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Hadoop VS ClickHouse

Compare Hadoop VS ClickHouse and see what are their differences

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Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing

ClickHouse logo ClickHouse

ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.
  • Hadoop Landing page
    Landing page //
    2021-09-17
  • ClickHouse Landing page
    Landing page //
    2019-06-18

Hadoop features and specs

  • Scalability
    Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
  • Cost-Effective
    It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
  • Fault Tolerance
    Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
  • Flexibility
    It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
  • Parallel Processing
    Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
  • Community Support
    As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

  • Complexity
    Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
  • Overhead
    The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
  • Security
    While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
  • Hardware Requirements
    Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
  • Lack of Real-Time Processing
    Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
  • Data Integrity
    Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

ClickHouse features and specs

  • High Performance
    ClickHouse is designed for fast processing of analytical queries, often performing significantly faster than traditional databases due to its columnar storage format and optimized query execution.
  • Scalability
    The system is built to handle extensive datasets by scaling horizontally through distributed cluster configurations, making it suitable for big data applications.
  • Real-time Data Ingestion
    ClickHouse supports real-time data ingestion and can immediately reflect changes in query results, which is valuable for use cases requiring instant data processing and analysis.
  • Cost Efficiency
    The open-source nature of ClickHouse makes it a cost-effective option, especially when compared to other commercial data warehouses.
  • SQL Compatibility
    ClickHouse features strong SQL support, which makes it easier for individuals with SQL expertise to transition and use the platform effectively.
  • Compression
    ClickHouse employs advanced compression algorithms that reduce storage requirements and improve query performance.

Possible disadvantages of ClickHouse

  • Complexity in Setup
    Setting up and managing ClickHouse, particularly in a distributed cluster environment, can be complex and require a higher level of expertise compared to some other database systems.
  • Limited Transaction Support
    ClickHouse is optimized for read-heavy operations and analytics but does not support full ACID transactions, which limits its use for certain transactional use cases.
  • Ecosystem and Tooling
    While the ecosystem is growing, ClickHouse still has fewer tools and third-party integrations compared to more mature databases, which can limit its utility in some environments.
  • Resource Intensive
    Running ClickHouse, especially for large datasets, can be resource-intensive, requiring significant memory and CPU resources.
  • Limited User Management
    The platform has relatively basic user management and security features, which may not meet the needs of enterprises with strict compliance and governance requirements.

Analysis of Hadoop

Overall verdict

  • Hadoop is a robust and powerful data processing platform that is well-suited for organizations that need to manage and analyze large-scale data. Its resilience, scalability, and open-source nature make it a popular choice for big data solutions. However, it may not be the best fit for all use cases, especially those requiring real-time processing or where ease of use is a priority.

Why this product is good

  • Hadoop is renowned for its ability to store and process large datasets using a distributed computing model. It is scalable, cost-effective, and efficient in handling massive volumes of data across clusters of computers. Its ecosystem includes a wide range of tools and technologies like HDFS, MapReduce, YARN, and Hive that enhance data processing and analysis capabilities.

Recommended for

  • Organizations dealing with vast amounts of data needing efficient batch processing.
  • Businesses that require scalable storage solutions to manage their data growth.
  • Companies interested in leveraging a diverse ecosystem of data processing tools and technologies.
  • Technical teams that have the expertise to manage and optimize complex distributed systems.

Analysis of ClickHouse

Overall verdict

  • ClickHouse is a powerful and capable columnar DBMS that offers excellent performance for analytical workloads. Its open-source nature allows for flexibility and community-driven improvements, making it a strong option for organizations needing a scalable analytics platform.

Why this product is good

  • ClickHouse is considered a good choice for many use cases due to its high performance in processing large volumes of data and its efficiency in executing complex analytical queries. It is designed to work well with large datasets and provides real-time query capabilities, making it ideal for applications like business intelligence, web analytics, and IoT.

Recommended for

  • Large-scale data analysis
  • Real-time analytics dashboards
  • Businesses needing high-speed query performance
  • Web analytics platforms
  • IoT data processing
  • Financial industry for quick data aggregation

Hadoop videos

+ Add

What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

ClickHouse videos

No ClickHouse videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Hadoop and ClickHouse)

Hadoop

ClickHouse

Databases
26 26%
Databases
74% 74
Big Data
100 100%
Big Data
0% 0
Relational Databases
13 13%
Relational Databases
87% 87
Data Warehousing
0 0%
Data Warehousing
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Hadoop and ClickHouse

Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
Source: www.datacamp.com
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
Source: www.analytixlabs.co.in
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is...
Source: www.analyticsinsight.net

