Google Cloud Bigtable vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Google Cloud Bigtable

Stacks173

Followers363

Votes25

Google Cloud Bigtable vs MongoDB: What are the differences?

Google Cloud Bigtable and MongoDB are two popular database management systems. Let's explore the key differences between them.

Data Model: Google Cloud Bigtable is a wide-column NoSQL database that is designed to handle large amounts of structured data efficiently. It stores data in tables with a schemaless layout, where each row can have varying column families and columns. In contrast, MongoDB is a document-based NoSQL database that stores data in a flexible and self-descriptive JSON-like format called BSON. It allows for nested data structures and supports a more fluid and dynamic data model.
Scalability and Performance: Google Cloud Bigtable is highly scalable and can handle massive workloads by automatically distributing data across multiple nodes. It is optimized for read-heavy and analytic workloads, making it suitable for applications that require high throughput and low latency. MongoDB also offers horizontal scalability through sharding, allowing data to be distributed across multiple servers. However, it may require more manual configuration and fine-tuning compared to Bigtable.
Data Consistency: Google Cloud Bigtable provides eventual consistency by default, which means that data changes may not be immediately reflected across all replicas. This trade-off allows for high availability and low latency. MongoDB offers both strong consistency (ACID transactions) and eventual consistency, giving developers more options depending on their application's requirements.
Querying and Indexing: Google Cloud Bigtable is primarily optimized for key-based lookups and range scans, with limited support for complex queries. It is not built for ad-hoc querying or aggregations. MongoDB, on the other hand, provides a rich query language and supports complex queries, aggregations, and indexing. It offers more flexibility in querying and can handle diverse data access patterns.
Data Durability: Google Cloud Bigtable provides high durability through advanced data replication techniques. It replicates data across multiple data centers to ensure durability and availability. MongoDB also offers replication and durability features, including configurable write concern and automatic failover, but it may require more manual configuration and monitoring compared to Bigtable.
Integration and Ecosystem: Google Cloud Bigtable is tightly integrated with the Google Cloud Platform (GCP) ecosystem, making it seamless to use with other GCP services like BigQuery, Dataflow, and Dataproc. It provides robust integration with other GCP tools for data analytics and processing. MongoDB has a strong presence in the open-source community and offers a wide range of connectors and integrations with popular frameworks and tools, making it more flexible in terms of deployment options.

In summary, Google Cloud Bigtable is a highly scalable wide-column database optimized for read-heavy and analytic workloads, with a focus on data efficiency and simplicity. MongoDB, on the other hand, is a flexible document-based database that offers rich querying capabilities and a strong open-source ecosystem.

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Advice on MongoDB, Google Cloud Bigtable

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	Google Cloud Bigtable
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.	Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.;Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google’s big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.;Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable’s total cost of ownership is less than half the cost of its direct competition.;Security: Cloud Bigtable is built with a replicated storage strategy, and all data is encrypted both in-flight and at rest.;Simplicity: Creating or reconfiguring a Cloud Bigtable cluster is done through a simple user interface and can be completed in less than 10 seconds. As data is put into Cloud Bigtable the backing storage scales automatically, so there’s no need to do complicated estimates of capacity requirements.;Maturity: Over the past 10+ years, Bigtable has driven Google’s most critical applications. In addition, the HBase API is a industry-standard interface for combined operational and analytical workloads.
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 173
Followers 82.0K	Followers 363
Votes 4.1K	Votes 25
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 11 High performance 9 Fully managed 5 High scalability
Integrations
No integrations available	Heroic Hadoop Apache Spark

What are some alternatives to MongoDB, Google Cloud Bigtable?

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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.

Related Comparisons

Google Cloud Bigtable vs MongoDB: What are the differences?

Google Cloud Bigtable and MongoDB are two popular database management systems. Let's explore the key differences between them.

Data Model: Google Cloud Bigtable is a wide-column NoSQL database that is designed to handle large amounts of structured data efficiently. It stores data in tables with a schemaless layout, where each row can have varying column families and columns. In contrast, MongoDB is a document-based NoSQL database that stores data in a flexible and self-descriptive JSON-like format called BSON. It allows for nested data structures and supports a more fluid and dynamic data model.
Scalability and Performance: Google Cloud Bigtable is highly scalable and can handle massive workloads by automatically distributing data across multiple nodes. It is optimized for read-heavy and analytic workloads, making it suitable for applications that require high throughput and low latency. MongoDB also offers horizontal scalability through sharding, allowing data to be distributed across multiple servers. However, it may require more manual configuration and fine-tuning compared to Bigtable.
Data Consistency: Google Cloud Bigtable provides eventual consistency by default, which means that data changes may not be immediately reflected across all replicas. This trade-off allows for high availability and low latency. MongoDB offers both strong consistency (ACID transactions) and eventual consistency, giving developers more options depending on their application's requirements.
Querying and Indexing: Google Cloud Bigtable is primarily optimized for key-based lookups and range scans, with limited support for complex queries. It is not built for ad-hoc querying or aggregations. MongoDB, on the other hand, provides a rich query language and supports complex queries, aggregations, and indexing. It offers more flexibility in querying and can handle diverse data access patterns.
Data Durability: Google Cloud Bigtable provides high durability through advanced data replication techniques. It replicates data across multiple data centers to ensure durability and availability. MongoDB also offers replication and durability features, including configurable write concern and automatic failover, but it may require more manual configuration and monitoring compared to Bigtable.
Integration and Ecosystem: Google Cloud Bigtable is tightly integrated with the Google Cloud Platform (GCP) ecosystem, making it seamless to use with other GCP services like BigQuery, Dataflow, and Dataproc. It provides robust integration with other GCP tools for data analytics and processing. MongoDB has a strong presence in the open-source community and offers a wide range of connectors and integrations with popular frameworks and tools, making it more flexible in terms of deployment options.

Google Cloud Bigtable vs MongoDB

Overview