Amazon Athena vs Hibernate

Overview

Hibernate

Stacks1.8K

Followers1.2K

Votes34

GitHub Stars0

Forks0

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Hibernate: What are the differences?

1. Scalability - Amazon Athena is a highly scalable service that can process massive amounts of data and run queries on large datasets. On the other hand, Hibernate is an object-relational mapping (ORM) framework that provides scalability by allowing developers to work with databases at a higher level of abstraction. While both offer scalability, Amazon Athena is better suited for handling big data and running complex queries at scale.
2. Query Language - Amazon Athena uses Presto, which is a distributed SQL query engine, as its query language. It supports ANSI SQL and allows users to write queries that can span multiple data sources such as S3, Hadoop, and relational databases. Hibernate, on the other hand, uses HQL (Hibernate Query Language) or Criteria API, which are both object-oriented query languages. While both allow querying data, Amazon Athena's Presto SQL provides more flexibility and compatibility with various data sources.
3. Data Storage - Amazon Athena is designed to work with data stored in Amazon S3, which provides scalable, durable, and cost-effective storage. It allows users to query data directly from their S3 buckets without the need for loading or transforming the data. Hibernate, on the other hand, can work with a variety of data storage options, including databases, file systems, and even memory. While both support different types of data storage, Amazon Athena's integration with Amazon S3 makes it a more versatile choice.
4. Cost Model - Amazon Athena follows a pay-per-query pricing model, where users only pay for the queries they run and the amount of data scanned during the query execution. This pricing model can be cost-effective for sporadic or ad-hoc queries. Hibernate, being an open-source framework, does not have any direct costs associated with it. However, it may require infrastructure and resource costs for hosting and managing databases. Amazon Athena's pay-per-query model offers more flexibility and cost control.
5. Data Format - Amazon Athena supports querying data in various formats such as CSV, JSON, Parquet, Avro, and more. This flexibility allows users to work with different types of data without any data conversion or restructuring. Hibernate, on the other hand, is primarily focused on working with structured relational data stored in databases. While both support data querying, Amazon Athena's ability to handle different data formats makes it more versatile for diverse data sources.
6. Data Catalog - Amazon Athena uses AWS Glue as its data catalog, which allows users to create, manage, and discover metadata about data stored in various data sources. It provides a centralized repository for storing and organizing metadata, making it easier to understand and query data. Hibernate, on the other hand, does not have a built-in data catalog but relies on the underlying databases' metadata. Amazon Athena's integration with AWS Glue provides a more comprehensive and centralized approach to managing metadata.

In summary, Amazon Athena is a highly scalable service that uses Presto SQL as its query language, works with data stored in Amazon S3, follows a pay-per-query pricing model, supports various data formats, and uses AWS Glue as its data catalog. Hibernate, on the other hand, is an ORM framework that provides scalability, uses HQL or Criteria API as its query language, supports different types of data storage, does not have direct costs, primarily focuses on structured relational data, and relies on the underlying databases' metadata.

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Advice on Hibernate, Amazon Athena

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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Detailed Comparison

Hibernate	Amazon Athena
Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper.	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.
Statistics
GitHub Stars 0	GitHub Stars -
GitHub Forks 0	GitHub Forks -
Stacks 1.8K	Stacks 521
Followers 1.2K	Followers 840
Votes 34	Votes 49
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 16 Use SQL to analyze CSV files 8 Glue crawlers gives easy Data catalogue 7 Cheap 6 Query all my data without running servers 24x7 4 No data base servers yay
Integrations
Java	Amazon S3 Presto

What are some alternatives to Hibernate, Amazon Athena?

Sequelize

Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.

Apache Spark

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.

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.

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

Amazon Athena vs Hibernate: What are the differences?

1. Scalability - Amazon Athena is a highly scalable service that can process massive amounts of data and run queries on large datasets. On the other hand, Hibernate is an object-relational mapping (ORM) framework that provides scalability by allowing developers to work with databases at a higher level of abstraction. While both offer scalability, Amazon Athena is better suited for handling big data and running complex queries at scale.
2. Query Language - Amazon Athena uses Presto, which is a distributed SQL query engine, as its query language. It supports ANSI SQL and allows users to write queries that can span multiple data sources such as S3, Hadoop, and relational databases. Hibernate, on the other hand, uses HQL (Hibernate Query Language) or Criteria API, which are both object-oriented query languages. While both allow querying data, Amazon Athena's Presto SQL provides more flexibility and compatibility with various data sources.
3. Data Storage - Amazon Athena is designed to work with data stored in Amazon S3, which provides scalable, durable, and cost-effective storage. It allows users to query data directly from their S3 buckets without the need for loading or transforming the data. Hibernate, on the other hand, can work with a variety of data storage options, including databases, file systems, and even memory. While both support different types of data storage, Amazon Athena's integration with Amazon S3 makes it a more versatile choice.
4. Cost Model - Amazon Athena follows a pay-per-query pricing model, where users only pay for the queries they run and the amount of data scanned during the query execution. This pricing model can be cost-effective for sporadic or ad-hoc queries. Hibernate, being an open-source framework, does not have any direct costs associated with it. However, it may require infrastructure and resource costs for hosting and managing databases. Amazon Athena's pay-per-query model offers more flexibility and cost control.
5. Data Format - Amazon Athena supports querying data in various formats such as CSV, JSON, Parquet, Avro, and more. This flexibility allows users to work with different types of data without any data conversion or restructuring. Hibernate, on the other hand, is primarily focused on working with structured relational data stored in databases. While both support data querying, Amazon Athena's ability to handle different data formats makes it more versatile for diverse data sources.
6. Data Catalog - Amazon Athena uses AWS Glue as its data catalog, which allows users to create, manage, and discover metadata about data stored in various data sources. It provides a centralized repository for storing and organizing metadata, making it easier to understand and query data. Hibernate, on the other hand, does not have a built-in data catalog but relies on the underlying databases' metadata. Amazon Athena's integration with AWS Glue provides a more comprehensive and centralized approach to managing metadata.

Amazon Athena vs Hibernate

Overview