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An Analytics Database Company to Watch Vendor Profile Exasol: An Analyt ics Dat abase Company t o Wat ch Philip Carnelley Erica Spinoni David Wells IDC OPINION Exasol is a Germany-headquartered analytics database management software company founded in 2000 in Nuremberg, with offices across Europe and the U.S. and recently growing rapidly on both sides of the Atlantic. With 20 years of experience in distributed database technology and a significant customer base, Exasol is becoming an influential player in the in-memory analytics database market. IN THIS VENDOR PROFILE This IDC Vendor Profile analyzes the success and the market positioning of Exasol, a Germany- headquartered analytics database management software company. From its foundation, Exasol planned to build database management software designed to take advantage of the increasing affordability of both parallel processing hardware platforms and RAM. SITUATION OVERVIEW Company Overview Exasol is an analytics database management software company founded 20 years ago in Nuremberg, Germany. In recent years, it has transformed from a small start-up to a company with an international footprint, principally in Europe and the U.S. In the nine months to November 2019, Exasol's recurring revenue grew over 45%, and IDC estimates it exceeded $23 million in 2019, with around a fifth of that coming from the U.S. Exasol's customer base consists mainly of large commercial enterprises with complex analytical needs from industries such as retail, financial services, media and publishing, and ecommerce — its entire range of customers is quite broad. The company has offices in both Europe and the U.S., providing support for its 165+ customers and over 500 installations. Launched in 2008, Exasol's in-memory analytics data platform aims to enhance analytics productivity and performance, with features such as automated indexing and optimization. Part of the Exasol vision is to provide functionality for all the key analytics stakeholders — business intelligence (BI) analysts, data scientists, and business users — to maximize the value of its platform. Exasol customers operate in several industries (details to follow). For example, retailers partnered with Exasol to improve customer experience and increase customer spend, involving complex, high data volume, and sometimes ad hoc queries along the whole supply chain to gauge changes in buying patterns in near real time to tune online marketing and for supply chain optimization. Fintech and financial organizations collaborated with Exasol for use cases such as analyzing customer loyalty and customer experience with the aim of increasing sales opportunities (cross- selling and upselling) and addressing customer churn and attrition. April 2020, IDC #EUR146164120 Product Overview As shown in Figure 1, Exasol can slot in alongside, or instead of, other databases in an analytics architecture as an analytics one-stop shop (replacing the enterprise data warehouse) or as an analytics performance accelerator or sidecar. Exasol was developed as a parallel system based on a shared-nothing architecture. It is designed to operate without any dedicated "master node" that would constitute a single point of failure and a performance bottleneck. To ensure the cluster hardware is leveraged optimally, Exasol distributes data evenly and across servers. FIGURE 1 Exasol's Context Diagram Source: Exasol The product was designed from the outset to run on distributed massively parallel processing (MPP) hardware as shown in Figure 2 with sufficient RAM, with most query processing taking place in-memory, even for high-volume analytics workloads. The main selling point of the product is query performance, and in 2019, the company achieved the best result for the decision-support benchmark TPC-H for both 3TB and 10TB scale factors (as documented on the TPC website). Exasol uses a variety of techniques to achieve its performance levels. To maximize query performance, Exasol uses an intelligent caching algorithm that only loads blocks containing the data it needs and keeps hot data in memory while cold data resides on the disk, as shown in Figure 2. This means around 10% of raw data size is needed as memory (considerably lower than other in-memory databases). Exasol's data compression algorithm is optimized to provide the best compromise between data size and processing speed. Furthermore, internal indices are automatically created and maintained to guarantee fast query performance out of the box. ©2020 IDC #EUR146164120 2 FIGURE 2 Exasol Performance Architecture Source: Exasol Compatibility with SQL-related standards (including JDBC, ODBC, and ADO.NET) and with leading visualization and extract, transform, and load (ETL)/ extract, load, transform (ELT) integration tools as well as support for R, Python, Java, and Lua means Exasol does not need to impose proprietary lock-in on its users despite its novel performance-oriented architectural features. The product — which runs on industry-standard hardware — also offers integration with HDFS (Hadoop), enabling existing Big Data stores to be queried along with data that resides in Exasol's database ; the associated data virtualization offering (Virtual Schema Framework) is maintained as an open source project on GitHub. Exasol includes autonomous self-tuning functionality intended to reduce manual database administration and total cost of ownership (TCO). Company Strategy Many organizations are facing the challenges of digital transformation (DX); IDC's CxO Survey, 2019, found that 80% of European CEOs are under pressure to execute a successful digital transformation. From a data perspective, this challenges organizations to manage increasing data volume, variety, and velocity while improving the range, quality, speed, and timeliness of analytics. It is crucial to have a state-of-the-art environment to respond to challenges, and IDC believes this should be an integrated technology platform that links companies with their suppliers and customers and inherently enables intelligence to be built into business processes. Exasol has been self-funded (and profitable) for much of the first 20 years of its life. IDC believes the company should consider seeking external funding to accelerate its growth — particularly in the huge U.S. market — leveraging the position it has achieved to date and building momentum while the opportunity is hot. ©2020 IDC #EUR146164120 3 Pricing Strategy Exasol provides a free trial version, available for up to 200Gb and licensed for commercial and private/academic use. Exasol One — the standard commercial offering — enables users to scale up to 1TB on a single node and provides standard support. In addition, Exasol offers an Enterprise Cluster package that provides a higher level of scalability and enterprise-level support with different support plans. Flexibility in Cloud Deployment IDC predicts that by 2022, 60% of European enterprises will integrate cloud management — across their public and private clouds — by deploying unified hybrid/multicloud technologies, tools, and processes. As users move in this direction, technology vendors must update their strategies and follow these new trends. Exasol partners with the three major cloud platforms — AWS, Azure, and GCP — to provide customers with options when using analytics databases in cloud environments. It also offers a fully managed private cloud — ExaCloud — to offer data location security and compliance with data regulation. Exasol can be deployed in on-premises, hybrid, and cloud-only environments. Partner Strategy Exasol has established two levels of partnership for integration/application services. Elite partners are chosen based on their capability to position and deploy Exasol-based solutions, focusing on developing an analytical data platform for clients. Exasol works with these partners on joint marketing (for instance, lead generation through meetups and webinars). Authorized partners largely focus on consulting rather than implementation, and they are tasked to work on training and education toward any aspect of the joint analytics platforms where Exasol plays a relevant role. Exasol stresses that its partners are not restricted to one business model but have flexibility on a per-project basis to either refer Exasol, jointly sell with the Exasol sales team, or resell independently. Furthermore, Exasol considers technology alliances as critical in supporting its best-of-breed technology stack. Technology alliance partners include Amazon AWS, Alteryx, Collibra, Google GCP, Microsoft (Azure), MicroStrategy, Protegrity, and Tableau. Technical Features A key feature of Exasol's positioning is to offer what it terms "unrivalled performance" for analytic workloads. This is based on a number of technology features, built on the assumption that the database will usually be deployed in a massively parallel processing environment in which much or all of data being analyzed will be in-memory. Exasol's core algorithms are based on the assumption that data access will usually be very fast. A number of techniques are used to increase the likelihood of the in-memory assumption being realized in practice, including: . Automatic hot data management in which frequently and recently accessed data is transparently kept in RAM . Predicting which data will need to be in fast storage for processing and pre-fetching into all storage tiers, including pre-fetching data from remote machines into temporary local buffers . Using a range of compression techniques to fit the maximum amount of data into fast- access
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