How do Data Scientists handle large datasets?

Data Scientists have used many distributed computing frameworks processing voluminous data, from Apache Hadoop to Apache Spark. Fundamentally, all such systems are designed to allow parallel processing of the data in different nodes and, therefore, effectively and efficiently treat a lot of information. Other techniques involve data reduction-volume: this is the practice where, through partitioning and sampling, Data Scientists work on a smaller scale compared to the actual volume so that it may be manageable while statistically significant.