Amazon EMR vs GINO
Overview
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CLI (Node.js)
Manual
Detailed Comparison
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. | It is a non-typical object-relational mapping library in Python for asyncio, built on top of SQLAlchemy core and asyncpg, aiming for explicitness and productivity. |
Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster;Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.;Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.;Hadoop Tools- EMR supports powerful and proven Hadoop tools such as Hive, Pig, and HBase. | Run SQLAlchemy core queries on asyncpg;
Optional non-intrusive objective API;
Advanced loader system to assemble objects from row results;
Support the SQLAlchemy ecosystem like Alembic;
Built-in support for context, lazy connection and more;
Extensions to integrate with Starlette/FastAPI, aiohttp, Tornado and more |
Statistics | |
GitHub Stars - | GitHub Stars 2.7K |
GitHub Forks - | GitHub Forks 150 |
Stacks 543 | Stacks 3 |
Followers 682 | Followers 9 |
Votes 54 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to Amazon EMR, GINO?
Google BigQuery
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
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.
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Qubole
Qubole is a cloud based service that makes big data easy for analysts and data engineers.
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.
Hibernate
Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper.
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.
Altiscale
we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.
Snowflake
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
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.
