List of Matillion Customers
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Since 2010, our global team of researchers has been studying Matillion 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 Matillion 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 Matillion for Extract, Transform, and Load (ETL) include: Aramex, a United Arab Emirates based Transportation organisation with 18000 employees and revenues of $1.55 billion, Slack, a United States based Communications organisation with 2700 employees and revenues of $903.0 million, Tui Ambassador Tours, a Portugal based Professional Services organisation with 53 employees and revenues of $18.0 million and many others.
Contact us if you need a completed and verified list of companies using Matillion, 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 Matillion 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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Aramex | Transportation | 18000 | $1.6B | United Arab Emirates | Matillion | Matillion | Extract, Transform, and Load (ETL) | 2020 | n/a | In 2020, Aramex deployed Matillion ETL for Amazon Redshift to prepare large scale operational data for machine learning and analytics across its logistics operations in the Middle East. The implementation used Matillion as the primary Extract, Transform, and Load (ETL) platform to centralize data preparation for analytics and ML workflows across operations, warehousing, and customer service functions. Matillion ETL was configured to run repeatable data preparation pipelines, including ingestion, transformation, and feature engineering workflows that support model training and analytics consumption. The implementation emphasized orchestration and scheduling of transformation jobs, and standardized pipeline templates to accelerate deployment of new data flows. The deployment integrated Matillion with Amazon Redshift as the enterprise analytics store, ingesting more than 250,000 records every 15 minutes to feed downstream ML and BI processes. These pipelines supported a rapid cadence of model delivery, enabling the rollout of roughly 200 ML models within a year and operationalizing insights for routing, delivery prediction, and customer engagement. Operational governance was introduced to manage pipeline lifecycle and data quality, using automated validation steps and standardized deployment practices to reduce drift between development and production. The rollout covered core logistics sites and regional analytics teams in the Middle East, aligning data owners, analytics engineers, and ML practitioners on pipeline ownership and change controls. Outcomes reported from the Matillion implementation include the ability to sustain high frequency ingestion and model delivery, and a reduction in inbound call center volume by over 40 percent through improved analytics driven customer workflows. Matillion remains the central Extract, Transform, and Load (ETL) application for Aramex’s analytics and machine learning infrastructure. | |
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Slack | Communications | 2700 | $903M | United States | Matillion | Matillion | Extract, Transform, and Load (ETL) | 2020 | n/a | In 2020, Slack implemented Matillion ETL for Snowflake to modernize its data warehouse and accelerate cross functional reporting for Sales, Marketing, Finance and Recruiting in the United States. The Matillion deployment, categorized as Extract, Transform, and Load (ETL), served as the primary orchestration layer feeding Snowflake and centralizing metric generation across business functions. The implementation standardized ETL workflows using Matillion job orchestration, reusable transformation components, and scheduled pipelines to replace bespoke extraction scripts. Matillion ETL for Snowflake reduced reliance on custom ETL code and consolidated configuration and transformation logic within the Matillion environment, enabling a compact operations footprint. Operational coverage focused on Sales, Marketing, Finance and Recruiting teams in the United States, with a small central data engineering team managing the stack. Governance shifted toward centralized pipeline ownership and repeatable deployment patterns, and the project delivered materially faster metric generation, reducing some revenue metric build times from six hours to about 30 minutes and allowing the small team to sustain the environment. | |
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Tui Ambassador Tours | Professional Services | 53 | $18M | Portugal | Matillion | Matillion | Extract, Transform, and Load (ETL) | 2022 | n/a | In 2022, Tui Ambassador Tours implemented Matillion to centralize analytics pipelines, using the Extract, Transform, and Load (ETL) category to consolidate hotel, booking, and third-party data for Hotels & Resorts analytics across multiple countries in Europe. Matillion was deployed as the primary ETL engine alongside Snowflake, establishing a cloud data pipeline that enabled spatial enrichment and supported capacity planning use cases for the Hotels and Resorts business function. The implementation architecture paired Matillion with Snowflake as the data warehouse, with Matillion orchestrating extraction from hotel feeds, booking systems, and external third-party sources, performing transformation and spatial enrichment, and loading consolidated datasets into Snowflake. Configuration emphasized lightweight orchestration and parallel processing, which produced visible results quickly, with first jobs running in test within hours of initial setup. Operational coverage focused on European Hotels and Resorts analytics, ingesting and processing hundreds of millions of rows daily to feed analytics and capacity planning workflows. Governance and runbook practices were established for job scheduling, monitoring, and cost control, with Matillion pipelines parameterized for incremental runs and environment promotion from test to production. The deployment delivered rapid runtime improvements and cost reductions, with daily pipeline runtimes reduced to minutes and substantially lower operational costs as reported, while spatial enrichment enabled more precise capacity planning across Tui Ambassador Tours sites. Matillion remains the central ETL engine for ongoing Hotels and Resorts analytics in this environment. |
Buyer Intent: Companies Evaluating Matillion
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