Why Hadoop is not recommended for 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. 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.