ChistaDATA’s ClickHouse v/s Hadoop for Real-time Analytics

Complexities in Cloudera Hadoop Infrastructure Operations Management:

  1. Cluster Configuration and Management: Setting up and configuring a Hadoop cluster involves various components, such as NameNode, DataNodes, Resource Manager, Node Manager, etc. Managing these components, ensuring their proper configuration, and handling cluster-wide settings can be complex and time-consuming.
  2. Hardware and Resource Management: Optimizing hardware resources, such as disk space, memory, and processing power, requires careful planning and monitoring. Balancing resource allocation across multiple nodes and managing hardware failures can be challenging.
  3. Data Replication and High Availability: Maintaining data redundancy and ensuring high availability in Hadoop clusters involves setting up mechanisms like Hadoop High Availability (HA) or using tools like HDFS Federation. These configurations require expertise and continuous monitoring.
  4. Data Security: Implementing robust security measures, such as authentication, authorization, and encryption, in a Hadoop cluster can be complex. Managing user access controls, securing sensitive data, and ensuring compliance with data privacy regulations add further complexity.
  5. Job Scheduling and Monitoring: Managing job scheduling, tracking job progress, and monitoring resource utilization across multiple jobs can be intricate. Ensuring optimal job performance, troubleshooting failures, and optimizing resource allocation require continuous monitoring and analysis.

Why is Hadoop not scalable in modern real-time analytics?

  1. Batch Processing Nature: Hadoop was initially designed for batch processing and offline analytics. It is not inherently optimized for real-time analytics, where low-latency processing and rapid data ingestion are crucial.
  2. Disk-Based Processing: Hadoop’s reliance on disk-based storage and MapReduce processing introduces higher latency than modern real-time analytics platforms that leverage in-memory processing and columnar storage.
  3. Complexity and Overhead: Hadoop’s distributed nature introduces additional complexity in managing clusters, data partitioning, and handling data locality. This complexity can hinder scalability and add overhead to real-time analytics workflows.
  4. Resource Management Challenges: Scaling Hadoop clusters to handle real-time analytics workloads requires careful capacity planning, hardware provisioning, and cluster management. Ensuring efficient resource allocation and utilization becomes increasingly challenging as the workload grows.

Why do corporations globally engage ChistaDATA for real-time analytics on ClickHouse?

  1. High Performance and Scalability: ClickHouse is explicitly designed for real-time analytics and offers exceptional performance and scalability. It can handle large volumes of data and high query concurrency, making it well-suited for modern real-time analytics requirements.
  2. Real-Time Querying: ClickHouse’s architecture enables near real-time querying and analysis, providing faster insights for time-sensitive decision-making.
  3. Columnar Storage and Compression: ClickHouse’s columnar storage format and efficient compression techniques optimize storage and query performance, enabling faster data retrieval and reducing storage costs.
  4. Simplified Operations and Management: ChistaDATA provides expertise in managing ClickHouse infrastructure, handling cluster configurations, and ensuring high availability. This allows corporations to focus on their core business activities without the complexities of infrastructure management.
  5. Advanced Analytics Capabilities: ClickHouse offers a wide range of built-in analytical functions and SQL extensions, enabling advanced analytics, including time series analysis, approximate algorithms, and machine learning integrations.
  6. Data Integration and Ecosystem Compatibility: ClickHouse integrates well with existing data ecosystems, including data pipelines, stream processing frameworks, and data visualization tools. This simplifies data integration and enables seamless integration with existing analytics workflows.
  7. ClickHouse Expertise and Support: ChistaDATA has extensive experience in implementing ClickHouse for real-time analytics and provides comprehensive support, including consulting, implementation, optimization, and maintenance services.


By engaging ChistaDATA for real-time analytics on ClickHouse, corporations can leverage the power of a high-performance columnar database purpose-built for real-time analytics. They can overcome the limitations of Hadoop’s batch-processing nature, achieve faster insights, and gain a competitive edge in their data-driven decision-making processes.

To know more about Clickhouse v/s Hadoop for real-time analytics, do consider reading the following articles:

ChistaDATA: Your Trusted ClickHouse Consultative Support and Managed Services Provider. Unlock the Power of Real-Time Analytics with ChistaDATA Cloud( – the World’s Most Advanced ClickHouse DBaaS Infrastructure. Contact us at or (844)395-5717 for tailored solutions and optimal performance.

About Shiv Iyer 215 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.