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Exasol

Exasol is a headquartered in , , specializing in high-performance analytics databases for data warehousing, , and / applications. Exasol is publicly traded on the (EXL.DE) and launched a fully managed offering in November 2025. Founded in 2000, the company entered the commercial market in 2008 and gained prominence that year by topping the TPC-H benchmark for decision support systems, establishing its reputation for speed in analytic query processing. Its flagship product, the Exasol Analytics Engine (also known as Exasol DB), is an in-memory, column-oriented, massively parallel processing () relational database management system (RDBMS) designed to handle billions of rows of data with sub-second query times, enabling rapid insights for enterprises. The Exasol Analytics Engine operates on clusters of standard hardware servers, utilizing to accelerate up to 1000 times faster than traditional systems while supporting flexible deployments including on-premises, (such as AWS), , and models. Key features include automatic tuning for optimal performance, transparent pricing that can reduce costs by up to 65%, and seamless integration with tools like Tableau, as well as support for governed AI/ML workflows to process complex models efficiently. It excels in use cases such as fraud detection, customer personalization, , and real-time KPIs, serving global organizations across industries including , healthcare, and . Exasol's emphasizes without re-platforming, enterprise-grade reliability with high uptime, and a edition for free testing, alongside enterprise subscriptions. The company, led by CEO Joerg Tewes and CTO Mathias Golombek, continues to innovate through customer collaborations and events like its Product Innovation Summit, positioning itself as a leader in the evolving landscape toward AI-driven .

History

Founding and Early Years

Exasol was founded in 2000 in , , by Falko Mattasch, who sought to commercialize a research project from the aimed at overcoming the performance limitations of existing analytic databases, particularly in handling large-scale data queries efficiently. The company's initial product development focused on building a high-performance, in-memory analytic database designed to process complex analytical queries significantly faster than traditional disk-based systems available at the time, addressing the growing need for advanced in the early 2000s. Exasol operated on a bootstrapped during its early years, relying on self-funding without external , which allowed persistence amid market skepticism toward specialized analytic tools when general-purpose databases dominated and concepts were nascent. This approach sustained operations through slow initial adoption, with the product not entering the commercial market until 2008. As part of its evolution toward broader market presence, Exasol took initial steps toward becoming a by incorporating as an (AG) in 2006, registering under HRB 23037 at the Local Court, which laid the groundwork for future stock market listing.

Key Milestones and Expansion

In 2008, Exasol gained early recognition for database performance through internal benchmarks claiming superiority in TPC-H scales from 100 GB to 100 TB; official TPC-H submissions began in 2011, where it achieved top positions demonstrating the scalability and speed of its in-memory analytic engine. By 2011, the company achieved further recognition with a new performance record in the TPC-H benchmark for its in-memory database, accompanied by initial appliance hardware options that enhanced deployment flexibility for enterprise analytics. Exasol went public in 2020, listing on the under the ticker FWB: EXL, with shares initially priced at €9.50 and rising to €14 on the first . As of November 2025, the company's stood at approximately €67 million. In the late , Exasol pivoted toward cloud offerings, launching its enterprise on in 2019 to support scalable, managed analytics deployments. This shift extended into and integration by 2023, with the introduction of features like querying in Exasol and a strategic emphasis on sovereign to ensure data privacy and in regulated environments. Exasol held its Product Innovation Summit on November 18, 2025, a focused on advancements in AI-driven and the Exasol Release 2025.2.

Products and Services

Exasol Analytics Engine

The Exasol Analytics Engine is a high-performance, column-oriented, in-memory management system (RDBMS) designed for (OLAP) and advanced workloads. It stores in columns to optimize query and , while keeping datasets in RAM to enable sub-second response times on large-scale operations. This architecture supports near real-time processing of complex queries, making it suitable for data-intensive environments where speed and scalability are critical. At its core, the engine provides robust SQL support compliant with ISO/IEC 9075:2023, facilitating seamless integration with tools through standard interfaces including ODBC, JDBC, and . It also features in-database scripting capabilities, allowing users to develop and execute custom functions using for general-purpose scripting, for object-oriented applications, for data science workflows, and for statistical analysis—all processed directly within the database to minimize data movement. These functionalities enable efficient handling of analytics pipelines, from data transformation to advanced computations, without requiring external processing engines. As of the Exasol 8 release in 2025, the engine includes built-in capabilities such as in-database language models and SQL-native integration, GPU support for accelerated computations, a new administrative UI, and connectors for platforms like and . Licensing for the Exasol Analytics Engine is structured around allocated , priced per , which aligns costs with usage and needs. A free Community Edition is available for testing and small-scale deployments, preconfigured with full core features but limited in capacity, while editions offer unlimited scaling, advanced support, and additional compliance options for production environments. This model promotes predictable pricing and flexibility for organizations of varying sizes. The engine excels in key use cases, including deployment as a standalone for , reporting, and ad-hoc querying, where it delivers up to 20x faster processing compared to traditional systems. It also accelerates analytics in existing ecosystems by integrating with tools like Tableau or Power BI to reduce query times from minutes to seconds. For and , it supports governed in-database model training and inference using Python, R, or Java scripts, ensuring data security and performance. Additionally, through Lakehouse Turbo, it integrates with data lakes to turbocharge lakehouse architectures, enabling unified analytics on unstructured and structured data without ingestion overhead.

