Choice of the storage backend

Hadoop has become the backbone of many organizations' big data operations due to its scalable, flexible, and cost-effective nature. Born out of a need to process an ever-growing amount of data, as we see now in WEB3.

Advantages of Hadoop:

  • Scalability: Hadoop clusters can be easily scaled up by simply adding more nodes. This means providers can start with the data they have and scale up as their data grows.

  • Cost-effective: Hadoop uses commodity hardware to store large quantities of data, which dramatically reduces the cost per terabyte of storage.

  • Flexibility: Hadoop is not limited to MapReduce, and we use our own Kubernetes operator which can schedule jobs inside and outside the Hadoop cluster letting us maximize all available CPU and network resources.

DH3 Hadoop operator

Deploying Hadoop to Kubernetes can enhance Hadoop's scalability and deployment flexibility further. Kubernetes, an open-source platform for managing containerized workloads and services, facilitates both declarative configuration and automation.

Implementing Hadoop on Kubernetes involves containerizing Hadoop services, configuring persistent storage solutions to manage HDFS, and setting up network configurations for seamless communication between the Hadoop components.

Data providers already running a custom Kubernetes operator specifically designed for Hadoop deployments, ensuring a smooth deployment process across different bare-metal environments. This allows providers to shift their emphasis towards the expansion of data lakes, thereby minimizing the redundancy of infra.

In conclusion, Hadoop's design philosophy of high fault tolerance, cost-effectiveness, and scalability, combined with Kubernetes' dynamic and flexible nature, presents a formidable solution for managing big data.

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