Difference Between hadoop vs other system
Hadoop is a distributed file system and big data processing framework that is designed to handle large data sets distributed across a cluster of computers. It provides a scalable, fault-tolerant and cost-effective solution for storing, processing, and analyzing large amounts of data. Here are some differences between Hadoop and other systems:
Hadoop vs traditional relational databases:
Traditional relational databases are designed to work with structured data that fits in a single machine. Hadoop, on the other hand, is designed to work with unstructured or semi-structured data that is too large to fit in a single machine. Hadoop uses a distributed file system (HDFS) to store data across a cluster of machines and allows parallel processing of large data sets.
Hadoop vs Spark:
Apache Spark is an open-source data processing engine that is designed to work with large-scale data processing. Spark uses in-memory processing to speed up the processing of data compared to Hadoop, which uses disk-based processing. Spark provides a higher-level API than Hadoop, which makes it easier to write complex data processing jobs.
Hadoop vs NoSQL databases:
NoSQL databases, such as MongoDB or Cassandra, are designed to handle unstructured or semi-structured data that is too large to fit in a single machine. NoSQL databases provide a flexible data model that allows you to store data in different formats. Hadoop, on the other hand, is designed to work with large-scale data processing and provides a framework for distributed data processing.
Hadoop vs cloud-based storage and processing services:
Cloud-based storage and processing services, such as Amazon S3 or Google Cloud Storage, provide a scalable and cost-effective solution for storing and processing large data sets. These services allow you to store data in the cloud and process it using distributed processing frameworks, such as Apache Spark or Hadoop. However, using cloud-based services can be more expensive than using Hadoop, especially if you have large amounts of data to process