Apache Kudu vs Hadoop: What are the differences?

Introduction

Apache Kudu and Hadoop are both popular open-source frameworks used for processing and analyzing big data. While they share some similarities, there are key differences between the two.

1. Data Storage: One significant difference between Apache Kudu and Hadoop is the way they handle data storage. Hadoop uses the Hadoop Distributed File System (HDFS) to store data in a distributed manner across multiple servers. On the other hand, Kudu uses a columnar storage model, which allows for efficient compression and retrieval of data.

2. Data Processing: Another difference lies in the way data is processed. Hadoop is primarily designed for batch processing of data using the MapReduce programming model. It divides large data sets into smaller chunks and processes them in parallel. In contrast, Kudu is more suitable for real-time processing and analytics. It provides mechanisms for random access and updates, making it ideal for applications that require low-latency data queries.

3. Schema Enforcement: Hadoop does not enforce explicit schemas for data stored in HDFS. It allows for flexible and dynamic schema evolution, which can be both an advantage and a challenge in certain scenarios. On the other hand, Kudu enforces a fixed schema for its tables, ensuring data consistency and compatibility. This allows for better optimization and query performance.

4. Data Updates: Hadoop processes data in a write-once, read-many fashion. Once data is written, it cannot be directly updated or deleted. Any changes require rewriting the entire dataset. In contrast, Kudu supports random reads and writes on individual rows, making it well-suited for use cases that involve frequent data updates or modifications.

5. Data Durability: Hadoop provides durability through replication, where data is replicated across multiple nodes to ensure fault tolerance. In contrast, Kudu uses its built-in replication mechanism, allowing configurable replication factors at the table level. This enables higher write throughput and lower latency for writes compared to Hadoop.

6. Integration with Ecosystem: Hadoop has a vast ecosystem of tools and frameworks that integrate well with it, making it suitable for a wide range of use cases. Kudu, on the other hand, is relatively newer and has a smaller ecosystem. However, it offers seamless integration with Apache Impala, a powerful SQL engine for real-time querying, which makes it a compelling choice for specific use cases.

In summary, Apache Kudu and Hadoop differ in terms of data storage, processing capabilities, schema enforcement, data updates, data durability, and ecosystem integration. These differences make each framework suitable for different use cases and workloads.