ClickHouse Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
‍ClickHouse is an open-source, column-oriented, distributed, and OLAP database that’s very easy to set up and maintain. “Because it’s columnar, it’s the best architectural approach for aggregations and for ‘sort by’ on more than one column. It also means that group by’s are very fast. It’s distributed, replication is asynchronous, and it’s OLAP—which means it’s meant for...
Source: embeddable.com
ClickHouse vs TimescaleDB
Recently, TimescaleDB published a blog comparing ClickHouse & TimescaleDB using timescale/tsbs, a timeseries benchmarking framework. I have some experience with PostgreSQL and ClickHouse but never got the chance to play with TimescaleDB. Some of the claims about TimescaleDB made in their post are very bold, that made me even more curious. I thought it’d be a great...
Source: pradeepchhetri.xyz
20+ MongoDB Alternatives You Should Know About
ClickHouse may be a great contender for moving analytical workloads from MongoDB. Much faster, and with JSON support and Nested Data Structures, it can be great choice for storing and analyzing document data.
Source: www.percona.com

Social recommendations and mentions

Based on our record, ClickHouse should be more popular than Hadoop. It has been mentiond 57 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Hadoop mentions (25)

  • Apache Hadoop: Open Source Business Model, Funding, and Community
    This post provides an in‐depth look at Apache Hadoop, a transformative distributed computing framework built on an open source business model. We explore its history, innovative open funding strategies, the influence of the Apache License 2.0, and the vibrant community that drives its continuous evolution. Additionally, we examine practical use cases, upcoming challenges in scaling big data processing, and future... - Source: dev.to / about 1 month ago
  • What is Apache Kafka? The Open Source Business Model, Funding, and Community
    Modular Integration: Thanks to its modular approach, Kafka integrates seamlessly with other systems including container orchestration platforms like Kubernetes and third-party tools such as Apache Hadoop. - Source: dev.to / about 1 month ago
  • India Open Source Development: Harnessing Collaborative Innovation for Global Impact
    Over the years, Indian developers have played increasingly vital roles in many international projects. From contributions to frameworks such as Kubernetes and Apache Hadoop to the emergence of homegrown platforms like OpenStack India, India has steadily carved out a global reputation as a powerhouse of open source talent. - Source: dev.to / about 1 month ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 3 months ago
  • Apache Hadoop: Pioneering Open Source Innovation in Big Data
    Apache Hadoop is more than just software—it’s a full-fledged ecosystem built on the principles of open collaboration and decentralized governance. Born out of a need to process vast amounts of information efficiently, Hadoop uses a distributed file system and the MapReduce programming model to enable scalable, fault-tolerant computing. Central to its success is a diverse ecosystem that includes influential... - Source: dev.to / 3 months ago
View more

ClickHouse mentions (57)

  • Cross-Compiling Haskell under NixOS with Docker
    I attended the AWS Summit 2025 in Singapore. I enjoyed the event. There were booths from various companies which I found interesting, such as GitLab and ClickHouse. More importantly, I got to meet very interesting people. - Source: dev.to / 15 days ago
  • How to Build a Streaming Deduplication Pipeline with Kafka, GlassFlow, and ClickHouse
    ClickHouse: A fast columnar database. It will be our final destination for clean data. And, for simplicity in this tutorial, we'll cleverly use it as our "memory" or state store to remember which events we've already seen recently. - Source: dev.to / about 1 month ago
  • Why You Shouldn’t Invest In Vector Databases?
    In fact, even in the absence of these commercial databases, users can effortlessly install PostgreSQL and leverage its built-in pgvector functionality for vector search. PostgreSQL stands as the benchmark in the realm of open-source databases, offering comprehensive support across various domains of database management. It excels in transaction processing (e.g., CockroachDB), online analytics (e.g., DuckDB),... - Source: dev.to / about 2 months ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    ClickHouse: ClickHouse is an open-source columnar database management system designed for high-performance analytics. It excels at processing large volumes of data and offers real-time querying capabilities. It’s probably the world’s fastest real-time data analytics system: ClickHouse Benchmark. - Source: dev.to / 2 months ago
  • DeepSeek's Data Breach: A Wake-Up Call for AI Data Security
    Further investigation revealed that these ports provided direct access to a publicly exposed ClickHouse database—entirely unprotected and requiring no authentication. This discovery raised immediate security concerns, as ClickHouse is an open-source, columnar database management system designed for high-speed analytical queries on massive datasets. Originally developed by Yandex, ClickHouse is widely used for... - Source: dev.to / 4 months ago
View more

What are some alternatives?

When comparing Hadoop and ClickHouse, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

MySQL - The world's most popular open source database

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

Apache Doris - Apache Doris is an open-source real-time data warehouse for big data analytics.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

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