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Apache Hive VS MatrixOne

Compare Apache Hive VS MatrixOne and see what are their differences

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Apache Hive logo Apache Hive

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

MatrixOne logo MatrixOne

Hyperconverged cloud-edge native database. Contribute to matrixorigin/matrixone development by creating an account on GitHub.
  • Apache Hive Landing page
    Landing page //
    2023-01-13
  • MatrixOne Landing page
    Landing page //
    2023-09-18

Apache Hive features and specs

  • Scalability
    Apache Hive is built on top of Hadoop, allowing it to efficiently handle large datasets by distributing the load across a cluster of machines.
  • SQL-like Interface
    Hive provides a familiar SQL-like querying language, HiveQL, which makes it easier for users with SQL knowledge to perform data analysis on large datasets without needing to learn a new syntax.
  • Integration with Hadoop Ecosystem
    Hive integrates seamlessly with other components of the Hadoop ecosystem such as HDFS for storage and MapReduce for processing, making it a versatile tool for big data processing.
  • Schema on Read
    Hive uses a schema-on-read model which allows it to work with flexible data schemas and handle unstructured or semi-structured data efficiently.
  • Extensibility
    Users can extend Hive's capabilities by writing custom UDFs (User Defined Functions), UDAFs (User Defined Aggregate Functions), and SerDes (Serializers/ Deserializers).

Possible disadvantages of Apache Hive

  • Latency in Query Processing
    Queries in Hive often take longer to execute compared to traditional databases, as they are converted to MapReduce jobs which can introduce significant latency.
  • Limited Real-time Processing
    Hive is designed for batch processing and is not suitable for real-time analytics due to its reliance on MapReduce, which is not optimized for low-latency operations.
  • Complex Configuration
    Setting up Hive and configuring it to work optimally within a Hadoop cluster can be complex and require a significant amount of effort and expertise.
  • Lack of Support for Transactions
    Hive does not natively support full ACID transactions, which can be a limitation for applications that require consistent transaction management across large datasets.
  • Dependency on Hadoop
    Hive's reliance on the Hadoop ecosystem means it inherits some of Hadoop's limitations, such as a steep learning curve and the need for substantial resources to manage a cluster.

MatrixOne features and specs

No features have been listed yet.

Apache Hive videos

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Hive vs Impala - Comparing Apache Hive vs Apache Impala

MatrixOne videos

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Category Popularity

0-100% (relative to Apache Hive and MatrixOne)

Apache Hive

MatrixOne

Databases
91 91%
Databases
9% 9
Relational Databases
84 84%
Relational Databases
16% 16
Big Data
88 88%
Big Data
12% 12
Data Warehousing
100 100%
Data Warehousing
0% 0

User comments

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Social recommendations and mentions

Based on our record, Apache Hive should be more popular than MatrixOne. It has been mentiond 8 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.

Apache Hive mentions (8)

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MatrixOne mentions (1)

  • Push or Pull, is this a question?
    Source code：matrixorigin/matrixone: Hyperconverged cloud-edge native database (github.com). - Source: dev.to / almost 2 years ago

What are some alternatives?

When comparing Apache Hive and MatrixOne, you can also consider the following products

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

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

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

Apache Pinot - Apache Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency.

Apache Druid - Fast column-oriented distributed data store

StarRocks - StarRocks offers the next generation of real-time SQL engines for enterprise-scale analytics. Learn how we make it easy to deliver real-time analytics.

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