List of Microsoft Azure Data Factory Customers
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Since 2010, our global team of researchers has been studying Microsoft Azure Data Factory customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Microsoft Azure Data Factory for Extract, Transform, and Load (ETL) from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Microsoft Azure Data Factory for Extract, Transform, and Load (ETL) include: Centene, a United States based Healthcare organisation with 60500 employees and revenues of $163.07 billion, Allstate, a United States based Insurance organisation with 55000 employees and revenues of $67.69 billion, Ferguson Enterprises, a United States based Distribution organisation with 35000 employees and revenues of $29.74 billion, Tenet Healthcare, a United States based Healthcare organisation with 74480 employees and revenues of $20.67 billion, Chubb USA, a United States based Insurance organisation with 31000 employees and revenues of $13.02 billion and many others.
Contact us if you need a completed and verified list of companies using Microsoft Azure Data Factory, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Microsoft Azure Data Factory customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Afiniti | Professional Services | 2000 | $350M | Bermuda | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2020 | n/a | In 2020, Afiniti implemented Microsoft Azure Data Factory to orchestrate enterprise Extract, Transform, and Load (ETL) workflows as part of a broader Azure cloud data platform strategy. Microsoft Azure Data Factory was used to standardize pipeline orchestration and scheduling alongside batch and streaming ingestion, establishing a platform-level ETL capability for analytics and operational reporting. The implementation emphasized modular pipeline design and automation, leveraging mapping data flows, pipeline templates, and automated validation steps. Afiniti developed change data capture workflows and dimensional models using ErWin and DBT to standardize enterprise schemas, and established a Medallion Architecture with Snowflake and Databricks to separate raw, curated, and served layers for downstream AI workloads. Azure Data Factory was integrated with Databricks and Airflow for transformation orchestration and with Kafka and Spark for real-time streams, while C#.NET APIs were used to support system interoperability. The program consolidated data from MySQL, SQL Server, Greenplum, and PostgreSQL sources and supported a global data portal used by more than ten global teams, enabling analytics and AI-driven customer service pairing across product deployments in Europe. Governance and operationalization included capacity planning, pipeline validation automation, and recruitment and training of database engineering teams to maintain runbooks and support SLAs. Across the cloud migration and Azure Data Factory-centered platform, Afiniti reported reduced infrastructure costs by 40 percent and a 50 percent increase in data processing speeds, while pipeline automation and validation work reduced manual effort by 20 percent and supported 99.9 percent data integrity. | |
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AGL Energy, Ltd. | Utilities | 3894 | $856M | Australia | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2016 | n/a | In 2016 AGL Energy, Ltd. deployed Microsoft Azure Data Factory into its Enterprise Data Platform, establishing an Extract, Transform, and Load (ETL) layer to support a centralised Data Lake and Warehouse for analytics. The implementation of Microsoft Azure Data Factory was positioned inside the Enterprise Data Platform to handle pipeline orchestration and bulk data movement for AGL Corporate Services and adjacent business units. The implementation focused on authoring and operationalizing Azure Data Factory pipelines for data loading into downstream tables, including typing and staged loading tasks. Technical workstreams documented in-house included raw vault modelling, information mart design and modelling, database testing, and system and end to end testing to validate upstream feeds and downstream marts. Integrations explicitly used Azure Databricks to fetch and transform data from other sources and databases, with Azure Data Factory executing the orchestrated ETL flows. Operational scope covered centralised support for customer market, wholesale and group operations business units, with engineering and data teams in Melbourne coordinating development and pipeline deployment. Delivery and governance reflected agile practices used at AGL, including participation in the BEES Billing Experience Enhancement Scrum, creation of user stories, and collaboration between Data Engineers, Product Owners, Business Analysts and Solution Architects using tools such as JIRA and Confluence. Testing responsibilities and source documentation tasks were assigned to data engineering roles to ensure pipeline correctness and information mart readiness. | |
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Allstate | Insurance | 55000 | $67.7B | United States | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2021 | n/a | In 2021, Allstate implemented Microsoft Azure Data Factory to establish a centralized Extract, Transform, and Load (ETL) foundation supporting analytics and reporting across finance, operations, and marketing. Microsoft Azure Data Factory was used to orchestrate data pipelines and schedule recurring data processing workflows, with adoption by analytics teams beginning in July 2021 and continuing into ongoing operations. Implementation focused on pipeline design and orchestration, using Azure Data Factory pipelines and data flows to perform ingestion, transformation, and scheduling. Configuration included automated data quality checks and validation scripts developed in Python, automated report generation using R Markdown and knitr, and repeatable resource allocation workflows informed by Power Query and Power Pivots. The deployment integrated data models in Azure Blob Storage and analytics delivery into Synapse Analytics, while operational data sources included MySQL databases and on premises Hadoop clusters that were maintained for scalable processing. Downstream analytics and visualization integrations included Tableau and QlikView Publisher, and the environment supported closed loop reporting that tied in Google Analytics and Salesforce data for marketing analysis. Jenkins was used for job monitoring and orchestration of CI pipelines, and Jira workflows were customized to align data operations and change control. Governance centered on automated validation, scripted quality checks and workflow controls implemented through customized Jira processes, providing a structured change management approach for ETL workflows. The configuration supported accuracy in financial and operational reporting and enabled improved marketing strategy through integrated closed loop reporting, as explicitly observed in Allstate analytics practice. | |
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Healthcare | 60500 | $163.1B | United States | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2020 | n/a |
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Insurance | 31000 | $13.0B | United States | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2018 | n/a |
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Professional Services | 300 | $50M | United States | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2019 | n/a |
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Utilities | 13008 | $8.1B | Brazil | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2019 | n/a |
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Banking and Financial Services | 8000 | $1.0B | Brazil | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2021 | n/a |
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Consumer Packaged Goods | 400 | $50M | Vietnam | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2024 | Yokogawa Votiva Solutions |
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Professional Services | 2000 | $200M | India | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2016 | n/a |
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Buyer Intent: Companies Evaluating Microsoft Azure Data Factory
- Lakehead University, a Canada based Education organization with 2000 Employees
- Black Sheep Coffee, a United Kingdom based Retail company with 497 Employees
- Landry's, a United States based Leisure and Hospitality organization with 58000 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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