Airflow vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Airflow

Stacks1.7K

Followers2.8K

Votes128

Airflow vs MongoDB: What are the differences?

Introduction:

Apache Airflow and MongoDB are two popular technologies in the field of data management and processing. While Airflow is a platform to programmatically author, schedule, and monitor workflows, MongoDB is a NoSQL database that provides high performance, high availability, and easy scalability.

Data Structure: One key difference between Airflow and MongoDB is their data structure. Airflow primarily deals with DAGs (Directed Acyclic Graphs) to define workflows and dependencies between tasks, whereas MongoDB stores data in collections and documents in a flexible JSON-like format without a predefined schema. This fundamental difference affects how data is stored and accessed.
Query Language: Another significant difference is the query language used by Airflow and MongoDB. Airflow uses Python scripts to define and execute tasks within workflows, making it more programmatically flexible. On the other hand, MongoDB uses MongoDB Query Language (MQL) for querying data, which is based on JSON-like syntax and provides powerful querying capabilities specific to document-based databases.
Data Processing: When it comes to data processing capabilities, Airflow focuses on orchestrating workflows, scheduling tasks, and monitoring processes within a pipeline. In contrast, MongoDB offers advanced aggregation features, MapReduce functionality, and indexing options for efficient data processing and analysis directly within the database itself.
Scalability: Scalability is another key area where Airflow and MongoDB differ. Airflow is more focused on managing workflows and task dependencies in distributed environments, ensuring scalable and efficient execution of workflows across multiple nodes. MongoDB, on the other hand, is designed to scale horizontally by sharding data across multiple nodes to handle large volumes of data and high traffic loads.
Community and Ecosystem: The community support and ecosystem around Airflow and MongoDB also vary. Airflow has a vibrant community of developers and contributors actively enhancing the platform with new features, integrations, and extensions. MongoDB, on the other hand, boasts a strong ecosystem of tools, libraries, and cloud services that complement its database platform and enhance its usability in various applications.
Use Cases: While Airflow is commonly used for workflow automation, data pipeline orchestration, and ETL processes in data engineering and analytics workflows, MongoDB is preferred for real-time analytics, content management, Internet of Things (IoT) applications, and other use cases that require flexible data modeling and scalable performance.

In Summary, Apache Airflow and MongoDB differ in data structure, query language, data processing capabilities, scalability, community support, and use cases, catering to distinct needs in data management and processing.

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Advice on MongoDB, Airflow

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

MongoDB	Airflow
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.	Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 1.7K
Followers 82.0K	Followers 2.8K
Votes 4.1K	Votes 128
Pros & Cons
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	Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 10 Extensibility Cons 2 Observability is not great when the DAGs exceed 250 2 Running it on kubernetes cluster relatively complex 2 Open source - provides minimum or no support 1 Logical separation of DAGs is not straight forward

What are some alternatives to MongoDB, Airflow?

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

Airflow vs MongoDB: What are the differences?

Introduction:

Data Structure: One key difference between Airflow and MongoDB is their data structure. Airflow primarily deals with DAGs (Directed Acyclic Graphs) to define workflows and dependencies between tasks, whereas MongoDB stores data in collections and documents in a flexible JSON-like format without a predefined schema. This fundamental difference affects how data is stored and accessed.
Query Language: Another significant difference is the query language used by Airflow and MongoDB. Airflow uses Python scripts to define and execute tasks within workflows, making it more programmatically flexible. On the other hand, MongoDB uses MongoDB Query Language (MQL) for querying data, which is based on JSON-like syntax and provides powerful querying capabilities specific to document-based databases.
Data Processing: When it comes to data processing capabilities, Airflow focuses on orchestrating workflows, scheduling tasks, and monitoring processes within a pipeline. In contrast, MongoDB offers advanced aggregation features, MapReduce functionality, and indexing options for efficient data processing and analysis directly within the database itself.
Scalability: Scalability is another key area where Airflow and MongoDB differ. Airflow is more focused on managing workflows and task dependencies in distributed environments, ensuring scalable and efficient execution of workflows across multiple nodes. MongoDB, on the other hand, is designed to scale horizontally by sharding data across multiple nodes to handle large volumes of data and high traffic loads.
Community and Ecosystem: The community support and ecosystem around Airflow and MongoDB also vary. Airflow has a vibrant community of developers and contributors actively enhancing the platform with new features, integrations, and extensions. MongoDB, on the other hand, boasts a strong ecosystem of tools, libraries, and cloud services that complement its database platform and enhance its usability in various applications.
Use Cases: While Airflow is commonly used for workflow automation, data pipeline orchestration, and ETL processes in data engineering and analytics workflows, MongoDB is preferred for real-time analytics, content management, Internet of Things (IoT) applications, and other use cases that require flexible data modeling and scalable performance.

Airflow vs MongoDB

Overview