LeanXcale is a spinoff company from the Technical University of Madrid (Universidad Politecnica de Madrid – UPM), developing an ultra-scalable transactional database that is full-SQL with Analytical capabilities (HTAP database). Founded in 2015, LeanXcale is established on deep technical research into distributed systems and data management. The professors leading the Distributed Systems Lab (LSD) at UPM, co-founders of LeanXcale, decided to discard over 15 years’ worth of research and start from scratch to conceive and architect a radically different transactional manager that could scale without limits. Over the course of nine months, the LeanXcale co-founders produced the first version of the algorithm with highly successful results – the delivery of the perfect solution for solving the biggest and most problematic bottleneck in databases for decades. The first generation of the database was produced in 2010 with further iterations produced through to 2015 and the company founding. The company team now has 15 people and is seeking to expand its business development team and hire additional database experts within the project. LeanXcale has been coached by EIT Digital acceleration program and had the offices at EIT Digital Madrid node from incorporation in 2015 to late 2016. Currently, the offices are at the startup greenhouse at UPM technological campus. The CEO of LeanXcale has a 20-year experience in European projects having participated in 10 of them, 5 of them as project coordinator (Stream, Adapt) or technical coordinator (CumuloNimbo, CoherentPaaS, LeanBigData, INFINITECH), and with relevant position in several others such as Scientific Director in NEXOF-RA and chief-editor of the research agenda in NESSI-Grid. LeanXcale team has also a 10-year experience in participating in EU projects including the aforementioned ones and others such as GORDA, BigDataStack, CYBELE, ClouDBAppliance, CrowdHEALTH, PolicyCLOUD, FAME, PHASE IV AI, iHelp, HumAIne, XR5.0 etc.
LeanXcale will lead all related the big data management activities in order to provide real-time big data analytics, exploiting its database. The latter supports hybrid transactional and analytical workloads, enabling support for data ingestion in very high hates, and allows for analytical query processing on the operational data, removing the need for using expensive ETL for data migration to data warehouses, which would have restricted the AI analytics to rely on outdated data. LeanXcale core technology, its big data platform, will be further extended to support parallel analytical processing, which will be the fundamental pillar to enable the efficient parallelization of data analytics algorithm. Moreover, the big data platform will be further developed to allow the correlation of streaming data, often called data in-flight, with static data, often called data at-rest.
