Feature comparison between Hadoop and ClickHouse?

Feature comparison between Hadoop and ClickHouse?


Hadoop and ClickHouse are both big data processing platforms, but they have different design goals and use cases. Here is a comparison of some key features:

  1. Data Processing: Hadoop is a data processing framework that is designed to process large amounts of data using distributed computing, while ClickHouse is a columnar database that is optimized for analytical queries and real-time analytics.
  2. Data Storage: Hadoop stores data in the Hadoop Distributed File System (HDFS), while ClickHouse stores data in a columnar format on disk.
  3. Data Model: Hadoop’s data model is based on the Hadoop Distributed File System (HDFS) and is structured, while ClickHouse has a more flexible data model and supports semi-structured data.
  4. Data Processing Engine: Hadoop uses MapReduce as the data processing engine, while ClickHouse uses a SQL-based query engine.
  5. Scalability: Hadoop is designed to scale horizontally, while ClickHouse is designed to scale horizontally and vertically.
  6. Real-time Processing: Hadoop is not designed for real-time processing, while ClickHouse is optimized for real-time analytics.
  7. Machine Learning and AI: Hadoop has support for machine learning and AI through the use of Mahout and Spark, while ClickHouse has support for machine learning and AI.
  8. Data Governance and Security: Hadoop has support for data governance and security through the use of Apache Ranger and Apache Sentry, while ClickHouse has support for data governance and security.
  9. Data Integration and ETL: Hadoop has support for data integration and ETL through the use of Apache Nifi and Apache Kafka, while ClickHouse has support for data integration and ETL.

In summary, Hadoop is a distributed data processing framework that is designed to process large amounts of data, while ClickHouse is a columnar database that is optimized for analytical queries and real-time analytics. Hadoop is not designed for real-time processing, while ClickHouse is optimized for real-time analytics and it also has support for machine learning and AI and data governance and security.

About Shiv Iyer 56 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.

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