that feed directly into machine learning models or other AI services.
Airbyte offers multiple sync modes, such as resumable full refresh, incremental, and full refresh with the deduplication option. These options allow you to track and synchronize the changes in the source system and replicate them to the destination to make it up-to-date.
With the release of Airbyte 1.0, you have the flexibility to reload historical data without downtime using the Refresh Sync feature. Unlike the Reset operation, Refresh allows you to remove the old data only after the new dataset is successfully read.
Besides these CDC syncs, Airbyte also supports very large incremental CDC syncs through its WASS (WAL Acquisition Synchronization System) algorithm. With WASS, you can periodically switch between capturing an initial data snapshot and reading the transaction log, preventing long-term buildup in the log. Airbyte also enables you to combine these features with database checkpointing capabilities to help you save the current state of the database at specific intervals. Together, these strategies allow you to sync databases of any size.
Conversely, ADF offers different CDC options:
Airbyte has several compliance certifications, including HIPAA, GDPR, ISO 27001, and SOC 2 Type II, which ensure safe integration and processing.
ADF also focuses on regulatory compliance standards. The key certifications include ISO/IEC 27001, FedRAMP, SOC 1 and SOC 2, and GDPR.
Airbyte’s active forum allows you to discuss topics like deployment tips, troubleshooting issues, and data integration practices. Airbyte also provides knowledgeable documentation, tutorials, and YouTube videos to help you build data pipelines with less complexity. For additional support, you can contact their customer support team.
Contrarily, ADF also provides online documentation, community forums, and email support. However, it offers additional customer service with its pricing plans— Standard, Professional Direct, and Premier.
Besides its open-source edition, Airbyte provides three predictable and scalable pricing options: Airbyte Cloud, Team, and Enterprise. If you are a data professional seeking an efficient way to consolidate data across different systems, utilize the Airbyte Cloud plan. The pricing structure of this plan is volume-based in which you are charged according to the number of rows stored or processed. If your organization needs a scalable option to manage vast datasets, Airbyte offers a cloud-hosted plan, Team. For those prioritizing security and control, the Enterprise edition would be a great choice. This edition includes enterprise support with SLA and enables self-hosting in your own Virtual Private Clouds (VPCs). The pricing model of both Teams and Enterprise editions is capacity-based. In this billing structure, the cost depends on the number of Airbyte connections you need to sync data and the frequency of refreshing the data.
In comparison, pricing in ADF is determined by several pipeline tasks:
In the Airbyte vs Azure Data Factory comparison, both solutions offer unique advantages tailored to different organizational needs.
Airbyte helps you streamline the integration process with its open-source flexibility, extensive connector catalog, generative AI support, vibrant community, and many more features. In contrast, Azure Data Factory offers CDC and transformation capabilities. Its efficient integration within the Azure ecosystem makes it useful for organizations already invested in Microsoft services.
Ultimately, the choice between Airbyte and Azure Data Factory depends on your specific use cases, existing infrastructure, and the level of customization required.
Discover the keys to enhancing data pipeline performance while minimizing costs with this benchmark analysis by McKnight Consulting Group.
Airbyte and Azure Data Factory are powerful data integration tools that enable you to build data pipelines for efficient migration across multiple platforms. However, each solution has unique features tailored to different use cases. If you are uncertain which solution better meets your requirements, this article helps you make a decision. Explore the Airbyte vs Azure Data Factory key differences in this comparison guide and choose the best one for your migration goals.
Let’s get started!
Airbyte is a data movement and replication platform used by over 20,000 data and AI professionals to handle varied data across multi-cloud environments. With over 550+ pre-built connectors, you can efficiently migrate data from API, databases, SaaS, and other sources to data warehouses, lakes, or vector databases. Alongside the built-in connectors, Airbyte offers three ways to develop personalized connectors—a no-code connector builder, low-code CDK, and language-specific CDKs.
Let’s take a look at a few features of Airbyte:
Azure Data Factory (ADF) is Microsoft Azure’s fully managed, serverless data integration and transformation service. It helps you build code-free ETL/ELT pipelines by using drag-and-drop data movement activities, such as Copy data activity.
With ADF, you can also create Data flow activities using its mapping data flow feature to clean and standardize your data. Data flow, an ADF-managed Apache Spark cluster, helps you develop and manage complex transformation graphs that run on Spark without requiring Spark programming knowledge. Once you extract and transform data, you can load it into a cloud or on-premise centralized data store for analytics and reporting.
Here are some of the features of Azure Data Factory:
Airbyte has become our single point of data integration. We continuously migrate our connectors from our existing solutions to Airbyte as they became available, and extensibly leverage their connector builder on Airbyte Cloud.
Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.
We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria.
The value of being able to scale and execute at a high level by maximizing resources is immense