Spark

Hadoop and Spark are two popular big data processing frameworks that are part of the Hadoop ecosystem. Here is a brief overview of each of these technologies and how they work together:

Hadoop:

 Hadoop is an open-source software framework for storing and processing large data sets across clusters of computers. It provides a distributed file system called HDFS (Hadoop Distributed File System), which allows data to be stored across multiple nodes in a cluster. Hadoop also includes a processing framework called MapReduce, which can be used to process data in parallel across multiple nodes.

Spark:

 Spark is an open-source data processing engine that is designed to work with big data. It can run on top of Hadoop and provides faster and more efficient processing of large data sets. Spark includes a variety of components, including Spark Core, Spark SQL, Spark Streaming, and MLlib (Machine Learning Library).

Together, Hadoop and Spark provide a powerful platform for processing large data sets. Data can be stored in HDFS and processed using Spark, which provides faster and more efficient processing than Hadoop's MapReduce. Spark can also be used for real-time processing of streaming data, while Hadoop is better suited for batch processing of large data sets.

In addition to Hadoop and Spark, the Hadoop ecosystem includes many other technologies, such as HBase (a NoSQL database), Hive (a data warehouse system), Pig (a high-level scripting language for data analysis), and ZooKeeper (a coordination service for distributed applications). These technologies work together to provide a comprehensive platform for storing, processing, and analyzing big data.