Batch Processing: Large, Complex Data Analysis
                     Batch Processing: Large, 
Complex Data Analysis
With batch processing, data is collected in batches and 
then fed into an analytics system. A “batch” is a group of 
data points collected within a given time period.
Batch Processing: Large, 
Complex Data Analysis
Unlike stream processing, batch processing does not immediately 
feed data into an analytics system, so results are not available in 
real-time. With batch processing, some type of storage is required 
to load the data, such as a database or a file system.
Batch Processing: Large, 
Complex Data Analysis
Batch processing is ideal for very large data sets and projects that 
involve deeper data analysis. The method is not as desirable for 
projects that involve speed or real-time results. Additionally, many 
legacy systems only support batch processing.
Batch Processing: Large, 
Complex Data Analysis
This often forces teams to use batch processing during a cloud 
data migration involving older mainframes and servers. In terms of 
performance, batch processing is also optimal when the data has 
already been collected.
Batch Processing Example: Each day, a retailer keeps track of 
overall revenue across all stores. Instead of processing every 
purchase in real-time, the retailer processes the batches of each 
store’s daily revenue totals at the end of the day.
For information on real time vs batch processing  please visit 
Rivery.io