Difference Between Hadoop And Spark
With its in-memory computing, Apache Spark outperforms Hadoop, enabling much faster processing through the caching of data over several computations and a decreased dependency on disk I/O. It makes programming more accessible by providing high-level APIs for a variety of workloads, including batch processing, real-time analytics, machine learning, and graph processing.
A core Spark data structure called the Resilient Distributed Dataset (RDD) allows for parallel processing and fault tolerance by representing a distributed collection of items over a cluster.
Hadoop |
Spark |
---|---|
Batch processing mostly using the MapReduce concept. |
Processing modes include batch, real-time, iterative, and interactive. |
Disk based data processing. |
In memory based data processing |
It has java as primary language with mapreduce paradigm. |
Supports APIs that are high-level in a variety of languages, including Scala, Python, and Java. |
Fault tolerance is offered through replication of data. |
Uses RDDs for fault tolerance. |
Provides a diverse ecosystem with tools such as HDFS, Hive, and Pig. |
Extending the ecosystem by adding libraries for streaming, graph processing, machine learning, etc. |
Azure Data Bricks For Spark-Based Analytics
Microsoft Azure is a cloud computing platform that provides a variety of services, including virtual machines, Azure App Services, Azure Storage, Azure Data Bricks, and more. Businesses may use Azure to create, deploy, and manage apps and services over Microsoft’s worldwide data center network. Microsoft Azure competes with other major cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP), and it is utilized by organizations of all sizes in a variety of sectors for cloud computing.