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Computerized batch processing is a method of running software programs called jobs in batches automatically. While users are required to submit the jobs, no other interaction by the user is required to process the batch. Batches may automatically be run at scheduled times as well as being run contingent on the availability of computer resources.
The batch layer precomputes results using a distributed processing system that can handle very large quantities of data. The batch layer aims at perfect accuracy by being able to process all available data when generating views. This means it can fix any errors by recomputing based on the complete data set, then updating existing views.
The most recent backup is the son, the previous the father, and the oldest backup is the grandfather. This method is commonly used for a batch transaction processing system with a magnetic tape drive. If the system fails during a batch run, the master file is recreated by restoring the son backup and then restarting the batch.
A job scheduler is a computer application for controlling unattended background program execution of jobs. [1] This is commonly called batch scheduling, as execution of non-interactive jobs is often called batch processing, though traditional job and batch are distinguished and contrasted; see that page for details.
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
Apache Flink is an open-source, unified stream-processing and batch-processing framework developed by the Apache Software Foundation. The core of Apache Flink is a distributed streaming data-flow engine written in Java and Scala. [3] [4] Flink executes arbitrary dataflow programs in a data-parallel and pipelined (hence task parallel) manner. [5]
By way of illustration, the following code fragments demonstrate detection of patterns within event streams. The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration). This code fragment illustrates a JOIN of two data streams ...
The Internet Engineering Task Force has defined RFCs for internet remote job entry protocols, but they are now considered obsolete or legacy. [14] The 200 USER Terminal is a remote batch terminal and protocol [15] developed by the Control Data Corporation for their CDC 6000 series and CDC 3000 series mainframe computers in the 1960s. A 200 USER ...