How does Spark's Catalyst Optimizer improve the efficiency of data processing?
- Data Compression
- Query Compilation
- Query Plan Optimization
- Schema Evolution
Spark's Catalyst Optimizer improves data processing efficiency through query plan optimization. It leverages advanced techniques like predicate pushdown, constant folding, and rule-based transformations to generate an optimized query plan, resulting in faster and more resource-efficient execution.
Loading...
Related Quiz
- ____ in YARN architecture is responsible for dividing the job into tasks and scheduling them on different nodes.
- ____ recovery techniques in Hadoop allow for the restoration of data to a specific point in time.
- In a Hadoop cluster, ____ are crucial for maintaining continuous operation and data accessibility.
- In HBase, ____ are used to define the retention and versioning policies of data.
- How does Hive handle schema design when dealing with big data?