Julia has quickly become the preferred programming language for data and analytics. Julia combines the functionality of quantitative environments such as R and Python with the speed of production programming languages like Java and C++ to solve big data and analytics problems. Julia provides parallel and distributed computing capabilities out of the box, and literally infinite scalability with minimal effort. It delivers dramatic improvements in simplicity, speed, capacity, and productivity for data scientists, algorithmic traders, quants, scientists, and engineers who need to solve massive computation problems quickly and accurately.

Julia offers an unbeatable combination of simplicity plus speed that is thousands of times faster than other mathematical, scientific and statistical computing languages.

Partners and users include: IBM, Intel, DARPA, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC), Federal Aviation Administration (FAA), MIT Lincoln Labs, Moore Foundation, Nobel Laureate Thomas J. Sargent, Federal Reserve Bank of New York (FRBNY), Brazilian National Development Bank (BNDES), BlackRock, Conning, Berkery Noyes, BestX and many of the world's largest investment banks, asset managers, fund managers, foreign exchange analysts, insurers, hedge funds and regulators.