Limitations of Hadoop in Real-time Analytics


Hadoop is a powerful, open-source ecosystem for storing and processing large datasets, but it is not well-suited for real-time analytics.

Hadoop’s limitations in Real-time Analytics

Here are a few reasons why Hadoop is not recommended for real-time analytics:

  1. Performance: Hadoop is designed for batch processing and data warehousing, which can result in longer query times. It’s not optimized for high-performance analytical queries, which are required for real-time analytics.
  2. Latency: Hadoop’s batch processing approach means that data is processed in large chunks, which can result in significant latency. This makes it difficult to provide near real-time analytics.
  3. Complexity: Hadoop requires a significant amount of configuration and management, which can be complex and time-consuming. It also requires a knowledge of programming languages such as Java or Python to work with the data.
  4. Scalability: Hadoop can scale horizontally, but it requires more resources and management than other technologies.
  5. Real-time streaming: Hadoop is not well-suited for real-time streaming data, which is becoming increasingly important for real-time analytics use cases.
  6. Cost: Hadoop can be expensive, as it requires expensive commercial licenses for some of its components, such as for HDFS and YARN.

In summary, Hadoop is a powerful tool for batch processing and data warehousing, but it is not well-suited for real-time analytics due to its high latency, complexity, and cost. Other technologies, such as ClickHouse, are better suited for real-time analytics because they are optimized for high-performance analytical queries, low latency, and real-time streaming.


Hadoop, known for batch processing, lacks suitability for real-time analytics due to performance constraints, latency issues, complexity, scalability challenges, and high costs. ClickHouse emerges as a superior alternative, offering optimized query performance, low latency, and real-time streaming capabilities.

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About Shiv Iyer 216 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.