Software Alternatives, Accelerators & Startups
Register | Login

Commonality VS Apache Hive

Compare Commonality VS Apache Hive and see what are their differences

Grapple
Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Commonality logo Commonality

Turn your data into results with OKR software that empowers your teams to impact your bottom line.

Apache Hive logo Apache Hive

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.
  • Commonality Landing page
    Landing page //
    2022-11-06
  • Apache Hive Landing page
    Landing page //
    2023-01-13

Commonality features and specs

  • Cost Efficiency
    Commonality offers a pricing model tailored to small and medium-sized businesses, providing cost-effective solutions compared to larger, more expensive platforms.
  • Ease of Use
    The platform is designed with a user-friendly interface that simplifies the process of managing and analyzing data, even for users with limited technical knowledge.
  • Customization
    Commonality provides customizable features and integration options that allow businesses to tailor the platform to their specific needs and workflows.

Possible disadvantages of Commonality

  • Limited Features
    Compared to more established platforms, Commonality may have a more limited range of advanced features, which could be a drawback for larger businesses with complex needs.
  • Scalability
    As a newer platform, Commonality's ability to scale with rapidly growing businesses might be limited, potentially requiring future migration to a more robust system.
  • Support Availability
    Customer support options may not be as comprehensive or responsive as those offered by larger companies, potentially leading to longer response times for resolving issues.

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.

Commonality videos

+ Add

Commonality Commercial_Mid-Review

Apache Hive videos

+ Add

Hive vs Impala - Comparing Apache Hive vs Apache Impala

Category Popularity

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

Commonality

Apache Hive

Business Intelligence
100 100%
Business Intelligence
0% 0
Databases
0 0%
Databases
100% 100
Kpi Dashboard
100 100%
Kpi Dashboard
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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

Social recommendations and mentions

Based on our record, Apache Hive seems to be more popular. It has been mentiond 9 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.

Commonality mentions (0)

We have not tracked any mentions of Commonality yet. Tracking of Commonality recommendations started around Apr 2021.

Apache Hive mentions (9)

  • 15 AWS EMR Cost Optimization Tips to Slash Your EMR Spending (2025)
    AWS EMR (Elastic MapReduce) is a fully managed big data platform. It manages the setup, configuration, and tuning of open source frameworks like Apache Hadoop, Apache Spark, Apache Hive, Presto, and more at scale on AWS infrastructure. EMR handles cluster scaling, resource allocation, and lifecycle management. This allows you to work with large datasets for various use cases, from ETL pipelines to ML workloads.... - Source: dev.to / 8 months ago
  • Apache Iceberg as storage for on-premise data store (cluster)
    Trino or Hive for SQL querying. Get Trino/Hive to talk to Nessie. Source: over 3 years ago
  • In One Minute : Hadoop
    Hive, A data warehouse infrastructure that provides data summarization and ad hoc querying. - Source: dev.to / almost 4 years ago
  • Apache Spark, Hive, and Spring Boot โ€” Testing Guide
    In this article, I'm showing you how to create a Spring Boot app that loads data from Apache Hive via Apache Spark to the Aerospike Database. More than that, I'm giving you a recipe for writing integration tests for such scenarios that can be run either locally or during the CI pipeline execution. The code examples are taken from this repository. - Source: dev.to / over 4 years ago
  • Jinja2 not formatting my text correctly. Any advice?
    ListItem(name='Apache Hive', website='https://hive.apache.org/', category='Interactive Query', short_description='Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.'),. Source: over 4 years ago
View more

What are some alternatives?

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

Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...

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

Trevor.io - Make everyone on your team a data beast

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

Reflection - Market insights for app developers

Amazon Athena - Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Loading...