Overview
What is Azure Data Factory?
Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own…
One of the best and reliable ETL & ELT platforms for pulling data from multiple sources
Azure Databricks
Simple Solution to Data Migration
Database management and ETL tool for big data that is smart and reliable
ADF is awesome!
Azure Data Factory - Don't Abandon SSIS Just Yet
Awards
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Popular Features
- Connect to traditional data sources (7)9.191%
- Simple transformations (7)9.191%
- Connecto to Big Data and NoSQL (7)9.090%
- Complex transformations (7)7.878%
Reviewer Pros & Cons
Pricing
What is Azure Data Factory?
Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than…
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- Free/Freemium Version
- Premium Consulting/Integration Services
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Features
Data Source Connection
Ability to connect to multiple data sources
- 9.1Connect to traditional data sources(7) Ratings
Ability to connect to traditional data sources like relational databases, flat files, XML files and packaged applications
- 9Connecto to Big Data and NoSQL(7) Ratings
Ability to connect to non-traditional data sources like Hadoop and other big data technologies, and NoSQL databases
Data Transformations
Data transformations include calculations, search and replace, data normalization and data parsing
- 9.1Simple transformations(7) Ratings
Simple data transformations are calculations, data type conversions, aggregations and search and replace operations
- 7.8Complex transformations(7) Ratings
Complex data transformations are data normalization, advanced data parsing, etc.
Data Modeling
A data model is a diagram or flowchart that illustrates the relationships between data
- 8.2Data model creation(5) Ratings
Ability to create and maintain data models using a graphical tool to define relationships between data
- 7.3Metadata management(6) Ratings
Automated discovery of metadata with ability to synchronize and share metadata with other tools like Master Data Management
- 7.3Business rules and workflow(7) Ratings
Ability to define and manage business rules and workflows
- 6.6Collaboration(6) Ratings
Collaboration is enabled by a shared repository of project information and metadata
- 7.3Testing and debugging(7) Ratings
Tool to debug and tune for optimal performance
Data Governance
Data governance is the practise of implementing policies defining effective use of an organization's data assets
- 7.3Integration with data quality tools(7) Ratings
Integration with tools for cleansing, parsing and normalizing data according to business rules
- 8Integration with MDM tools(7) Ratings
Integration with master data management tools to ensure data consistency across the organization
Product Details
- About
- Tech Details
- FAQs
What is Azure Data Factory?
Azure Data Factory Technical Details
Operating Systems | Unspecified |
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Mobile Application | No |
Frequently Asked Questions
Comparisons
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Reviews and Ratings
(57)Attribute Ratings
Reviews
(1-7 of 7)The go-to ETL tool for most situations
Transformations: Azure Data Factory's data flow transformations help us clean, transform, and enrich our data before loading it to the destination. This is crucial for maintaining data quality, especially when dealing with diverse datasets.
- Azure Data Factory supports a vast array of source and destination connectors, both from within the Microsoft ecosystem (like Azure Blob Storage, Azure SQL Database, Azure Cosmos DB) and external platforms (like Amazon S3, Google Cloud Storage, SAP, Salesforce, and many more).
- Azure Data Factory's Mapping Data Flows provides a code-free environment to design data transformations visually. Users can drag and drop elements to create complex ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes without needing to write any code.
- Azure Data Factory provides a unified monitoring dashboard that offers a holistic view of all pipeline activities. I think this makes it easier for users to track the status of various jobs, identify failures, and pinpoint bottlenecks.
- Granularity of Errors: Sometimes, Azure Data Factory provides error messages that are too generic or vague for us, making it challenging to pinpoint the exact cause of a pipeline failure. Enhanced error messages with more actionable details would greatly assist us as users in debugging their pipelines.
- Pipeline Design UI: In my experience, the visual interface for designing pipelines, especially when dealing with complex workflows or numerous activities, can become cluttered. I think a more intuitive and scalable design interface would improve usability. In my opinion, features like zoom, better alignment tools, or grouping capabilities could make managing intricate designs more manageable.
- Native Support: While Azure Data Factory does support incremental data loads, in my experience, the setup can be somewhat manual and complex. I think native and more straightforward support for Change Data Capture, especially from popular databases, would simplify the process of capturing and processing only the changed data, making regular data updates more efficient
When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources.
Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing.
For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy.
Less Appropriate Scenarios for Azure Data Factory:
Real-time Data Streaming - Azure Data Factory is primarily batch-oriented.
Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice.
Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
One of the best and reliable ETL & ELT platforms for pulling data from multiple sources
- It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
- We can use linked service in multiple pipeline/data load.
- It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.
- For complex JSON when it comes to mapping nested attribute it's not easy to flatten out
- Data Factory V1 does not have a good implementation experience as compared to V2
- Work with on premise solutions sometimes is not too friendly because you will need to set a VPN
Azure Databricks
- Orchestration engine
- Low code Data pipeline
- Logic apps integration
- Error Flagging, Details of the error code is not specific especially faced this during Azure Table load
- Missing feature of Data exploration functionality similar to Synapse Data explorer
- missing access to orchestrate/create stream analytics job
- Offers low code/no code features executes against spark pool.
- Batch processing features, Tight coupling with Databricks & ETL jobs.
- Offers Logic apps & Azure functions invoking API.
- Not much inherent features of Stream analytics (Liasing Azure Stream analytics to DF might be good option).
- Advanced load options viz . Upsert type operations missing.
Simple Solution to Data Migration
- Orchestration
- On premises support
- Support to vast no of data connectors
- Cloud migration
- Native transform functions missing.
- Pricing
- Limited trigger functions.
- Creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code.
- Hybrid data integration is easily and agilely possible through this software.
- It has lot of various useful components
- It should integrate more ETL and audit functionality.
- Pipelines lack flexibility because moving Data Factory pipelines between different environments, such as for development or testing, require increased security and flexibility.
- The number of pre-defined templates is small and they should have more variety.
ADF is awesome!
- Cloud-based
- Fast
- Reliable
- Some features exist on the UI but are not implemented
- Its always changing
Azure Data Factory - Don't Abandon SSIS Just Yet
- Easy to set up and get started.
- Runtimes make integration with on-prem data simple and also allow for support of existing investments in SSIS.
- Limited source/sink (target) connectors depending on which area of Azure Data Factory you are using.
- Does not yet have parity with SSIS as far as the transforms available.