Hadoop vs Scylla: What are the differences?

Introduction

Hadoop and Scylla are both popular open-source distributed database systems used for handling big data. However, they differ in several key aspects. This article will outline the major differences between Hadoop and Scylla.

1. Architecture: Hadoop follows a master-slave architecture where a single NameNode coordinates multiple DataNodes. On the other hand, Scylla is built on a shared-nothing architecture, where each node operates independently and there is no centralized coordination.

2. Data Model: Hadoop is based on the Hadoop Distributed File System (HDFS), which is a distributed file system that stores data as blocks. It uses a batch processing model for data analysis. In contrast, Scylla is a NoSQL database that follows a columnar data model, which offers high write and read performance for a wide range of workloads.

3. Scalability: Hadoop is highly scalable and can handle petabytes of data by adding more nodes to the cluster. However, scaling Hadoop clusters can be complex and require careful configuration. Scylla, on the other hand, is designed for horizontal scalability with automatic data distribution and load balancing, making it easier to scale as data grows.

4. Latency: Hadoop is optimized for high throughput but may have higher latency due to its batch processing nature. In contrast, Scylla is designed for low-latency operations, making it suitable for use cases that require real-time data processing and low latency, such as online transaction processing (OLTP) and real-time analytics.

5. Consistency and Durability: Hadoop provides eventual consistency and offers fault-tolerance through data replication across multiple nodes. However, in the event of a node failure, there may be a delay in data recovery. Scylla, on the other hand, offers strong consistency guarantees with immediate data availability and durability through synchronous replication, ensuring high data integrity.

6. Ecosystem: Hadoop has a rich ecosystem with various tools and frameworks, such as MapReduce, Hive, Pig, and Spark, which provide advanced data processing capabilities. Scylla, although newer, has its own ecosystem and integrates well with popular frameworks like Apache Kafka and Prometheus, making it suitable for real-time streaming and monitoring applications.

In summary, Hadoop and Scylla differ in their architecture, data model, scalability, latency, consistency, and ecosystem. Hadoop is more suitable for batch processing and handling large volumes of data, while Scylla excels in low-latency operations, real-time analytics, and high data integrity.