In Apache Spark, efficient data management is essential for maximizing performance in distributed computing. Partitioning, repartitioning, and coalescing actively govern how data organizes and distributes across the cluster. Partitioning involves dividing datasets into smaller chunks, enabling parallel processing and optimizing operations. Repartitioning allows for the redistribution of data across partitions, adjusting the balance for more […]
Posts Tagged ‘partitioning’
New In-Memory Architecture in MicroStrategy 10 Enables Speed-of-Thought Report Performance
In today’s world, users expect their reporting applications to provide near instantaneous response times despite the fact that today’s analytic applications consist of dashboards containing dozens of visualizations built on top of exploding data volumes. MicroStrategy has made significant changes to its in-memory architecture as part of their version 10 release to address the new […]