What are the benefits of lazy evaluation?
Answer / Saurabh Rana
Lazy evaluation in Apache Spark provides several benefits. Firstly, it reduces the overhead of processing intermediate results by only calculating them when necessary, improving efficiency and performance. Secondly, it allows for better parallelization as tasks can be executed concurrently, taking advantage of multi-core processors and distributed computing resources. Lastly, it enables operators to be chained together seamlessly, making data transformations more flexible and easier to manage.
| Is This Answer Correct ? | 0 Yes | 0 No |
What are shared variables?
Explain the use of broadcast variables
Can you explain how to minimize data transfers while working with Spark?
Is dataframe immutable?
What is the latest version of spark?
Is spark used for machine learning?
What language is apache spark?
Can spark work without hadoop?
How can you launch Spark jobs inside Hadoop MapReduce?
What is spark catalyst?
Define "PageRank".
Does spark use hive?
Apache Hadoop (394)
MapReduce (354)
Apache Hive (345)
Apache Pig (225)
Apache Spark (991)
Apache HBase (164)
Apache Flume (95)
Apache Impala (72)
Apache Cassandra (392)
Apache Mahout (35)
Apache Sqoop (82)
Apache ZooKeeper (65)
Apache Ambari (93)
Apache HCatalog (34)
Apache HDFS Hadoop Distributed File System (214)
Apache Kafka (189)
Apache Avro (26)
Apache Presto (15)
Apache Tajo (26)
Hadoop General (407)