Software Alternatives, Accelerators & Startups
Register | Login

Apache Hive VS Apache Pinot

Compare Apache Hive VS Apache Pinot and see what are their differences

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Apache Hive logo Apache Hive

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

Apache Pinot logo Apache Pinot

Apache Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency.
  • Apache Hive Landing page
    Landing page //
    2023-01-13
Not present

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.

Apache Pinot features and specs

No features have been listed yet.

Apache Hive videos

+ Add

Hive vs Impala - Comparing Apache Hive vs Apache Impala

Apache Pinot videos

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

Add video

Category Popularity

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

Apache Hive

Apache Pinot

Databases
86 86%
Databases
14% 14
Big Data
80 80%
Big Data
20% 20
Relational Databases
100 100%
Relational Databases
0% 0
Data Warehousing
84 84%
Data Warehousing
16% 16

User comments

Share your experience with using Apache Hive and Apache Pinot. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Hive and Apache Pinot

Apache Hive Reviews

We have no reviews of Apache Hive yet.
Be the first one to post

Apache Pinot Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
The biggest value behind Apache Pinot is that you can index each column, which allows it to process data at a super fast speed. “It’s like taking a pivot table and saving it to disk. So you can get this highly dimensional data with pre-computed aggregations and pull those out in what seems like supernaturally fast time,” says Tim Berglund, Developer Relations at StarTree....
Source: embeddable.com

Social recommendations and mentions

Based on our record, Apache Hive seems to be more popular. 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)

View more

Apache Pinot mentions (0)

We have not tracked any mentions of Apache Pinot yet. Tracking of Apache Pinot recommendations started around May 2025.

What are some alternatives?

When comparing Apache Hive and Apache Pinot, 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.

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

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

Apache Druid - Fast column-oriented distributed data store

DuckDB - DuckDB is an in-process SQL OLAP database management system

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.

Loading...