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Cloudberry Database VS Apache Hive

Compare Cloudberry Database VS Apache Hive and see what are their differences

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Cloudberry Database logo Cloudberry Database

Next-gen unified database for Analytics and AI.

Apache Hive logo Apache Hive

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.
  • Cloudberry Database Landing page
    Landing page //
    2024-05-17

Cloudberry Database is created by a team of original Greenplum Database developers and ASF committers. We aim to bring modern computing capabilities to the traditional distributed MPP database to support Analytics and AI/ML workloads in one platform.

As a derivative of Greenplum Database 7, Cloudberry Database is compatible with Greenplum Database, but it's shipped with a newer PostgreSQL 14.4 kernel (scheduled kernel upgrade yearly) and a bunch of features Greenplum Database lacks or does not support.

  • Apache Hive Landing page
    Landing page //
    2023-01-13

Cloudberry Database features and specs

No features have been listed yet.

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.

Cloudberry Database videos

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Apache Hive videos

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Hive vs Impala - Comparing Apache Hive vs Apache Impala

Category Popularity

0-100% (relative to Cloudberry Database and Apache Hive)

Cloudberry Database

Apache Hive

Databases
19 19%
Databases
81% 81
Data Warehousing
29 29%
Data Warehousing
71% 71
Big Data
0 0%
Big Data
100% 100
Big Data Analytics
100 100%
Big Data Analytics
0% 0

User comments

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Social recommendations and mentions

Based on our record, Apache Hive should be more popular than Cloudberry Database. 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.

Cloudberry Database mentions (1)

  • Show HN: Apache Cloudberry 2.0.0 – First ASF release of MPP database
    - Changelog: https://cloudberry.apache.org/releases/2.0.0-incubating. - Source: Hacker News / about 1 month ago

Apache Hive mentions (8)

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What are some alternatives?

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

Teradata Database - Teradata Database is a high performance analytical database.

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

SAP BW - SAP BW Tutorial - SAP Business Warehouse (BW) integrates data from different sources, transforms and consolidates the data, does data cleansing, and storing of data as well. It a

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

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