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ClickHouse VS Pig

Compare ClickHouse VS Pig and see what are their differences

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ClickHouse logo ClickHouse

ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

Pig logo Pig

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  • ClickHouse Landing page
    Landing page //
    2019-06-18
Not present

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.

Pig features and specs

No features have been listed yet.

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

Category Popularity

0-100% (relative to ClickHouse and Pig)

ClickHouse

Pig

Databases
97 97%
Databases
3% 3
Data Dashboard
0 0%
Data Dashboard
100% 100
Relational Databases
100 100%
Relational Databases
0% 0
Big Data
82 82%
Big Data
18% 18

User comments

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Reviews

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

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

Pig Reviews

We have no reviews of Pig yet.
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Social recommendations and mentions

Based on our record, ClickHouse seems to be a lot more popular than Pig. While we know about 60 links to ClickHouse, we've tracked only 1 mention of Pig. 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.

ClickHouse mentions (60)

  • Setting up ClickHouse on macOS and Testing with Node.js
    ClickHouse is an open-source columnar database built for high-performance analytical queries. This guide shows how I installed ClickHouse on macOS, ran it in the background using a lightweight nohup setup that stores logs and PID in hidden user folders, and tested it with a minimal Node.js + TypeScript example using @clickhouse/client. - Source: dev.to / 15 days ago
  • From Go to Rust: Supercharging Our ClickHouse UDFs with Alloy
    At Agnostic, we build open-source infrastructure for collaborative blockchain data platforms. One of our flagship tools is clickhouse-evm, a suite of high-performance User Defined Functions (UDFs) that brings native Ethereum decoding and querying capabilities directly into ClickHouse. - Source: dev.to / 3 months ago
  • 🧠 From Hive and Elastic to ClickHouse: What Surprised Me
    Over the past few weeks, I’ve been diving into ClickHouse — and it’s been full of surprises. - Source: dev.to / 3 months ago
  • 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 / 4 months 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 / 5 months ago
View more

Pig mentions (1)

  • Launch HN: C/Ua (YC X25) – Open-Source Docker Container for Computer-Use Agents
    Check out pig: https://pig.dev (I am not affiliated). - Source: Hacker News / 5 months ago

What are some alternatives?

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

MySQL - The world's most popular open source database

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

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

Azure HDInsight - Azure HDInsight is a managed Apache Hadoop cloud service that lets you run Apache Spark, Apache Hive, Apache Kafka, Apache HBase, and more.

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

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

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