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Denodo VS Apache Hive

Compare Denodo VS Apache Hive and see what are their differences

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Denodo logo Denodo

Denodo delivers on-demand real-time data access to many sources as integrated data services with high performance using intelligent real-time query optimization, caching, in-memory and hybrid strategies.

Apache Hive logo Apache Hive

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.
  • Denodo Landing page
    Landing page //
    2023-09-21
  • Apache Hive Landing page
    Landing page //
    2023-01-13

Denodo features and specs

  • Data Virtualization
    Denodo excels at data virtualization, allowing organizations to access, integrate, and manage data from various heterogeneous sources in real-time without physical data movement.
  • Performance Optimization
    Includes features like intelligent data caching and query optimization techniques that enhance performance and ensure data is retrieved swiftly.
  • Security and Governance
    Provides robust data security features, including data masking, encryption, and a comprehensive set of governance tools to ensure data privacy and compliance.
  • Agility and Flexibility
    Offers a high level of agility, allowing quick adaptation to evolving business needs and the ability to deliver new data services rapidly.
  • Enterprise Connectivity
    Supports connectivity with a vast range of data sources and applications, making it suitable for organizations with diverse data ecosystems.

Possible disadvantages of Denodo

  • Complexity
    The platform can be complex to set up and manage, requiring skilled personnel or additional training, which might be a hurdle for some organizations.
  • Cost
    Denodo can be expensive, especially for smaller enterprises, as it might involve significant licensing fees and potential additional costs for training and maintenance.
  • Learning Curve
    Users may experience a steep learning curve, particularly if they are unfamiliar with data virtualization concepts and tools.
  • Dependency on Network
    As it relies heavily on data connectivity, performance can be affected by network latency and reliability issues.
  • Limited Offline Capability
    Denodo primarily functions optimally in real-time environments and may not be suitable for scenarios requiring extensive offline data manipulation.

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.

Analysis of Denodo

Overall verdict

  • Overall, Denodo is considered a strong and reliable option for data virtualization, especially for companies that need to integrate large volumes of diverse data quickly and securely. Its advanced features and robust technology make it a suitable choice for enterprises requiring scalable and powerful data solutions.

Why this product is good

  • Denodo is well-regarded for its data virtualization platform, which allows organizations to access and integrate disparate data sources without the need for physical data relocation. Its platform is known for providing real-time, fast, and agile data access, which enhances decision-making and business processes. Denodo excels in areas like performance optimization, security, and support for a wide range of data sources, making it a strong choice for businesses looking to improve their data integration capabilities.

Recommended for

    Denodo is recommended for large enterprises, organizations with complex data landscapes, companies looking to implement a logical data warehouse, and businesses that require seamless integration of both structured and unstructured data from various sources. It's particularly beneficial for industries like finance, healthcare, and technology, where data-driven decision-making is crucial.

Denodo videos

+ Add

2018 09 07 11 06 Denodo Demo

More videos:

  • Review - Denodo Platform Enhancements - 7.0 August 2020 Update
  • Review - Denodo Platform Enhancements - 7.0 Update 20200310

Apache Hive videos

+ Add

Hive vs Impala - Comparing Apache Hive vs Apache Impala

Category Popularity

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

Denodo

Apache Hive

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
Data Integration
100 100%
Data Integration
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

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

Denodo Reviews

The 28 Best Data Integration Tools and Software for 2020
Description: The Denodo Platform offers data virtualization for joining multistructured data sources from database management systems, documents, and a wide variety of other big data, cloud, and enterprise sources. Connectivity support includes relational databases, legacy data, flat files, CML, packed applications, and emerging data types including Hadoop. Denodo is the...
Source: solutionsreview.com

Apache Hive Reviews

We have no reviews of Apache Hive yet.
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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.

Denodo mentions (0)

We have not tracked any mentions of Denodo yet. Tracking of Denodo recommendations started around Mar 2021.

Apache Hive mentions (8)

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What are some alternatives?

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

data.world - The social network for data people

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

IBM Cloud Pak for Data - Move to cloud faster with IBM Cloud Paks running on Red Hat OpenShift – fully integrated, open, containerized and secure solutions certified by IBM.

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

Zetaris Platform - Data Fabric

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

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