A naukri.com initiative
Ubuntu
2w
142
Spark or Hadoop: the best choice for big data teams?
- Apache Spark is an open-source, distributed processing system that allows large amounts of data to be processed efficiently.
- Spark solved several performance problems related to processing large datasets, making it the number one choice in the industry and a direct competitor of Hadoop.
- Spark's strength is distributed computing, making it a champion for operations on large datasets.
- Apache Spark’s architecture consists of three main components: the driver, the executor, and the partitioner. It utilises a manager/worker configuration, where a manager determines the number of worker nodes needed and how they should function.
- Generally, Spark's advantage over Hadoop is speed. Spark is able to perform tasks up to 100 times faster than Hadoop, making it a great solution for low-latency processing use cases, such as machine learning.
- Using Apache Spark on Kubernetes offers numerous advantages over other cluster resource managers, such as Apache YARN, including simplified deployment, management, and authentication.
- Spark offers four main built-in libraries: Spark SQL, Spark Streaming, MLlib and GraphX, providing a large set of functionalities for different operations, such as data streaming, dataset handling, and machine learning.
- Common use cases for Spark include processing large volumes of data, complex operations, scalability requirements, performance improvements for large datasets, and machine learning.
- It is not always the case that Apache Spark and Hadoop are competing solutions and they can be used together depending on business needs.
- Canonical’s Charmed Apache Spark on Kubernetes simplifies the deployment and management process, offering greater flexibility, performance, and ease of use, ensuring quick, reliable, and scalable data processing.
Read Full Article
8 Likes
For uninterrupted reading, download the app