Apache Spark vs Hibernate

Overview

Hibernate

Stacks1.8K

Followers1.2K

Votes34

GitHub Stars0

Forks0

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs Hibernate: What are the differences?

Introduction

Apache Spark and Hibernate are both popular open-source technologies used in the world of big data and data processing. While they serve similar purposes, there are key differences that set them apart. In this article, we will explore and discuss the main differences between Apache Spark and Hibernate.

Scalability and Distributed Processing: Apache Spark is designed for handling large-scale data processing tasks and provides built-in support for parallel processing. It can distribute data across multiple nodes, allowing it to handle big data workloads efficiently. On the other hand, Hibernate is an Object-Relational Mapping (ORM) framework that focuses on simplifying database access for Java applications. It operates at a smaller scale and does not have built-in support for distributed processing like Apache Spark.
Data Processing Capabilities: Apache Spark offers a wide range of libraries and APIs, making it a powerful platform for various data processing tasks. It provides high-level APIs for batch processing, streaming, machine learning, and graph processing. Hibernate, on the other hand, primarily focuses on database interaction and provides features for mapping Java objects to relational databases. While it does support querying databases and performing data operations, it does not have the extensive data processing capabilities of Apache Spark.
Support for Real-Time Data: Apache Spark's streaming libraries enable real-time processing of data streams. It supports real-time data ingestion, processing, and analytics, making it suitable for use cases that require real-time insights. Hibernate, being primarily an ORM framework, does not have built-in support for real-time data processing. It is more suitable for applications that need to interact with relational databases rather than handle real-time data streams.
Flexibility and Language Support: Apache Spark provides APIs and libraries for various programming languages, including Java, Scala, and Python. This flexibility allows developers to choose the language they are most comfortable with for writing Spark applications. Hibernate, on the other hand, is built specifically for Java applications and provides Java-based APIs for interacting with databases. It does not have the same level of language flexibility as Apache Spark.
Use Cases and Application Scenarios: Apache Spark is commonly used for big data processing tasks such as data exploration, ETL (Extract, Transform, Load) processes, machine learning, and real-time analytics. It is well-suited for scenarios where large volumes of data need to be processed efficiently. On the other hand, Hibernate is widely used in Java applications that interact with relational databases. It simplifies the database access layer by abstracting away the complexities of SQL queries and providing an easy-to-use object-oriented interface.
Performance and Execution Model: Apache Spark's in-memory computing capabilities and optimized execution model make it highly performant for big data processing tasks. It leverages distributed processing and caching techniques to achieve faster data processing speeds. Hibernate, being an ORM framework, focuses more on database interactions and mapping objects to database records. While Hibernate can optimize database operations, it may not provide the same level of performance as Apache Spark when it comes to handling large-scale data processing tasks.

In summary, Apache Spark and Hibernate have distinct differences in terms of scalability, data processing capabilities, real-time data support, flexibility, use cases, and performance. Apache Spark is more suitable for big data processing tasks, while Hibernate is focused on simplifying database access in Java applications.

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Advice on Hibernate, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Hibernate	Apache Spark
Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper.	Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
-	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars 0	GitHub Stars 42.2K
GitHub Forks 0	GitHub Forks 28.9K
Stacks 1.8K	Stacks 3.1K
Followers 1.2K	Followers 3.5K
Votes 34	Votes 140
Pros & Cons
Pros 22 Easy ORM 8 Easy transaction definition 3 Is integrated with spring jpa 1 Open Source Cons 3 Can't control proxy associations when entity graph used	Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed
Integrations
Java	No integrations available

What are some alternatives to Hibernate, Apache Spark?

Sequelize

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Presto

Distributed SQL Query Engine for Big Data

Prisma

Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js.

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.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Doctrine 2

Doctrine 2 sits on top of a powerful database abstraction layer (DBAL). One of its key features is the option to write database queries in a proprietary object oriented SQL dialect called Doctrine Query Language (DQL), inspired by Hibernates HQL.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

MikroORM

TypeScript ORM for Node.js based on Data Mapper, Unit of Work and Identity Map patterns. Supports MongoDB, MySQL, MariaDB, PostgreSQL and SQLite databases.

Related Comparisons

Apache Spark vs Hibernate: What are the differences?

Introduction

Scalability and Distributed Processing: Apache Spark is designed for handling large-scale data processing tasks and provides built-in support for parallel processing. It can distribute data across multiple nodes, allowing it to handle big data workloads efficiently. On the other hand, Hibernate is an Object-Relational Mapping (ORM) framework that focuses on simplifying database access for Java applications. It operates at a smaller scale and does not have built-in support for distributed processing like Apache Spark.
Data Processing Capabilities: Apache Spark offers a wide range of libraries and APIs, making it a powerful platform for various data processing tasks. It provides high-level APIs for batch processing, streaming, machine learning, and graph processing. Hibernate, on the other hand, primarily focuses on database interaction and provides features for mapping Java objects to relational databases. While it does support querying databases and performing data operations, it does not have the extensive data processing capabilities of Apache Spark.
Support for Real-Time Data: Apache Spark's streaming libraries enable real-time processing of data streams. It supports real-time data ingestion, processing, and analytics, making it suitable for use cases that require real-time insights. Hibernate, being primarily an ORM framework, does not have built-in support for real-time data processing. It is more suitable for applications that need to interact with relational databases rather than handle real-time data streams.
Flexibility and Language Support: Apache Spark provides APIs and libraries for various programming languages, including Java, Scala, and Python. This flexibility allows developers to choose the language they are most comfortable with for writing Spark applications. Hibernate, on the other hand, is built specifically for Java applications and provides Java-based APIs for interacting with databases. It does not have the same level of language flexibility as Apache Spark.
Use Cases and Application Scenarios: Apache Spark is commonly used for big data processing tasks such as data exploration, ETL (Extract, Transform, Load) processes, machine learning, and real-time analytics. It is well-suited for scenarios where large volumes of data need to be processed efficiently. On the other hand, Hibernate is widely used in Java applications that interact with relational databases. It simplifies the database access layer by abstracting away the complexities of SQL queries and providing an easy-to-use object-oriented interface.
Performance and Execution Model: Apache Spark's in-memory computing capabilities and optimized execution model make it highly performant for big data processing tasks. It leverages distributed processing and caching techniques to achieve faster data processing speeds. Hibernate, being an ORM framework, focuses more on database interactions and mapping objects to database records. While Hibernate can optimize database operations, it may not provide the same level of performance as Apache Spark when it comes to handling large-scale data processing tasks.

Apache Spark vs Hibernate

Overview

Apache Spark vs Hibernate: What are the differences?