List of Microsoft Azure Databricks (AI) 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 Databricks (AI) 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 Databricks (AI) for Analytics and BI, Data Warehouse 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 Databricks (AI) for Analytics and BI, Data Warehouse include: Microsoft, a United States based Professional Services organisation with 228000 employees and revenues of $320.00 billion, 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, Tata Motors, a India based Manufacturing organisation with 91496 employees and revenues of $49.62 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 Databricks (AI), 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 Databricks (AI) 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 Databricks (AI) | Analytics and BI,Data Warehouse | 2020 | n/a |
In 2020, Afiniti deployed Microsoft Azure Databricks (AI) in the Analytics and BI,Data Warehouse category to centralize AI driven data processing and analytics for customer experience and data engineering functions. The Microsoft Azure Databricks (AI) implementation was scoped to support Afiniti core AI workloads that pair customers with CSR agents, and to consolidate pipeline orchestration and exploratory analytics across product teams.
The deployment established a Medallion Architecture and emphasized layered data ingestion and transformation, with Databricks serving as the primary unified analytics engine. Functional capabilities implemented included ETL and ELT pipeline orchestration, automated validation routines, dimensional modeling, change data capture workflows, and real time streaming analytics, driven by Airflow orchestrations and data pipeline frameworks such as DBT and ErWin.
Integrations tied Microsoft Azure Databricks (AI) to a heterogeneous data estate, ingesting from MySQL, SQL Server, Greenplum, and PostgreSQL using tools like Talend, Airbyte, and QLIK Replicate, and coordinating with Azure Data Factory and Airflow for cloud movement. The implementation also interfaced with downstream analytics and data portal layers such as Apache Superset and cloud data platforms including Snowflake and AWS Redshift, and implemented Kafka and Spark for streaming data flows and reduced latency.
Governance and operational rollout included standardized dimensional models, CDC governance, capacity planning, and C#.NET API integration patterns to enable interoperability with operational systems. The program included team growth and enablement activities to onboard engineers and support staff, and produced documented outcomes that were tracked, including a 15 percent faster onboarding, automated validation that reduced manual effort by 20 percent and delivered 99.9 percent data integrity, database performance improvements and cost and processing gains as recorded during the migration to Azure cloud.
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AGL Energy | Utilities | 3900 | $9.7B | Australia | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2016 | n/a |
In 2016, AGL Energy, Ltd. implemented Microsoft Azure Databricks (AI) as a core component of an Enterprise Data Platform supporting Analytics and BI,Data Warehouse for cross‑corporate analytics. The deployment was positioned as a centralized Data Lake and Warehouse solution to fulfill analytics needs across business units including customer market, wholesale and group operations.
The implementation of Microsoft Azure Databricks (AI) emphasized raw vault modelling and information mart design to structure curated analytics layers. Data engineering activities included requirement analysis, Raw Vault modelling activities, information mart modelling and database testing, complemented by system and end‑to‑end testing and creation of Azure Data Factory pipelines for scheduled data loading into downstream tables. Development work leveraged Scala and Python within Azure Databricks to prepare and transform source data for downstream marts.
Integrations were explicitly implemented with Azure Data Factory for orchestration and data movement, and with on‑premise and cloud data stores including Microsoft SQL Server, SAP DS and Informatica driven sources, plus SAP device management and market interaction feeds tied to IS‑U billing processes. Azure Databricks was used to fetch data from other sources and databases, consolidating feeds into the Enterprise Data Platform used by multiple departments and sites inside AGL.
Governance and operational processes were implemented through formal requirement analysis and agile scrum workflows, with Product Owners, Business Analysts and Solution Architects collaborating on story definition and acceptance criteria. Testing regimes included system integration and database testing, and delivery tooling and process governance used Agile tools such as JIRA and Confluence to manage sprints, incidents and rollout activities.
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Allstate | Insurance | 55000 | $67.7B | United States | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2024 | n/a |
In 2024 Allstate implemented Microsoft Azure Databricks (AI) to expand its Analytics and BI,Data Warehouse capabilities. The initiative focused on strengthening analytics and data warehousing workloads to support critical business applications through centralized data processing, transformation, and reporting.
