Difference between revisions of "Batch Processing"
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Building scalable and dependable data infrastructures required the development of batch processing. A batch processing technique called MapReduce was patented in 2004 and later included in open-source programs like Hadoop, CouchDB, and MongoDB. | Building scalable and dependable data infrastructures required the development of batch processing. A batch processing technique called MapReduce was patented in 2004 and later included in [[Open source|open-source]] programs like Hadoop, CouchDB, and MongoDB. | ||
Batch processing, as the name suggests, loads "batches" of data into a repository at predetermined intervals, usually scheduled during off-peak business hours. As batch processing jobs frequently operate with enormous volumes of data, which can strain the system as a whole, other workloads aren't impacted. When there isn't a pressing need to evaluate a particular dataset (such as monthly accounting), batch processing is typically the best [[Data Pipeline|data pipeline]]. It is more closely related to the ETL data integration process, which stands for "extract, transform, and load." | |||
A batch processing job is a workflow of commands that are executed in order, with the output of one command serving as the input for the next. For instance, one command might start data ingestion, another might start filtering out certain columns, and a third might take care of aggregation. Up until the data is fully changed and written into the data repository, this set of commands will continue. | |||
Latest revision as of 06:31, 18 September 2022
Building scalable and dependable data infrastructures required the development of batch processing. A batch processing technique called MapReduce was patented in 2004 and later included in open-source programs like Hadoop, CouchDB, and MongoDB.
Batch processing, as the name suggests, loads "batches" of data into a repository at predetermined intervals, usually scheduled during off-peak business hours. As batch processing jobs frequently operate with enormous volumes of data, which can strain the system as a whole, other workloads aren't impacted. When there isn't a pressing need to evaluate a particular dataset (such as monthly accounting), batch processing is typically the best data pipeline. It is more closely related to the ETL data integration process, which stands for "extract, transform, and load."
A batch processing job is a workflow of commands that are executed in order, with the output of one command serving as the input for the next. For instance, one command might start data ingestion, another might start filtering out certain columns, and a third might take care of aggregation. Up until the data is fully changed and written into the data repository, this set of commands will continue.