Question

How can Apache Spark be used alongside Hadoop?

a. Spark can replace Hadoop entirely.
b. Spark can run on Hadoop clusters and access HDFS data.
c. Spark can only run on standalone clusters.
d. Spark is not compatible with Hadoop.

Answer 1

Apache Spark can be used alongside Hadoop in various ways, including running on Hadoop clusters and accessing HDFS data, replacing certain components of Hadoop, and interacting with other Hadoop ecosystem tools.

Step-by-step explanation:

Apache Spark can be used alongside Hadoop in several ways:

1. Spark can run on Hadoop clusters and access HDFS data. Hadoop provides a distributed storage system called Hadoop Distributed File System (HDFS), and Spark can read data from HDFS and process it using its powerful distributed computing capabilities.
2. Spark can replace certain components of Hadoop. While Spark cannot entirely replace Hadoop, it can replace certain components such as MapReduce for data processing and Spark Streaming for real-time data processing.
3. Spark can interact with other Hadoop ecosystem tools. Spark can seamlessly work with other tools of the Hadoop ecosystem, such as Hive for data warehousing and integration, and HBase for real-time read/write access to Hadoop data.