London, United Kingdom — September 17, 2019 — MarkLogic Corporation, the next-generation data platform provider for simplifying data integration, raised over $30,000 during its second annual charity bike ride from London to Amsterdam on September 12-15.
The 23 participants, comprised of MarkLogic employees from the London, Munich, New York, Silicon Valley, Utrecht, and Washinton, D.C. offices, spent months training in preparation for their 205-mile journey to raise money for the charity of their choosing. The charities included Demelza, an organization that provides hospice care for children, Autism Speaks and the Allied Forces Foundation, among several others. Last year, MarkLogic raised over $20,000 during the company’s first charity bike ride from London to Paris.
“The charity bike ride was an amazing experience and achievement by a great team of employees,” stated Roger Lee, Senior Director of EMEA Solutions Engineering at MarkLogic. “I’m overwhelmed by the generosity and support we received for the ride from the company, our families as well as our friends in the local technology and business communities.”
The charity bike ride is one of MarkLogic’s many annual philanthropic efforts to give back to both local communities and national organizations around the world. Earlier this year, MarkLogic employees volunteered at the Second Harvest Food Bank and put together backpack supply kits for the Hope Supply Co. At MarkLogic World 2019, held in Washington, D.C. in May, MarkLogic also donated funds to local nonprofit organizations, including DC SCORES and the Capital Area Food Bank.
MarkLogic views philanthropy as an important part of company culture and makes it a priority to participate in at least one philanthropic effort each quarter. Leading by example, Gary Bloom, CEO of MarkLogic, regularly participates in volunteer search and rescue efforts and provides emergency medical care at concerts in the Bay Area with Rock Medicine.
MarkLogic helps customers create value from complex data faster. Our platform ingests data from any source, creating and refining metadata to support powerful models. Customers use these models for deep search and query, building enterprise applications and bringing unique insights to analytics and machine learning.