Cloud Firestore vs Google Cloud Bigtable

Overview

Google Cloud Bigtable

Stacks173

Followers363

Votes25

Cloud Firestore

Stacks751

Followers900

Votes112

Google Cloud Bigtable vs Cloud Firestore: What are the differences?

Cloud Firestore and Google Cloud Bigtable are database services offered by Google Cloud. Let's explore the key differences between Cloud Firestore and Google Cloud Bigtable in more detail:

Data Model: Cloud Firestore is a NoSQL document database that stores data in collections of documents. Each document contains key-value pairs and can contain nested structures. It offers flexible querying and indexing capabilities, making it suitable for complex data models and hierarchical data. On the other hand, Google Cloud Bigtable is a wide-column NoSQL database that organizes data in rows and columns. It is designed for handling massive amounts of data with high read and write throughput, making it ideal for large-scale, high-performance workloads.
Scalability: Cloud Firestore is horizontally scalable and can automatically scale to handle increasing workloads. It offers seamless scaling as the data and user demand grow, ensuring consistent performance. Google Cloud Bigtable, on the other hand, is optimized for massive scalability and can handle petabytes of data. It leverages Google's distributed infrastructure to provide high throughput and low-latency access to data, making it suitable for applications with extreme scalability requirements.
Data Consistency: Cloud Firestore provides strong consistency by default, ensuring that the data read reflects the latest updates. It also offers eventual consistency for specific cases where strong consistency is not required, providing developers with flexibility. Google Cloud Bigtable, being a distributed system, offers eventual consistency, where data changes may take some time to propagate across the system. It prioritizes high throughput and low latency over strong consistency, making it suitable for use cases where real-time data access is more critical than immediate consistency.
Data Analytics: Cloud Firestore is designed for real-time applications and provides real-time synchronization of data across clients. It offers built-in integration with other Firebase services, making it well-suited for mobile and web applications that require real-time updates. Google Cloud Bigtable, on the other hand, is optimized for handling massive amounts of structured data and provides efficient storage and retrieval of data at scale. It is often used in conjunction with other data processing tools and frameworks, such as Apache Hadoop and Apache Spark, for large-scale analytics and batch processing.
Pricing Model: Cloud Firestore pricing is based on document reads, writes, and storage. It offers a free tier with certain limits and provides pricing based on usage beyond those limits. Google Cloud Bigtable pricing is based on the capacity used, including storage and operations like reads and writes. It offers different pricing tiers based on the desired throughput and storage requirements.

In summary, Cloud Firestore and Google Cloud Bigtable are database services offered by Google Cloud. Cloud Firestore is a flexible NoSQL document database suitable for real-time applications with complex data models, while Google Cloud Bigtable is a wide-column NoSQL database designed for massive scalability and high-performance workloads.

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Detailed Comparison

Google Cloud Bigtable	Cloud Firestore
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.	Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.
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.	Documents and collections with powerful querying;iOS, Android, and Web SDKs with offline data access;Real-time data synchronization;Automatic, multi-region data replication with strong consistency;Node, Python, Go, and Java server SDKs
Statistics
Stacks 173	Stacks 751
Followers 363	Followers 900
Votes 25	Votes 112
Pros & Cons
Pros 11 High performance 9 Fully managed 5 High scalability	Pros 15 Cloud Storage 15 Easy to use 12 Easy setup 12 Realtime Database 9 Super fast Cons 8 Doesn't support FullTextSearch natively
Integrations
Heroic Hadoop Apache Spark	Golang Node.js Java Python Firebase Cloud Functions for Firebase Google Cloud Functions

What are some alternatives to Google Cloud Bigtable, Cloud Firestore?

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.

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Google Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

CloudBoost

CloudBoost.io is a database service for the “next web” - that not only does data-storage, but also search, real-time and a whole lot more which enables developers to build much richer apps with 50% less time saving them a ton of cost and helping them go to market much faster.

Firebase Realtime Database

It is a cloud-hosted NoSQL database that lets you store and sync data between your users in realtime. Data is synced across all clients in realtime, and remains available when your app goes offline.

restdb.io

RestDB is a NoSql document oriented database cloud service. Data is accessed as JSON objects via HTTPS. This gives great flexibility, easy system integration and future compatibility.

Amazon DocumentDB

Amazon DocumentDB is a non-relational database service designed from the ground-up to give you the performance, scalability, and availability you need when operating mission-critical MongoDB workloads at scale. In Amazon DocumentDB, the storage and compute are decoupled, allowing each to scale independently, and you can increase the read capacity to millions of requests per second by adding up to 15 low latency read replicas in minutes, regardless of the size of your data.

Amazon SimpleDB

Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.

Datomic Cloud

A transactional database with a flexible data model, elastic scaling, and rich queries. Datomic is designed from the ground up to run on AWS. Datomic leverages AWS technology, including DynamoDB, S3, EFS, and CloudFormation to provide a fully integrated solution.

Related Comparisons

Data Model: Cloud Firestore is a NoSQL document database that stores data in collections of documents. Each document contains key-value pairs and can contain nested structures. It offers flexible querying and indexing capabilities, making it suitable for complex data models and hierarchical data. On the other hand, Google Cloud Bigtable is a wide-column NoSQL database that organizes data in rows and columns. It is designed for handling massive amounts of data with high read and write throughput, making it ideal for large-scale, high-performance workloads.
Scalability: Cloud Firestore is horizontally scalable and can automatically scale to handle increasing workloads. It offers seamless scaling as the data and user demand grow, ensuring consistent performance. Google Cloud Bigtable, on the other hand, is optimized for massive scalability and can handle petabytes of data. It leverages Google's distributed infrastructure to provide high throughput and low-latency access to data, making it suitable for applications with extreme scalability requirements.
Data Consistency: Cloud Firestore provides strong consistency by default, ensuring that the data read reflects the latest updates. It also offers eventual consistency for specific cases where strong consistency is not required, providing developers with flexibility. Google Cloud Bigtable, being a distributed system, offers eventual consistency, where data changes may take some time to propagate across the system. It prioritizes high throughput and low latency over strong consistency, making it suitable for use cases where real-time data access is more critical than immediate consistency.
Data Analytics: Cloud Firestore is designed for real-time applications and provides real-time synchronization of data across clients. It offers built-in integration with other Firebase services, making it well-suited for mobile and web applications that require real-time updates. Google Cloud Bigtable, on the other hand, is optimized for handling massive amounts of structured data and provides efficient storage and retrieval of data at scale. It is often used in conjunction with other data processing tools and frameworks, such as Apache Hadoop and Apache Spark, for large-scale analytics and batch processing.
Pricing Model: Cloud Firestore pricing is based on document reads, writes, and storage. It offers a free tier with certain limits and provides pricing based on usage beyond those limits. Google Cloud Bigtable pricing is based on the capacity used, including storage and operations like reads and writes. It offers different pricing tiers based on the desired throughput and storage requirements.

Cloud Firestore vs Google Cloud Bigtable