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Hibernate VS Apache Spark

Compare Hibernate VS Apache Spark and see what are their differences

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Hibernate logo Hibernate

Hibernate an open source Java persistence framework project.

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Hibernate Landing page
    Landing page //
    2022-04-25
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Hibernate features and specs

  • Object-Relational Mapping
    Hibernate simplifies database interaction in Java by providing Object-Relational Mapping (ORM), allowing developers to map Java objects to database tables without writing repetitive SQL code.
  • Automatic Table Generation
    Hibernate can automatically generate database tables based on your Java entity classes, reducing the need for manually creating and maintaining database schemas.
  • HQL (Hibernate Query Language)
    Hibernate provides its own query language, HQL, which allows developers to write queries in an object-oriented manner and reduces the dependency on SQL.
  • Caching
    Hibernate supports caching mechanisms like first-level cache (session cache) and second-level cache, which can significantly improve performance by reducing the number of database hits.
  • Transaction Management
    Hibernate integrates with the Java Transaction API (JTA) to provide robust transaction management, ensuring data consistency and reducing the complexities of handling transactions manually.
  • Lazy Loading
    Hibernate supports lazy loading of associated entities, which can optimize performance by retrieving only the necessary data from the database on-demand.

Possible disadvantages of Hibernate

  • Learning Curve
    Hibernate has a steep learning curve for beginners due to its extensive set of features and configurations, which can be overwhelming initially.
  • Performance Overhead
    The abstraction layer provided by Hibernate can introduce a performance overhead compared to using plain SQL queries, especially in complex queries or large-scale applications.
  • Complexity in Configuration
    While Hibernate provides flexibility in configuration, it can become complex and cumbersome to manage, especially in large applications or when tuning performance.
  • Debugging Difficulty
    Debugging issues in Hibernate can be challenging due to its abstraction and proxy mechanisms, making it harder to trace problems back to the source.
  • Dependency Management
    The use of Hibernate adds additional dependencies to your project, which can complicate dependency management and increase the size of your application.
  • Limited Control Over SQL
    Hibernate abstracts away SQL, which can be a disadvantage for developers who need fine-grained control over the generated SQL and database optimizations.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Hibernate videos

+ Add

Should you Hibernate, Shut down, or put your PC to sleep?

More videos:

  • Review - GELERT Hibernate 400 sleeping bag review.
  • Tutorial - Java Hibernate Tutorial Part 8 Chapter 1 Review 1

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Hibernate and Apache Spark)

Hibernate

Apache Spark

Web Frameworks
100 100%
Web Frameworks
0% 0
Databases
0 0%
Databases
100% 100
Developer Tools
100 100%
Developer Tools
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 Hibernate and Apache Spark

Hibernate Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
MyBatis is somewhat similar to the Hibernate framework, as both facilitate communication between the application layer and the database. However, MyBatis doesn’t map Java objects to database tables like Hibernate does — instead, it links Java methods to SQL statements. As a result, SQL is visible when you’re working with the MyBatis framework, and you still have control over...
Source: raygun.com
10 Best Java Frameworks You Should Know
Hibernate is one of the best Frameworks which is capable of extending Java's Persistence API support. Hibernate is an open-source, extremely lightweight, performance-oriented, and ORM (Object-Relational-Mapping) tool.
Source: www.simplilearn.com

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Hibernate. It has been mentiond 70 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.

Hibernate mentions (16)

  • How To Secure APIs from SQL Injection Vulnerabilities
    Object-Relational Mapping frameworks like Hibernate (Java), SQLAlchemy (Python), and Sequelize (Node.js) typically use parameterized queries by default and abstract direct SQL interaction. These frameworks help eliminate common developer errors that might otherwise introduce vulnerabilities. - Source: dev.to / about 2 months ago
  • Top 10 Java Frameworks Every Dev Need to Know
    Overview: Hibernate is a Java ORM (Object Relational Mapping) framework that simplifies database operations by mapping Java objects to database tables. It allows developers to focus on business logic without worrying about SQL queries, making database interactions seamless and more maintainable. - Source: dev.to / 5 months ago
  • In One Minute : Hibernate
    Hibernate is the umbrella for a collection of libraries, most notably Hibernate ORM which provides Object/Relational Mapping for java domain objects. In addition to its own "native" API, Hibernate ORM is also an implementation of the Java Persistence API (jpa) specification. - Source: dev.to / over 2 years ago
  • Spring Boot – Black Box Testing
    I'm using Spring Data JPA as a persistence framework. Therefore, those classes are Hibernate entities. - Source: dev.to / over 2 years ago
  • How to Secure Nodejs Application.
    To prevent SQL Injection attacks to sanitize input data. You can either validate every single input or validate using parameter binding. Parameter binding is mostly used by developers as it offers efficiency and security. If you are using a popular ORM such as sequelize, hibernate, etc then they already provide the functions to validate and sanitize your data. If you are using database modules other than ORM such... - Source: dev.to / almost 3 years ago
View more

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 20 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 22 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing Hibernate and Apache Spark, you can also consider the following products

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Grails - An Open Source, full stack, web application framework for the JVM

Hadoop - Open-source software for reliable, scalable, distributed computing

Sequelize - Provides access to a MySQL database by mapping database entries to objects and vice-versa.

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

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