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Amazon DocumentDB vs Google Cloud Datastore: What are the differences?
# Introduction
This Markdown code provides key differences between Amazon DocumentDB and Google Cloud Datastore.
1. **Data Model**: Amazon DocumentDB uses a JSON-like document model, allowing for flexible schemas and nested data structures. In contrast, Google Cloud Datastore uses a NoSQL database model with entities and properties, providing a more structured approach to data storage.
2. **Scaling**: Amazon DocumentDB offers horizontal scaling through sharding, automatic data distribution, and multi-AZ deployments for high availability. On the other hand, Google Cloud Datastore automatically scales to meet user demands without the need for manual intervention, making it more user-friendly for developers.
3. **Indexing**: Amazon DocumentDB supports global secondary indexes that allow for efficient querying on non-primary keys, enhancing query performance. Google Cloud Datastore also supports indexing but requires manual configuration for composite indexes, impacting overall query speed and efficiency.
4. **Pricing**: Amazon DocumentDB pricing is based on instance sizes and storage usage, along with additional charges for data transfer and backups. In contrast, Google Cloud Datastore pricing is calculated based on operations, storage, and network usage, offering a more pay-as-you-go pricing model for cost efficiency.
5. **Management**: Amazon DocumentDB provides features like automated backups, scaling, and monitoring through integration with AWS services, simplifying database management tasks. Google Cloud Datastore offers seamless integration with other Google Cloud services, making it easier for developers to build and deploy applications within the Google Cloud ecosystem.
6. **Consistency**: Amazon DocumentDB offers strong consistency with configurable read preferences, ensuring that data is up-to-date across different replica instances. Google Cloud Datastore provides eventual consistency by default, allowing for faster performance at the cost of potential data staleness in distributed environments.
In Summary, the key differences between Amazon DocumentDB and Google Cloud Datastore lie in their data models, scaling methods, indexing capabilities, pricing structures, management features, and consistency models.
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Learn MorePros of Amazon DocumentDB
Pros of Google Cloud Datastore
Pros of Amazon DocumentDB
- Storage elasticity0
- Scalable0
- Easy Setup0
Pros of Google Cloud Datastore
- High scalability7
- Serverless2
- Ability to query any property2
- Pay for what you use1
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What is 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.
What is 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.
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What companies use Amazon DocumentDB?
What companies use Google Cloud Datastore?
What companies use Google Cloud Datastore?
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What tools integrate with Amazon DocumentDB?
What tools integrate with Google Cloud Datastore?
What tools integrate with Amazon DocumentDB?
What are some alternatives to Amazon DocumentDB and Google Cloud Datastore?
MongoDB
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.
MongoDB Atlas
MongoDB Atlas is a global cloud database service built and run by the team behind MongoDB. Enjoy the flexibility and scalability of a document database, with the ease and automation of a fully managed service on your preferred cloud.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Atlas
Atlas is one foundation to manage and provide visibility to your servers, containers, VMs, configuration management, service discovery, and additional operations services.
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.