Azure Data Factory Reviews & Pricing

by Microsoft | Founded 1975, Redmond, WA

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ETL Tools
Data Integration Tools
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What is Azure Data Factory?

Industry Specialties: Serves all industries

Azure Data Factory orchestrates data movement and transformation across diverse cloud and on-premises sources. It caters to businesses struggling with data silos and complex integration needs. Key benefits include its visual interface for building ETL/ELT pipelines, native connectors to various data stores, and serverless execution for scalable data processing. User experiences highlight its ease of use, robust scheduling capabilities, and powerful data transformation tools. Compared to similar offerings, Azure Data Factory shines in its cloud-native design, integration with other Azure services, and cost-effective pay-per-use pricing based on data volume and execution duration.

Pros

Visual ETL/ELT builder
Native data store connectors
Serverless execution
Easy scheduling
Powerful data transformations

Cons

Limited custom code options
Steep learning curve for complex workflows
Potential cost increase with high data volume
Limited debugging options
Less control over serverless execution

PRICE

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DEPLOYMENT

PLATFORM

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User Sentiment i

Based on 128 reviews:

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#9

Azure Data Factory is ranked #9 on the top 10 ETL Tools leaderboard based on a comprehensive analysis performed by SelectHub research analysts. Compare the leaders with our In-Depth Report.

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Azure Data Factory Pricing

Based on our most recent analysis, Azure Data Factory pricing starts at $0 (Per DIU Hour).

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Price: $
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Starting From: $0.07
Pricing Model: Per DIU Hour
Free Trial: Yes, Request for Free

Training Resources

Azure Data Factory is supported with the following types of training:

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In Person
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Support

The following support services are available for Azure Data Factory:

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Azure Data Factory Benefits and Insights

Why use Azure Data Factory?

Key differentiators & advantages of Azure Data Factory

Streamlined Data Orchestration: Simplify data movement across diverse on-premises, cloud, and hybrid environments with a unified platform.
Boosted Developer Productivity: Leverage code-free and low-code data flows to build and manage pipelines without writing extensive scripts, saving time and resources.
Enhanced Scalability and Elasticity: Scale data pipelines seamlessly to handle fluctuating data volumes without infrastructure limitations, ensuring smooth performance.
Reduced Costs and Optimization: Pay-as-you-go pricing model and built-in optimization tools minimize infrastructure costs and maximize resource utilization.
Unified Data Governance: Implement consistent data security and compliance policies across all integrated data sources, ensuring data integrity and trust.
Accelerated Data Insights: Deliver faster and more reliable data pipelines to your analytics platforms, enabling faster time-to-insights and data-driven decision making.
Streamlined Data Migration: Easily migrate existing data integration workloads, including SSIS packages, to the cloud with minimal disruption and effort.
Rich Ecosystem of Connectors: Integrate with a vast array of on-premises and cloud data sources and applications, fostering a truly connected data landscape.
Enhanced Monitoring and Alerting: Gain real-time visibility into pipeline performance and proactively address potential issues with built-in monitoring and alerting features.
Continuous Innovation: Benefit from Microsoft's ongoing updates and enhancements to the platform, ensuring access to the latest data integration capabilities.

Industry Expertise

While Azure Data Factory excels at general data integration across diverse environments, it boasts specific strengths in certain industries. Its pre-built connectors and industry-specific templates shine in healthcare, finance, retail, and manufacturing. For example, pre-configured connectors streamline HL7 data ingestion in healthcare, while financial institutions leverage built-in data quality checks for regulatory compliance. Retailers benefit from automated marketing campaign data pipelines, and manufacturers gain real-time production insights through sensor data integration. Azure Data Factory's industry expertise goes beyond connectors, offering best practices and reference architectures tailored to specific needs, accelerating data-driven success in various sectors.

Azure Data Factory Reviews

Average customer reviews & user sentiment summary for Azure Data Factory:

128 reviews

88%

of users would recommend this product

Azure Data Factory has a 'great' User Satisfaction Rating of 88% when considering 128 user reviews from 3 recognized software review sites.

Synopsis of User Ratings and Reviews

Based on an aggregate of Azure Data Factory reviews taken from the sources above, the following pros & cons have been curated by a SelectHub Market Analyst.

Pros

Ease of Use for ETL/ELT Tasks: Users praise the intuitive drag-and-drop interface and pre-built connectors for simplifying data movement and transformation, even for complex ETL/ELT scenarios.
Faster Time to Insights: Many users highlight the improved data pipeline efficiency leading to quicker data availability for analysis and decision-making.
Cost Savings and Optimization: Pay-as-you-go pricing and built-in optimization features are frequently mentioned as helping users keep data integration costs under control.
Reduced Development Time: Code-free and low-code capabilities are appreciated for enabling faster pipeline development and reducing reliance on coding expertise.
Improved Data Governance: Unified data security and compliance across hybrid environments are valued by users dealing with sensitive data.

