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Apache Hive VS IBM Streaming Analytics

Compare Apache Hive VS IBM Streaming Analytics and see what are their differences

Hive
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Apache Hive logo Apache Hive

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

IBM Streaming Analytics logo IBM Streaming Analytics

IBM Streaming Analytics enables you to analyze a broad range of streaming text, video, audio, geospatial and sensor data.
  • Apache Hive Landing page
    Landing page //
    2023-01-13
  • IBM Streaming Analytics Landing page
    Landing page //
    2023-02-11

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.

IBM Streaming Analytics features and specs

No features have been listed yet.

Apache Hive videos

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Hive vs Impala - Comparing Apache Hive vs Apache Impala

IBM Streaming Analytics videos

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IBM Streaming Analytics and Python

Category Popularity

0-100% (relative to Apache Hive and IBM Streaming Analytics)

Apache Hive

IBM Streaming Analytics

Databases
100 100%
Databases
0% 0
Data Management
0 0%
Data Management
100% 100
Big Data
100 100%
Big Data
0% 0
Stream Processing
0 0%
Stream Processing
100% 100

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.

Apache Hive mentions (8)

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IBM Streaming Analytics mentions (0)

We have not tracked any mentions of IBM Streaming Analytics yet. Tracking of IBM Streaming Analytics recommendations started around Mar 2021.

What are some alternatives?

When comparing Apache Hive and IBM Streaming Analytics, you can also consider the following products

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

Azure Stream Analytics - Azure Stream Analytics offers real-time stream processing in the cloud.

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

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

TIBCO Spotfire - TIBCO Spotfire is a Business Intelligence (BI) solution that provides users with executive dashboards, data visualization, data analytics and KPIs push to mobile devices.

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