Exasol is a technology provider with headquarters in Nuremberg, Germany. Founded in 2000, the company operates mainly in Europe and the United States although the majority of its customers still come from German-speaking countries. However, approximately 15 percent of total sales are generated in the United States, with a strong upward trend. Exasol is leveraged and has been operating profitably for five years. The company went public in May 2020. Organizations of various sizes and industries use Exasol, which has a broad network of implementation and technology partners. In addition to its partners, Exasol works closely with its community, which develops the integration capabilities of the database based on a free open source version of the Exasol database on GitHub. Developments from the community are moderated and reviewed by a dedicated dev team.
Exasol is a high-performance analytics database designed for high-performance analytics (reporting, advanced analytics & ML, real-time KPIs & dashboards). It can be deployed as a data warehouse, data mart or as a BI acceleration layer. From a technical perspective, it is a relational, column-based, MPP-enabled, in-memory database – an analytical database on Exasol’s own infrastructure. Exasol sees itself as the highest-performing database for analytical workloads and proves this with TPC-H benchmarks. Some customers leverage Exasol as an accelerator for BI or analytics, but a growing number have modernized their central legacy data warehouse by switching completely to Exasol. Using Exasol as a logical data platform with a central access layer is the next step, and this also offers the capability to provide BI users with access to data science models in any language through Exasol’s data science integration.
Version 8 introduces a fundamentally renewed architecture. The separation of storage and compute combined with the introduction of multi-cluster support enables completely new operating modes. This architecture is also the basis of Exasol’s SaaS offering. Nowadays, Exasol can be deployed on-premises, with any of the major vendors in the public cloud, in a private Exasol cloud or in a hybrid approach. Its SaaS offering is available on AWS having been developed as part of the AWS SaaS Factory. With this new availability as SaaS, Exasol has also made the transition to a cloud-first company. This means that new functionalities are always made available on SaaS first and later released in the other versions (e.g., the self-hosted/PaaS versions of Exasol for public clouds or on-premises).
The database itself is characterized by its high query performance, self-optimization algorithms and a plug-in concept that simplifies the integration and functional expansion of the database.
Self-optimization has been on the feature list of the database since day one and is constantly being developed. To ensure high performance for current workloads, algorithms monitor database usage as well as keeping the organization of data optimized. This automation is a core function and value-add, and it also means that few manual configuration options are available for the database.
Exasol’s plug-in concept enables easy functional extensions of the platform. It mainly includes the virtual connection of new data sources via Virtual Schemas and a framework for the development of in-database analytics programs via R, Python, Java or Lua, that can be executed in parallel.
A good deal of investment has been put into Virtual Schemas. Exasol has developed additional connectors – in particular, for AWS, Microsoft Azure and Google GCP – and expanded support for cloud data formats. Plenty of connectors are available as open source projects in Exasol’s GitHub repository, so customers can also easily adapt connectors and also share changes and improvements with the Exasol community.
In this document, we evaluate version 7 of the Exasol database. Unfortunately, the version number of the database in this survey does not indicate the database deployment type. We assume that most customers are using a cloud implementation. However, in version 8 (the current version), several further integrations have been added, in particular the integration with the major public cloud platforms to make Exasol a first class citizen on these platforms. Some performance improvements have also been introduced.
User & Use Cases
97 percent of survey respondents use Exasol for data warehousing and BI, while only 24 percent use it for advanced analytics. This is a clear statement about the preferred use case of Exasol customers. It is also interesting to note that only 42 percent use it for self-service analytics. With the availability of analytical functions as a service paired with the consumption-based licensing model, Exasol could get closer to data users and self-service analytics usage may well strengthen noticeably in the next survey. 64 percent use the database for data preparation and a further 61 percent for data integration. Exasol itself does not offer any functions for augmented data integration. However, the database technology is used to provide integrated views on data, and data can be loaded into the database in parallel, even though only 21 percent of respondents reported making use of data virtualization capabilities.
42 percent of users operate Exasol in a company-wide deployment, with another 42 percent deploying it in several divisions. This indicates that Exasol is mainly used in more complex scenarios. 55 percent of customers are companies with more than 2,500 employees. A mean of 636 and a median of 100 users also shows that Exasol is quite scalable in terms of user numbers.
Use cases
n=33
Extend of usage in the company
n=33
Total number of users per company
n=33
Total number of developers per company
n=33
Company size (number of employees)
n=33
Summary of Exasol Database highlights
User ratings of the Exasol database are well above average in all KPIs except Product Enhancement this year. ‘Convincing performance’ and ‘scalability’ are prominent technical reasons why customers buy the product. ‘Fast reaction time on support tickets’ and ‘good price performance ratio’ are also major reasons why Exasol is chosen. A lack of disaster recovery capabilities and integrations with third-party IT tools are the only reported problems, but are only cited by a minority of users. All in all, Exasol is a really strong performer in this year’s Data Management Survey, achieving 6 top ranks and 11 leading positions in each of the Analytical Database Products and Exasol vs. Cloud Data Warehousing Products peer groups.
Automation – Top-ranked
Peer group: Analytical Database Products
Customer Experience – Top-ranked
Peer group: Analytical Database Products
Functionality
Peer group: Analytical Database Products
Price to Value – Leader
Peer group: Analytical Database Products
Technical Capability – Top-ranked
Peer group: Analytical Database Products
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