Deployment Options

Exasol offers flexible deployment options to accommodate diverse infrastructure needs, including on-premises installations, cloud-based services, and configurations, ensuring and control over environments. For on-premises deployments, Exasol 8 is installed as a software package on user-chosen distributions running on commodity hardware or virtual machines, with software decoupled from the operating for greater flexibility. It includes the database, administration tools, and services for and automatic to minimize during node failures. Integrated storage is achieved through a distributed that replicates across s using redundancy levels to protect against disk or node loss, enabling seamless without data interruption. In cloud environments, Exasol supports native deployments on AWS via the Cloud Deployment Wizard, which simplifies cluster configuration and provisioning of scalable instances. For and , installation as a application on instances is supported; as of November 2025, Exasol is also available in the for streamlined VM deployment. Additionally, Exasol delivers a fully managed service with pay-as-you-go pricing, automatic updates, and quick setup, allowing users to focus on without management. options combine on-premises and cloud elements for optimized flow and across sensitive and scalable workloads. Exasol's massively parallel processing (MPP) architecture enables horizontal scaling by distributing data and queries across multiple nodes or clusters in a shared-nothing model, supporting expansion without downtime through automated node addition and load balancing. This design allows clusters to grow dynamically to handle increasing data volumes and query demands. Deployment choices contribute to cost efficiency, with options achieving up to 65% reductions in total costs compared to traditional setups, driven by optimized resource utilization, transparent pricing models, and elimination of on-premises hardware overhead.

Technology and Features

Database Architecture

Exasol employs a () architecture based on a shared-nothing design, where is distributed across multiple nodes in a , each equipped with its own CPUs and for operation. This setup enables parallel query execution, as incoming queries are received by any connected node, optimized, and then distributed via a to all nodes for local of data partitions, with partial results aggregated before returning to the user. The database utilizes an in-memory columnar format, which compresses natively and loads relevant columns into RAM to minimize I/O operations and accelerate analytical workloads by processing only necessary data segments. Data management in Exasol is handled by EXAStorage, a distributed that organizes information into volumes across local or remote disks, ensuring through when exceeds one. EXACluster OS, a derivative tailored for the environment, orchestrates operations by monitoring nodes, facilitating inter-node communication over a , and supporting public network access for users and . Automatic failover is integrated into EXAStorage, allowing reserve nodes to seamlessly replace failed ones while maintaining availability without interruption. Exasol incorporates self-tuning optimization layers that operate without manual intervention, including automatic statistics gathering after each data manipulation language (DML) statement to inform the cost-based query optimizer about table sizes, row counts, and distributions. The system dynamically creates indexes for join operations during query execution, maintains them following relevant DML changes, and discards unused ones after five weeks to balance performance and resource use. Query rewriting is performed by the optimizer on the compiled query graph, enabling transformations such as pushdown optimizations for virtual schemas to enhance efficiency. For AI and machine learning workflows, Exasol provides native support for vectorized processing through its columnar in-memory structure, which facilitates efficient handling of vectorized queries and in-database model execution, as demonstrated in benchmarks for AI/ML tasks. As of 2025, enhancements in Exasol 8 include built-in AI capabilities like in-database language models and SQL-native AI integration, further supporting governed AI/ML workflows. This integration allows seamless preparation and analysis of data within the database, reducing the need for data movement.

