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Informatica Intelligent Data Platform VS Apache Hive

Compare Informatica Intelligent Data Platform VS Apache Hive and see what are their differences

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Informatica Intelligent Data Platform logo Informatica Intelligent Data Platform

Unleash data's potential with Informatica infrastructure services that all roll up under a robust and intelligent data integration platform.

Apache Hive logo Apache Hive

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.
  • Informatica Intelligent Data Platform Landing page
    Landing page //
    2023-02-04
  • Apache Hive Landing page
    Landing page //
    2023-01-13

Informatica Intelligent Data Platform features and specs

  • Comprehensive Data Integration
    Informatica Intelligent Data Platform offers robust tools for data integration, allowing organizations to seamlessly integrate data from various sources. This ensures accuracy and consistency across enterprise data.
  • Scalability
    The platform is designed to scale with the organization’s needs, accommodating increasing volumes of data without compromising performance.
  • Advanced Data Management
    The platform provides advanced data management capabilities, including data quality, data governance, and metadata management, ensuring that data is reliable and trusted.
  • Cloud and Hybrid Deployments
    Informatica supports both cloud and on-premises deployments, providing flexibility to move data across different environments according to business requirements.
  • User-Friendly Interface
    The platform features an intuitive and user-friendly interface, making it easier for users to perform complex data tasks without extensive technical expertise.

Possible disadvantages of Informatica Intelligent Data Platform

  • Complexity
    Given its vast array of features and capabilities, getting started with Informatica can be complex, requiring significant time and expertise to implement effectively.
  • Cost
    Informatica can be costly, especially for small to medium enterprises, as its licensing and operational costs may be prohibitive compared to other data management solutions.
  • Steep Learning Curve
    New users may experience a steep learning curve due to the depth of features offered, necessitating comprehensive training and possibly impacting productivity initially.
  • Integration Challenges
    While integration is a strength, there can be challenges when dealing with very diverse or legacy systems, potentially requiring custom solutions.
  • Dependency on Vendor
    Organizations may experience dependency on Informatica for updates, support, and additional features, which can affect flexibility and long-term planning.

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.

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Hive vs Impala - Comparing Apache Hive vs Apache Impala

Category Popularity

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Informatica Intelligent Data Platform

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Data Integration
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Databases
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Databases
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Data Dashboard
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Big Data
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Big Data
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User comments

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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.

Informatica Intelligent Data Platform mentions (0)

We have not tracked any mentions of Informatica Intelligent Data Platform yet. Tracking of Informatica Intelligent Data Platform recommendations started around Mar 2021.

Apache Hive mentions (8)

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What are some alternatives?

When comparing Informatica Intelligent Data Platform and Apache Hive, you can also consider the following products

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

data.world - The social network for data people

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

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.

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