The deployment included development and management of automated data ingestion, transformation, and loading pipelines that were instrumented to improve processing efficiency. Microsoft Azure Databricks (AI) was used to host and optimize Apache Spark clusters that processed approximately 500 TB of data, and Azure SQL databases were designed and implemented to support critical application queries with database optimization for better performance.
Streaming capabilities were addressed by implementing Apache Flink applications to handle real time data processing with low latency, positioned alongside Spark streaming capabilities for batch and micro batch workloads. Complex ETL workflows were designed and orchestrated using Apache Airflow to automate pipeline execution, and interactive Power BI reports were built using advanced DAX and Power Query modeling to surface analytics to downstream reporting and decisioning functions.
Governance centered on automated orchestration and pipeline configuration to standardize data operations and reduce manual handoffs, with documented ETL workflows managed in Airflow. Recorded outcomes from the engagement include a 30% improvement in data processing efficiency, a 20% improvement in Azure SQL query performance, processing of 500 TB on Databricks Spark clusters, and Apache Flink delivering approximately 20% faster results compared to Spark Streaming, while Power BI reports enabled deeper analytical access for business users.
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Australian Pacific Airports (Melbourne Airport) | Transportation | 405 | $800M | Australia | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2022 | n/a |
In 2022 Melbourne Airport implemented Microsoft Azure Databricks (AI) as a central component of a cloud data platform to support Analytics and BI,Data Warehouse use cases across the organisation. The implementation was positioned to democratise reporting and unlock operational and customer insights without adding complex on-premises infrastructure.
The platform was architected as a data lakehouse on Microsoft Azure, combining Azure Databricks with Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure SQL and dedicated SQL pools to create a unified ingestion, processing and analytics layer. Microsoft Azure Databricks (AI) was used for scalable data engineering and analytics workloads, enabling interactive notebooks, ETL pipelines and the potential for machine learning model training. The design emphasized self-service reporting and BI capabilities so business teams could generate insights while retaining centralized data management.
Operational coverage included passenger experience, terminal operations, retail precincts and ground transport, with explicit use cases for check-in throughput, queue management at baggage and border security, staffing and vendor planning, and parking activity analysis. The platform ingests real-time and batch data streams and is being prepared to incorporate Internet of Things telemetry for predictive maintenance scenarios, alongside Microsoft AI tooling for future predictive analytics. These capabilities targeted business functions such as operations, safety and security, facilities management, commercial retail and resource allocation.
Governance and rollout were conducted in partnership with Microsofts Data and AI team, starting with a data envisioning workshop and continuing with bi-weekly training sessions and technical enablement to build internal skills. The implementation included measures to democratise access without compromising data security and customer privacy, and plans to expand dataset availability to additional business units as part of staged enablement. Executive sponsorship from the CIO and a named Data Lead supported adoption and operationalisation.
Explicit outcomes reported include significantly enhanced reporting capabilities, improved visibility into operations and customer flows, and more informed decision making for resource allocation and safety monitoring. The airport also described plans to extend the platform to predictive maintenance and sustainability analytics as part of ongoing roadmap work.
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Centene | Healthcare | 60500 | $163.1B | United States | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2020 | n/a |
In 2020, Centene implemented Microsoft Azure Databricks (AI) to centralize analytics and data engineering workloads within its Analytics and BI,Data Warehouse environment. The Microsoft Azure Databricks (AI) deployment was positioned to support data engineering teams analyzing customer usage patterns and to provide a unified processing layer for ingestion and transformation workflows.
The implementation centered on Spark-based ETL and transformation capabilities, with developers building Spark applications using PySpark and Spark SQL, and applying Scala functions and native data structures for code reuse. Data processing patterns included Spark DataFrame operations for validation and analytics on Hive data, U-SQL and T-SQL for structured transformations, and unit testing to ensure pipeline correctness.