Cons

Limited Debugging Tools: Troubleshooting complex pipelines can be challenging due to lack of advanced debugging features and reliance on basic log analysis.
Cost Overruns: Unoptimized pipelines or unexpected usage spikes can lead to higher-than-anticipated costs in the pay-as-you-go model.
Learning Curve for Data Flows: The code-free data flow visual designer, while powerful, can have a learning curve for non-technical users, hindering adoption.
Azure Ecosystem Reliance: Integration with non-Azure services often requires workarounds or custom development, limiting flexibility.
Version Control Challenges: Lack of native version control features necessitates integration with external tools for effective pipeline management.

Researcher's Summary:

Overall, user reviews of Azure Data Factory (ADF) paint a picture of a powerful and versatile data integration tool with both strengths and limitations. Many users praise its ease of use, particularly the drag-and-drop interface and pre-built connectors, which significantly simplify ETL/ELT tasks even for complex scenarios. This is especially valuable for reducing development time and making data pipelines accessible to users with less coding expertise.

Another major advantage highlighted by users is faster time to insights. Streamlined data pipelines in ADF lead to quicker data availability for analysis, enabling data-driven decision making with minimal delay. Additionally, the pay-as-you-go pricing model and built-in optimization features are appreciated for helping users control costs. This is particularly important for organizations with fluctuating data volumes or unpredictable usage patterns.

However, some limitations also emerge from user reviews. Debugging complex pipelines can be challenging due to the lack of advanced debugging tools and reliance on basic logging. This can lead to frustration and lost time when troubleshooting issues. Additionally, the learning curve for data flows, while ultimately powerful, can hinder adoption for less technical users who might prefer a more code-centric approach.

Compared to similar products, ADF's strengths lie in its user-friendliness, scalability, and cost-effectiveness. Notably, its extensive library of pre-built connectors gives it an edge over some competitors in terms of out-of-the-box integration capabilities. However, other tools might offer more advanced debugging features or cater better to users with strong coding skills.

Ultimately, the decision of whether ADF is the right choice depends on individual needs and priorities. For organizations looking for a user-friendly, scalable, and cost-effective data integration solution, ADF is a strong contender. However, it's essential to consider its limitations, particularly around debugging and data flow learning curve, and compare it to alternative tools to ensure the best fit for specific requirements.

Key Features

Data Source Connectivity: Visually integrate data sources with more than 90 pre-defined connectors through guided workflows. Connect to Amazon Redshift, Google BigQuery, HDFS, Oracle Exadata, Teradata, Salesforce, Marketo and ServiceNow, and all Azure data services. View data previews and customize as needed.
Mapping Data Flow: Design code-free data transformation logic with an intuitive interface and visual tools. Schedule, control and monitor transformation tasks with easy point-and-click actions — the vendor manages code translation, path optimization and job runs at the back end.
Authoring: Drag and drop to create end-to-end data processing workflows – from ingestion to reporting. Operationalize the pipeline using Apache Hive, Apache Pig, Azure HDInsight, Apache Spark and Azure Databricks. Upload data to warehouses like Azure Storage, then connect to analytics platforms for visual insights and reporting.
Debugging: Debug the data pipeline as a whole or in parts — set breakpoints on specific workflows.
Data Processing: Set event and schedule-based triggers to kick off the pipelines. Scales with Azure Event Grid to run event-based processing after upstream operations are complete. Speeds up ML-based pipelines and retrains processes as new data comes in.

Limitations

Notable limitations of Azure Data Factory include:

Debugging Complexity: Troubleshooting complex pipelines can be time-consuming due to limited debugging tools and reliance on logging.
Version Control Challenges: Native version control features are currently lacking, requiring integration with external tools for effective pipeline management.
Learning Curve for Data Flows: The code-free data flow visual designer has a learning curve, potentially hindering adoption for less technical users.
Cost Overruns: Unoptimized pipelines or unexpected usage spikes can lead to higher-than-anticipated costs.
Azure Ecosystem Reliance: Integration with non-Azure services may require additional workarounds or custom development.

Suite Support

Subscribe to the Basic plan for free 24*7 access to online training, documentation, how-to videos and community resources. Get third-party software support with configuration guidance and troubleshooting with the Developer plan; only email assistance is available during business hours.

Sign up for the Standard plan for round-the-clock support. Train with Azure engineers, and get personalized expert guidance with the Professional Direct plan.

Support hours are from 6 a.m. to 6 p.m. (PST), Monday through Friday, excluding holidays.

Email: Not specified.

Phone: Not specified.

Training: Sign up for data integration courses on the vendor’s website or get certified as an Azure Data Engineer Associate. Third-party websites offer paid training courses for the product.

Tickets: Submit a ticket on the vendor’s online support portal.

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