Performance and Optimization

Exasol's engine achieves significant speed advantages through its in-memory columnar storage and vectorized query execution, enabling up to 1000 times faster query performance compared to traditional disk-based databases for complex analytical workloads. This acceleration stems from processing data entirely in , which eliminates I/O bottlenecks, combined with vectorized operations that execute computations on batches of data simultaneously rather than row-by-row, optimizing CPU utilization for high-throughput . In evaluations, Exasol has demonstrated leadership in the TPC-H for decision systems since its first submission in 2008, holding top positions at scales such as 1 TB (6,145,628 QphH@Size as of 2019) and 10 TB (22,756,594 QphH@Size as of ), though surpassed at 100 TB in 2024 by Explorer (54,803,403 QphH@Size). For instance, in , Exasol set records including 22,756,594 QphH@Size at the 10 TB scale and 22,297,225 QphH@Size at the 100 TB scale using its version 7.1 on hardware, outperforming competitors in both raw speed and price-performance at those scales. These results underscore Exasol's capabilities in handling large-scale analytic queries, with earlier milestones like a 100 TB in-memory in 2014 further establishing its . Exasol incorporates automatic optimization mechanisms to maintain efficiency without manual intervention, including self-tuning indexing that dynamically creates and manages indexes based on query patterns, advanced data compression achieving typical ratios of around 2.5x to 3x, and cost-based query optimization using automatically gathered statistics on table attributes like row counts and sizes. is handled through a fully automatic manager that distributes CPU, memory, and I/O across active queries, prioritizing critical workloads via configurable consumer groups to minimize latency and maximize system throughput. These features, enabled by the underlying processing architecture, ensure consistent performance even under varying loads. For reliability, Exasol delivers enterprise-grade through built-in at the storage and node levels, with configurable options that replicate data across multiple nodes to prevent . The shared-nothing design eliminates single points of failure by distributing and segments with mirrors on neighboring nodes, enabling automatic and recovery for node outages, supported by dual configurations for .

Company Overview

Leadership and Operations

Exasol AG is headquartered in , , at Neumeyerstraße 22–26, with its primary operations centered in and additional presence in the United States, including an office in , California. The company maintains a global footprint to support its international customer base, focusing on strategic regions for sales, support, and development activities. As of December 2024, Exasol employed 176 people, reflecting a structure dedicated to and . The executive leadership team guides Exasol's strategic direction and operational efficiency. Joerg Tewes serves as , overseeing overall strategy and growth initiatives since January 2023. Mathias Golombek, as , drives technology and product development. Jan-Dirk Henrich holds the roles of and Chief Operations Officer, managing finance, operations, and executive board responsibilities. Alexander Stigsen is , focusing on product roadmap and enhancements. Recent appointments include Henrik Jorgensen as to accelerate market expansion and Lars Milde as to strengthen brand positioning. Exasol's organizational focus emphasizes research and development for integrating into solutions, alongside comprehensive customer support to guide users through their journeys. This approach supports the company's mission to deliver high-performance while fostering innovation in AI-driven insights. In November 2025, Exasol reported strong annual recurring revenue (ARR) growth in focus industries for the first nine months of the year.

Customers and Market Impact

Exasol has been adopted by numerous enterprises worldwide, with hundreds of installations across various sectors as of 2025. Notable customers include giant , which utilizes Exasol for advanced analytics to improve customer experiences and operational efficiency; healthcare provider Healthcare, which processes large datasets to enhance patient care and runs over 40,000 queries per hour; Swiss health insurer Helsana, serving over 2 million customers and leveraging Exasol for real-time insurance process optimization with data load times reduced from 123 to 20 hours; and Digital Planet, which employs the platform for to boost customer satisfaction through faster data management. Other prominent users include , , , Olympus, and , demonstrating Exasol's appeal to data-driven organizations seeking rapid insights. The company primarily serves data-intensive industries such as banking and insurance, hedge funds, retail and e-commerce, and healthcare and pharmaceuticals, where high-speed analytics are essential for , fraud prevention, , and patient outcomes. In these sectors, Exasol enables access and scalable processing, supporting enterprises in handling growing data volumes without performance bottlenecks. For instance, in healthcare, it facilitates compliant for better resource allocation, while in finance, it accelerates and through in-memory architecture. Exasol holds a strong market position in analytics databases, earning a 4.8 out of 5 rating on Peer Insights based on 48 verified reviews as of 2025, with users highlighting its exceptional productivity through fast query performance on large datasets and stability for mission-critical operations. Reviewers also praise significant cost-savings, including low and rapid , often achieving within eight months via reduced maintenance and licensing fees. This recognition underscores Exasol's role in enabling efficient, high-performance without the overhead of traditional systems. Exasol's broader market impact lies in its contributions to cost-efficient and the acceleration of and adoption in enterprises, particularly through in-database integration that allows governed model execution directly on . By embedding models and SQL-native capabilities, it supports and real-time insights, helping organizations in regulated industries like healthcare and derive deeper value from their without extensive data movement. This has driven transformative outcomes, such as Helsana's 65% reduction in license and maintenance costs, fostering wider enterprise /ML deployment by 2025.

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