Integrations and platform components were implemented across cloud and on-premise tooling, including Azure Data Factory for orchestration, GitHub for source control, and Jenkins for CI CD pipelines and automated deployment. The environment ingested data through Kafka, leveraged Hive on Beeline and a Cloudera stack for Hadoop-era tooling such as Hive, Pig, Zookeeper, Flume, Impala, and Sqoop, and ran alongside relational stores including PostgreSQL, Oracle, and MySQL while using Azure Virtual Networks, Azure Application Gateway, Azure Storage, and affinity group constructs for network and storage configuration.
Operational governance emphasized build and deployment automation, with Jenkins agents configured for distributed builds and team training to raise CI CD proficiency. Data governance and quality controls were enforced through Spark SQL validation, pipeline unit tests, and automated deployments, and the documented CI CD changes achieved a stated 50% reduction in deployment time.
Centene Microsoft Azure Databricks (AI) in the Analytics and BI,Data Warehouse category therefore served as a centralized data engineering platform supporting ETL orchestration, large scale Spark processing, cross functional data ingestion, and CI CD driven release governance for analytics and reporting workflows.
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Insurance | 70 | $30M | United States | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2018 | n/a |
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Insurance | 31000 | $13.0B | United States | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2019 | n/a |
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Professional Services | 300 | $50M | United States | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2019 | n/a |
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Utilities | 13008 | $8.1B | Brazil | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2019 | n/a |
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Consumer Packaged Goods | 400 | $50M | Vietnam | Microsoft | Microsoft Azure Databricks (AI) | Analytics and BI,Data Warehouse | 2024 | Yokogawa Votiva Solutions |
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Showing 1 to 10 of 25 entries
Buyer Intent: Companies Evaluating Microsoft Azure Databricks (AI)
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Microsoft Azure Databricks (AI). Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Microsoft Azure Databricks (AI) for Analytics and BI, Data Warehouse include:
- Modern Aviation, a United States based Transportation organization with 210 Employees
- Owensboro Municipal Utilities Electric Light & Power System, a United States based Utilities company with 235 Employees
- Arcsource, a United States based Distribution organization with 25 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated |
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Modern Aviation | Transportation | 210 | $63M | United States | 2026-03-20 |
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Owensboro Municipal Utilities Electric Light & Power System | Utilities | 235 | $25M | United States | 2026-01-26 |
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Arcsource | Distribution | 25 | $8M | United States | 2025-12-19 |
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Distribution | 28 | $1M | Hong Kong | 2025-12-07 |
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Professional Services | 20 | $4M | United States | 2025-09-23 |
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Insurance | 30 | $4M | Canada | 2025-08-22 |
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Professional Services | 2100 | $450M | United States | 2025-07-23 |
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Manufacturing | 60 | $6M | United Kingdom | 2025-03-19 |
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Professional Services | 10 | $2M | United States | 2025-01-08 |
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Construction and Real Estate | 1000 | $274M | South Korea | 2024-11-21 |
FAQ - APPS RUN THE WORLD Microsoft Azure Databricks (AI) Coverage
Microsoft Azure Databricks (AI) is a Analytics and BI, Data Warehouse solution from Microsoft.
Companies worldwide use Microsoft Azure Databricks (AI), from small firms to large enterprises across 21+ industries.
Organizations such as Microsoft, Centene, Allstate, Tata Motors and Chubb USA are recorded users of
Microsoft Azure Databricks (AI) for Analytics and BI, Data Warehouse.
Companies using Microsoft Azure Databricks (AI) are most concentrated in Professional Services, Healthcare and Insurance, with adoption spanning over 21 industries.
Companies using Microsoft Azure Databricks (AI) are most concentrated in United States and India, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Microsoft Azure Databricks (AI) across Americas, EMEA, and APAC.
Companies using Microsoft Azure Databricks (AI) range from small businesses with 0-100 employees - 8%, to mid-sized firms with 101-1,000 employees - 28%, large organizations with 1,001-10,000 employees - 24%, and global enterprises with 10,000+ employees - 40%.
Customers of Microsoft Azure Databricks (AI) 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 Databricks (AI) customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Analytics and BI, Data Warehouse.