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List of GitHub repositories of the project: IBM This data is not pre-processed List of GitHub repositories of the project: IBM Cloud This data is not pre-processed List of GitHub repositories of the project: Build Lab Team This data is not pre-processed List of GitHub repositories of the project: Terraform IBM Modules This data is not pre-processed
The development of the product began in 2014 as a grassroots incubation project in the Israeli R&D center of Microsoft, [12] with the internal code name 'Kusto' [9] [7] (named after Jacques Cousteau, as a reference to "exploring the ocean of data"). The project aim was to address Azure services' needs for fast and scalable log and telemetry ...
Programming with Big Data in R (pbdR) [1] is a series of R packages and an environment for statistical computing with big data by using high-performance statistical computation. [ 2 ] [ 3 ] The pbdR uses the same programming language as R with S3/S4 classes and methods which is used among statisticians and data miners for developing statistical ...
In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data pre-processing. [ 225 ] Big data is a buzzword and a "vague term", [ 226 ] [ 227 ] but at the same time an "obsession" [ 227 ] with entrepreneurs, consultants, scientists, and the media.
An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." [12] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a ...
Hudi: provides atomic upserts and incremental data streams on Big Data; Iceberg: an open standard for analytic SQL tables, designed for high performance and ease of use. Ignite: an In-Memory Data Fabric providing in-memory data caching, partitioning, processing, and querying components [8] Impala: a high-performance distributed SQL engine
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The job tracker schedules map or reduce jobs to task trackers with an awareness of the data location. For example: if node A contains data (a, b, c) and node X contains data (x, y, z), the job tracker schedules node A to perform map or reduce tasks on (a, b, c) and node X would be scheduled to perform map or reduce tasks on (x, y, z).