List of Microsoft Azure Data Factory Customers
Microsoft
One Microsoft Way,
Redmond, 98052-6399, WA,
United States
Redmond, 98052-6399, WA,
United States
1 425-882-8080
Professional Services
228000
$320.0B
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 |
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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 | Utilities | 3900 | $9.7B | 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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Centene | Healthcare | 60500 | $163.1B | United States | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2020 | n/a |
In 2020 Centene implemented Microsoft Azure Data Factory to orchestrate enterprise Extract, Transform, and Load (ETL) pipelines. Microsoft Azure Data Factory, an Extract, Transform, and Load (ETL) application, was used to centralize orchestration of data movement and transformation across Azure services and on-premise big data processing components.
The implementation centered on pipeline orchestration, programmatic Spark job execution, and data validation workflows. Engineers developed Spark applications using PySpark and Spark SQL for extraction, transformation and aggregation, and used Scala functions and reusable data structures to improve code modularity. Azure Data Factory pipelines coordinated ETL jobs that invoked Azure Databricks processing, T-SQL and U-SQL tasks, and automated data frame based validation and analytics.
Integrations were explicit and heterogeneous, the Microsoft Azure Data Factory deployment integrated with Azure Databricks, Hive on Cloudera, Spark, Kafka ingestion streams, Sqoop based Data Fabric jobs, and upstream relational sources including Oracle, MySQL and PostgreSQL. CI processes were linked to a GitHub repository and Jenkins based build and deployment pipelines, with configured Jenkins agents for distributed builds. Azure infrastructure components provisioned during rollout included Azure Virtual Networks, Azure Application Gateway and Azure Storage.
Governance and operational workflows were restructured to support continuous integration and deployment and to improve code quality. Unit testing of Spark applications, code reuse practices, and Jenkins driven automated deployments were institutionalized, and training sessions were conducted to raise team proficiency in deployment and pipeline management. Data engineering and analytics functions were the primary operational scope for the deployed Microsoft Azure Data Factory solution.
Outcomes reported in the engagement included automated build and deployment pipelines that reduced deployment time by 50 percent, ongoing data validation using Spark DataFrame operations, and optimization and management of PostgreSQL databases for downstream processing. The Microsoft Azure Data Factory implementation established a centralized ETL orchestration layer for Centene that coordinated big data processing, continuous deployment, and multi source ingestion.
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Chubb USA | Insurance | 31000 | $13.0B | United States | Microsoft | Microsoft Azure Data Factory | Extract, Transform, and Load (ETL) | 2018 | n/a |
In 2018, Chubb USA implemented Microsoft Azure Data Factory for Extract, Transform, and Load (ETL) workloads to centralize data ingestion and transformation for analytics. The deployment was executed by Big Data engineering resources based in Whitehouse Station, NJ, and targeted analytics and data science business functions across the insurance data estate.
Microsoft Azure Data Factory was configured to orchestrate sourcing, profiling, cleansing, and transformation pipelines that produce master data sets for downstream reporting and modeling. Functional capabilities emphasized data wrangling and aggregation, conversion of raw feeds into analysis-ready datasets, complex data parsing and natural language processing transforms, and support for training and deploying statistical and machine learning models. Azure Databricks served as the principal environment for hands-on data transformation and massaging of large datasets.
The implementation integrated Microsoft Azure Data Factory with Azure Data Lake store and Azure Data Lake, Azure Databricks, Azure Cosmos DB and an Azure-based SQL Data Warehouse, plus relational targets including Azure MySQL and PostgreSQL. Automation and orchestration surfaces included Azure RestAPIs and a bespoke Python Django UI used in proof of concept work, while CI/CD pipelines were implemented with GitHub and coordinated with Azure-ops. Operational tooling also included SSMS client, Git version control, and Azure Kubernetes for containerized workloads.
Governance and delivery practices included regular code and design reviews, collaboration between senior and lead data engineers to develop scraping APIs for external sources, and a focused proof of concept with an external geocoding vendor to accelerate on-premises processes. Security and operational controls incorporated Azure identity Management AIM and CI/CD governance, supporting reproducible deployments of pipelines and model artifacts within the Extract, Transform, and Load (ETL) platform.
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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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Showing 1 to 10 of 21 entries
Buyer Intent: Companies Evaluating Microsoft Azure Data Factory
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Microsoft Azure Data Factory. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Microsoft Azure Data Factory for Extract, Transform, and Load (ETL) include:
- 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
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD Microsoft Azure Data Factory Coverage
Microsoft Azure Data Factory is a Extract, Transform, and Load (ETL) solution from Microsoft.
Companies worldwide use Microsoft Azure Data Factory, from small firms to large enterprises across 21+ industries.
Organizations such as Centene, Allstate, Ferguson Enterprises, Tenet Healthcare and Chubb USA are recorded users of
Microsoft Azure Data Factory for Extract, Transform, and Load (ETL).
Companies using Microsoft Azure Data Factory are most concentrated in Healthcare, Insurance and Distribution, with adoption spanning over 21 industries.
Companies using Microsoft Azure Data Factory are most concentrated in United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Microsoft Azure Data Factory across Americas, EMEA, and APAC.
Companies using Microsoft Azure Data Factory range from small businesses with 0-100 employees - 4.76%, to mid-sized firms with 101-1,000 employees - 19.05%, large organizations with 1,001-10,000 employees - 42.86%, and global enterprises with 10,000+ employees - 33.33%.
Customers of Microsoft Azure Data Factory include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Microsoft Azure Data Factory customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Extract, Transform, and Load (ETL).