Google BigQuery vs MongoDB

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Google BigQuery vs MongoDB: What are the differences?

Google BigQuery and MongoDB are both popular databases used for different purposes. Let's explore the key difference between them.

Data Structure: One of the significant differences between Google BigQuery and MongoDB is their data structure. BigQuery follows a structured data model and primarily supports the storage of structured data in tables with columns and rows, similar to a traditional relational database. On the other hand, MongoDB is a document-oriented database that stores data in flexible, JSON-like documents, making it suitable for handling unstructured or semi-structured data.
Data Distribution: Google BigQuery and MongoDB differ in their approach to data distribution across multiple machines. BigQuery utilizes a distributed architecture, with data automatically partitioned and replicated across various nodes for improved scalability and reliability. MongoDB, on the other hand, supports horizontal scaling through sharding, which involves splitting data across multiple shards or servers for increased performance and storage capabilities.
Querying and Analytics: Another key difference lies in the querying and analytics capabilities of Google BigQuery and MongoDB. BigQuery is optimized for running complex analytical queries on large datasets and provides powerful features like nested queries, window functions, and support for standard SQL. MongoDB, on the other hand, offers a flexible query language with rich filtering and document querying capabilities, making it suitable for real-time applications and agile development.
Data Consistency: BigQuery and MongoDB also differ in their approaches to data consistency. BigQuery guarantees strong consistency by default, ensuring that queries always see the most recent data. MongoDB, being a NoSQL database, provides eventual consistency by default, meaning that data changes may take some time to propagate to all replicas or shards. However, MongoDB does offer options for enforcing stronger consistency if needed.
Availability and Scalability: In terms of availability and scalability, BigQuery and MongoDB have different mechanisms. BigQuery is a fully-managed service provided by Google Cloud Platform, which means that it automatically scales resources based on demand and provides high availability and fault tolerance out of the box. MongoDB, on the other hand, can be self-managed or deployed on cloud providers, offering more flexibility in terms of scalability and customization but requiring manual configuration and management for high availability.
Data Storage Costs: Lastly, there is a distinction in the pricing models and data storage costs between Google BigQuery and MongoDB. BigQuery operates on a pay-as-you-go pricing model, where users are billed based on the amount of data processed in queries and storage used. MongoDB typically relies on per-instance pricing or a subscription model when used as a managed service, with costs varying based on factors like instance size, storage capacity, and additional features.

In summary, BigQuery is a fully-managed, serverless data warehousing solution provided by Google Cloud Platform, while MongoDB is a NoSQL database that focuses on scalability and flexibility.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Google BigQuery, 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

Google BigQuery	MongoDB
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.	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.
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	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 1.8K	Stacks 96.6K
Followers 1.5K	Followers 82.0K
Votes 152	Votes 4.1K
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	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
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	No integrations available

What are some alternatives to Google BigQuery, 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

Bootstrap vs Materialize

Django vs Laravel vs Node.js

Bootstrap vs Foundation vs Material UI

Node.js vs Spring-Boot

Flyway vs Liquibase

Google BigQuery vs MongoDB: What are the differences?

Google BigQuery and MongoDB are both popular databases used for different purposes. Let's explore the key difference between them.

Data Structure: One of the significant differences between Google BigQuery and MongoDB is their data structure. BigQuery follows a structured data model and primarily supports the storage of structured data in tables with columns and rows, similar to a traditional relational database. On the other hand, MongoDB is a document-oriented database that stores data in flexible, JSON-like documents, making it suitable for handling unstructured or semi-structured data.
Data Distribution: Google BigQuery and MongoDB differ in their approach to data distribution across multiple machines. BigQuery utilizes a distributed architecture, with data automatically partitioned and replicated across various nodes for improved scalability and reliability. MongoDB, on the other hand, supports horizontal scaling through sharding, which involves splitting data across multiple shards or servers for increased performance and storage capabilities.
Querying and Analytics: Another key difference lies in the querying and analytics capabilities of Google BigQuery and MongoDB. BigQuery is optimized for running complex analytical queries on large datasets and provides powerful features like nested queries, window functions, and support for standard SQL. MongoDB, on the other hand, offers a flexible query language with rich filtering and document querying capabilities, making it suitable for real-time applications and agile development.
Data Consistency: BigQuery and MongoDB also differ in their approaches to data consistency. BigQuery guarantees strong consistency by default, ensuring that queries always see the most recent data. MongoDB, being a NoSQL database, provides eventual consistency by default, meaning that data changes may take some time to propagate to all replicas or shards. However, MongoDB does offer options for enforcing stronger consistency if needed.
Availability and Scalability: In terms of availability and scalability, BigQuery and MongoDB have different mechanisms. BigQuery is a fully-managed service provided by Google Cloud Platform, which means that it automatically scales resources based on demand and provides high availability and fault tolerance out of the box. MongoDB, on the other hand, can be self-managed or deployed on cloud providers, offering more flexibility in terms of scalability and customization but requiring manual configuration and management for high availability.
Data Storage Costs: Lastly, there is a distinction in the pricing models and data storage costs between Google BigQuery and MongoDB. BigQuery operates on a pay-as-you-go pricing model, where users are billed based on the amount of data processed in queries and storage used. MongoDB typically relies on per-instance pricing or a subscription model when used as a managed service, with costs varying based on factors like instance size, storage capacity, and additional features.

In summary, BigQuery is a fully-managed, serverless data warehousing solution provided by Google Cloud Platform, while MongoDB is a NoSQL database that focuses on scalability and flexibility.