Airbyte vs Azure Data Factory

Feature Comparison between Airbyte & Azure Data Factory

Here’s a tabular comparison that helps you understand the Airbyte vs Azure Data Factory differences:

Key Distinction between Airbyte & Azure Data Factory

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.

Personalized Connector Development

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.

Streamlining AI Workflows

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.

CDC Support

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:

• CDC Factory Resource: The fastest way to begin using CDC in ADF is through a factory-level CDC resource, which enables you to capture incremental data.‍
• Native CDC in Data Flows: ADF mapping data flows help you automatically detect and process inserted, deleted, and updated data from the source store.‍
• Auto Incremental Extraction: This CDC option uses ADF mapping data flows to extract only new or updated data from sources.‍
• Customer-Managed Delta Data Extraction: Delta data refers to the changes made to a dataset since the last time it was accessed or processed. You can build custom delta data extraction pipelines in ADF for all supported connectors using lookup activity. This pipeline helps you track and process delta data based on a timestamp or ID column.

Regulatory Compliance Certifications

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.

Community Support

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.

Pricing

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:

• Pipeline Orchestration: This includes the execution of activity runs, debug runs, and triggers. For 1,000 activity runs, ADF will charge you $1 per integration runtime hour.
• ‍Pipeline Activity Execution: Activities such as delete, schema operations, and lookup occur during the integration runtime. The cost of a pipeline activity is $0.005 per 1000 activity runs.
• ‍Data Flow Execution and Debugging: You must pay for data flow execution and debugging time per vCore-hour. The price for general-purpose execution is $0.274/vCore-hour, while optimized execution charges $0.343.
• ‍Data Factory Operations: In ADF, read/write operations are charged at $0.50/50,000 updated entries, while monitoring activities incur a fee of $0.25/50,000 retrieved records.

Benefits of Airbyte

• You can simplify data integration by moving large datasets across different platforms through Airbyte’s no-code/low-code interface and a vast library of pre-built connectors.
• With Airbyte and dbt integration, you can define custom transformations to clean and standardize your data into a highly usable format for analytics and reporting.
• Airbyte enables you to build ETL pipelines using an open-source Python library called PyAirbyte. PyAirbyte allows you to extract data from various sources using Airbyte connectors in your Python environment.
• Airbyte provides a record change history feature, which allows you to keep track of all changes made to the data.
• You can deploy Airbyte in various environments, including on-premises, cloud, and hybrid setups, which offers flexibility to meet your organizational preferences.
• By utilizing Airbyte, you can reduce the cost associated with data integration compared to traditional ETL tools, especially with the free, open-source version.
• By integrating with data orchestrators such as Airflow, Dagster, Prefect, and Kestra, you can efficiently automate and coordinate the data workflows across different platforms.
• After configuring schema settings, Airbyte helps you automatically check for source schema changes. For cloud users, it is checked every 15 minutes, and for self-hosted users, once every 24 hours.
• Airbyte allows you to move data from all your sources into AI-enabled data warehouses such as Snowflake Cortex and BigQuery Vertex AI.

Limitations with Azure Data Factory

• ADF supports only 90 built-in connectors, which may not cover all data sources and destinations that your organization requires.
• Due to its extensive features and capabilities, ADF can have a learning curve for users unfamiliar with cloud data integration tools. As a result, additional training might be required.
• ADF does not have vibrant community contributions compared to open-source solutions like Airbyte.
• ADF’s billing costs vary based on data movement, pipeline orchestration, and activity execution, making it difficult to estimate total expenses.

Conclusion

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.

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!

A Brief Overview of Airbyte

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:

• Enterprise GA: Airbyte introduced Airbyte Self-Managed Enterprise in general availability (Enterprise GA). With centralized user access and self-service data ingestion features, Enterprise GA is great for managing multiple teams and projects in one Airbyte setup. It can even help you secure sensitive data by hashing personally identifiable information (PII) as it flows through your pipeline.
• ‍Resumable Full Refresh: The resumable full refresh feature is an improved version of Airbyte’s full refresh sync mode that enhances data synchronization. It allows you to restart failed sync operations from where they left off rather than starting from scratch. This is useful when handling large datasets, and interruptions may occur due to network issues or resource constraints.
• ‍Pipeline Job Monitoring: Airbyte’s notifications and webhooks help you monitor the health of your pipeline jobs. You will receive alerts for successful syncs, failed jobs, and schema changes through email or Slack. For enhanced automation, you can leverage webhooks to trigger actions, such as notifications in other systems, based on the events during the sync. This functionality minimizes the time spent on monitoring pipelines.

A Brief Overview of Azure Data Factory

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:

• Data Compression: Azure Data Factory allows you to compress data during the Copy data activity before writing it to the destination. This feature helps reduce the amount of data transferred, enabling you to optimize bandwidth usage and lower migration times. The supported compression formats in ADF are GZIP, BZIP2, Deflate, LZO, ZipDeflate, and Snappy.
• ‍CI/CD Support: ADF fully supports Continuous Integration/Continuous Delivery (CI/CD) for your data pipelines through the integration of Azure DevOps and GitHub. With CI/CD pipeline, you can build and update data pipeline workflows before final deployment.
• ‍Pipeline Monitoring: Once you develop and deploy the integration pipeline, you must track it and its scheduled activities to assess success and failure rates. ADF has built-in pipeline monitoring capabilities through APIs, PowerShell, Azure Monitor logs, and the Azure portal’s health panels.