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Apache Hive VS AWS DataOps Development Kit

Compare Apache Hive VS AWS DataOps Development Kit and see what are their differences

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Apache Hive logo Apache Hive

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

AWS DataOps Development Kit logo AWS DataOps Development Kit

An open source development framework to help you build data workflows and modern data architecture on AWS.
  • Apache Hive Landing page
    Landing page //
    2023-01-13
  • AWS DataOps Development Kit Landing page
    Landing page //
    2023-07-25

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.

AWS DataOps Development Kit 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

AWS DataOps Development Kit videos

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Category Popularity

0-100% (relative to Apache Hive and AWS DataOps Development Kit)

Apache Hive

AWS DataOps Development Kit

Databases
94 94%
Databases
6% 6
Data Stores
0 0%
Data Stores
100% 100
Big Data
100 100%
Big Data
0% 0
Big Data Tools
0 0%
Big Data Tools
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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AWS DataOps Development Kit mentions (0)

We have not tracked any mentions of AWS DataOps Development Kit yet. Tracking of AWS DataOps Development Kit recommendations started around Jul 2022.

What are some alternatives?

When comparing Apache Hive and AWS DataOps Development Kit, you can also consider the following products

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

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

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

Splunk - Splunk's operational intelligence platform helps unearth intelligent insights from machine data.

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.

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.

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