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Databricks, Inc. is a global data, analytics, and artificial intelligence (AI) company, founded in 2013 by the original creators of Apache Spark. [ 1 ] [ 4 ] The company provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models.
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
Ali Ghodsi (born December 1978) [3] is a Swedish-American computer scientist and entrepreneur [4] of Persian origin, specializing in distributed systems and big data.He is a co-founder and CEO of Databricks [5] [6] [7] and an adjunct professor at UC Berkeley.
In 2013, along with Matei Zaharia and other key Spark contributors, Xin co-founded Databricks, a venture-backed company based in San Francisco that offers data platform as a service, based on Spark. In 2014, Xin led a team of engineers from Databricks to compete in the Sort Benchmark and won the 2014 world record in Daytona GraySort using Spark ...
In computing, the star schema or star model is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. [1]
In programming language theory, lazy evaluation, or call-by-need, [1] is an evaluation strategy which delays the evaluation of an expression until its value is needed (non-strict evaluation) and which avoids repeated evaluations (by the use of sharing).
Some database implementations adopted the term upsert (a portmanteau of update and insert) to a database statement, or combination of statements, that inserts a record to a table in a database if the record does not exist or, if the record already exists, updates the existing record.
In computer science and statistics, the Jaro–Winkler similarity is a string metric measuring an edit distance between two sequences. It is a variant of the Jaro distance metric [1] (1989, Matthew A. Jaro) proposed in 1990 by William E. Winkler.