Apache Hive VS Tarantool

• 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

• 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.

• High Performance
  Tarantool is renowned for its high-speed transactions and low-latency response times, making it suitable for applications that require fast data processing.
• In-Memory Storage
  Utilizes an in-memory architecture, which enhances data retrieval speeds, beneficial for real-time applications and caching solutions.
• Lua Integration
  Provides seamless integration with Lua scripting, allowing developers to easily write stored procedures and embed logic directly with the data layer.
• Scalability
  Offers features like asynchronous replication and sharding, enabling horizontal scaling for large-scale applications.
• Flexible Schema
  Supports schema-less design, giving developers the flexibility to handle data without strict schemas, making it versatile for evolving data models.

Possible disadvantages

• Limited Community Support
  Being a less mainstream technology compared to other databases, it has a smaller community, which might mean less third-party resources and community-driven support.
• Complexity
  Its powerful features can add complexity, making it potentially challenging for developers unfamiliar with in-memory databases and advanced configurations.
• Fewer Integrations
  Compared to more popular databases, Tarantool may have fewer out-of-the-box integrations with third-party applications and services.
• Learning Curve
  Requires understanding of Lua scripting and its architecture, which might pose a learning hurdle for developers accustomed to more conventional databases.
• Limited Documented Use Cases
  There is a relative scarcity of documented use cases and real-world applications, which could be a drawback for companies looking for proven and documented success stories.

Hive vs Impala - Comparing Apache Hive vs Apache Impala

5. СУБД в HighLoad. Tarantool | Технострим

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Tracking Tarantool since Mar 2021.