Amazon EMR vs MongoDB

Overview

Amazon EMR

Stacks543

Followers682

Votes54

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Amazon EMR vs MongoDB: What are the differences?

Introduction

This markdown code provides a comparison between Amazon EMR and MongoDB, focusing on the key differences between the two.

Scalability and Big Data Processing: Amazon EMR is specifically designed for dealing with large-scale data processing tasks, utilizing Apache Hadoop and other open-source big data frameworks. It employs a distributed computing approach to handle enormous amounts of data with ease. On the other hand, MongoDB is a NoSQL database system that offers horizontal scalability but may not be as well-suited for complex big data processing tasks.
Data Modeling and Schema: While Amazon EMR focuses on distributed processing and doesn't enforce a strict schema, MongoDB utilizes a flexible document model, allowing schema to vary across different collections. This allows for easy data modeling and dynamic changes to the schema, making it suitable for agile development where requirements evolve over time.
ACID Compliance and Transaction Support: Amazon EMR is optimized for batch processing and is not primarily designed for transactional workloads. It sacrifices ACID (Atomicity, Consistency, Isolation, Durability) compliance in favor of high-throughput data processing. On the contrary, MongoDB is ACID-compliant and supports multi-document transactions, making it more suitable for transactional applications that require strong consistency and data integrity.
Query Language and Expressiveness: Amazon EMR utilizes Hive, Pig, and Spark, offering SQL-like query languages for data processing tasks. However, these languages may have limitations compared to the flexibility provided by MongoDB's query language. MongoDB's query language is expressive and powerful, allowing developers to perform complex queries, aggregations, and joins, making it more suitable for data manipulation and analysis.
Data Distribution and Replication: In Amazon EMR, data is stored in a distributed file system (such as Hadoop Distributed File System - HDFS) and is automatically replicated across multiple nodes for fault tolerance. In contrast, MongoDB provides built-in sharding, allowing data distribution across clusters and automatic replication for high availability. This makes MongoDB a better choice for scenarios where data needs to be horizontally scaled and replicated across geographically distributed locations.
Integration with Other Services and Ecosystem: Amazon EMR seamlessly integrates with other AWS services and can easily interact with data stored in Amazon S3, Amazon Redshift, and other services. It is tightly integrated into the AWS ecosystem, allowing users to leverage existing services and tools. MongoDB also provides plugins and connectors to integrate with various systems, but it may not have the same level of integration and compatibility with the broader cloud ecosystem as Amazon EMR.

In Summary, Amazon EMR is focused on large-scale data processing with high scalability and availability, while MongoDB excels at providing a flexible data model, strong consistency, and expressive querying capabilities. The choice between the two depends on specific use cases and requirements.

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Advice on Amazon EMR, MongoDB

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

Amazon EMR	MongoDB
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.	MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
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.	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars -	GitHub Stars 27.7K
GitHub Forks -	GitHub Forks 5.7K
Stacks 543	Stacks 96.6K
Followers 682	Followers 82.0K
Votes 54	Votes 4.1K
Pros & Cons
Pros 15 On demand processing power 12 Don't need to maintain Hadoop Cluster yourself 7 Hadoop Tools 6 Elastic 4 Backed by Amazon	Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language

What are some alternatives to Amazon EMR, MongoDB?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Amazon EMR vs MongoDB: What are the differences?

Introduction

This markdown code provides a comparison between Amazon EMR and MongoDB, focusing on the key differences between the two.

Scalability and Big Data Processing: Amazon EMR is specifically designed for dealing with large-scale data processing tasks, utilizing Apache Hadoop and other open-source big data frameworks. It employs a distributed computing approach to handle enormous amounts of data with ease. On the other hand, MongoDB is a NoSQL database system that offers horizontal scalability but may not be as well-suited for complex big data processing tasks.
Data Modeling and Schema: While Amazon EMR focuses on distributed processing and doesn't enforce a strict schema, MongoDB utilizes a flexible document model, allowing schema to vary across different collections. This allows for easy data modeling and dynamic changes to the schema, making it suitable for agile development where requirements evolve over time.
ACID Compliance and Transaction Support: Amazon EMR is optimized for batch processing and is not primarily designed for transactional workloads. It sacrifices ACID (Atomicity, Consistency, Isolation, Durability) compliance in favor of high-throughput data processing. On the contrary, MongoDB is ACID-compliant and supports multi-document transactions, making it more suitable for transactional applications that require strong consistency and data integrity.
Query Language and Expressiveness: Amazon EMR utilizes Hive, Pig, and Spark, offering SQL-like query languages for data processing tasks. However, these languages may have limitations compared to the flexibility provided by MongoDB's query language. MongoDB's query language is expressive and powerful, allowing developers to perform complex queries, aggregations, and joins, making it more suitable for data manipulation and analysis.
Data Distribution and Replication: In Amazon EMR, data is stored in a distributed file system (such as Hadoop Distributed File System - HDFS) and is automatically replicated across multiple nodes for fault tolerance. In contrast, MongoDB provides built-in sharding, allowing data distribution across clusters and automatic replication for high availability. This makes MongoDB a better choice for scenarios where data needs to be horizontally scaled and replicated across geographically distributed locations.
Integration with Other Services and Ecosystem: Amazon EMR seamlessly integrates with other AWS services and can easily interact with data stored in Amazon S3, Amazon Redshift, and other services. It is tightly integrated into the AWS ecosystem, allowing users to leverage existing services and tools. MongoDB also provides plugins and connectors to integrate with various systems, but it may not have the same level of integration and compatibility with the broader cloud ecosystem as Amazon EMR.

Amazon EMR vs MongoDB

Overview

Amazon EMR vs MongoDB: What are the differences?