Amazon DynamoDB vs Google Cloud Datastore

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Amazon DynamoDB vs Google Cloud Datastore: What are the differences?

Introduction

Amazon DynamoDB and Google Cloud Datastore are both NoSQL databases that offer scalability, flexibility, and high-performance storage solutions. However, there are key differences between the two that make each service unique. In this article, we will explore these differences in detail.

  1. Data Model: One of the key differences between Amazon DynamoDB and Google Cloud Datastore is their underlying data model. DynamoDB uses a key-value store model, where each item is uniquely identified by its primary key. Datastore, on the other hand, uses an entity model, similar to a relational database, where data is structured into entities with properties and relationships.

  2. Consistency Model: DynamoDB provides eventual consistency by default, meaning that it allows for some delay in the propagation of updates across all replicas. However, it also offers strong consistency as an option. In contrast, Datastore guarantees strong consistency for all reads and writes, ensuring that all replicas are always up to date.

  3. Scalability: DynamoDB is designed to scale horizontally, allowing you to add more read and write capacity as your application needs grow. It automatically handles the distribution of data across multiple servers. Datastore, on the other hand, scales vertically, meaning that you can increase the storage capacity and performance of an individual entity as needed.

  4. Query Capabilities: DynamoDB provides a flexible query language called DynamoDB Query, which allows you to retrieve data based on conditions and filters. Datastore, on the other hand, offers a powerful query language called GQL (Google Query Language) that supports complex queries with filtering, sorting, and projection.

  5. Secondary Indexes: DynamoDB supports global secondary indexes, which allow you to create additional indexes on non-key attributes for efficient querying. Datastore, on the other hand, supports automatic indexing on all properties of an entity, making it easier to query data without the need for explicit indexing.

  6. Pricing Structure: The pricing structure of DynamoDB is based on provisioned throughput, storage, and data transfer. You pay for the read and write capacity units that you provision. Datastore, on the other hand, has a pricing structure based on the number of entities, entity reads/writes, and storage usage. You pay for the number of entities you store and the operations you perform on them.

In summary, Amazon DynamoDB and Google Cloud Datastore differ in their data models, consistency models, scalability approaches, query capabilities, secondary index support, and pricing structures. The choice between the two depends on your specific requirements and the nature of your application.

Advice on Amazon DynamoDB and Google Cloud Datastore

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

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Replies (1)
William Frank
Data Science and Engineering at GeistM · | 2 upvotes · 112.8K views
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Hi, Akash,

I wouldn't make this decision without lots more information. Cloud Firestore has a much richer metamodel (document-oriented) than Dynamo (key-value), and Dynamo seems to be particularly restrictive. That is why it is so fast. There are many needs in most applications to get lightning access to the members of a set, one set at a time. Dynamo DB is a great choice. But, social media applications generally need to be able to make long traverses across a graph. While you can make almost any metamodel act like another one, with your own custom layers on top of it, or just by writing a lot more code, it's a long way around to do that with simple key-value sets. It's hard enough to traverse across networks of collections in a document-oriented database. So, if you are moving, I think a graph-oriented database like Amazon Neptune, or, if you might want built-in reasoning, Allegro or Ontotext, would take the least programming, which is where the most cost and bugs can be avoided. Also, managed systems are also less costly in terms of people's time and system errors. It's easier to measure the costs of managed systems, so they are often seen as more costly.

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Decisions about Amazon DynamoDB and Google Cloud Datastore
Eduardo Fernandez
Software Engineer at Parrot Software, Inc. · | 5 upvotes · 12.9K views

CouchDB has proven us to be a reliable multi-master NoSQL JSON database built natively for the web.

We decided to use it over alternatives such as Firebase due topology, costs and frontend architecture.

Thanks to CouchDB we are now a frontend first CRM platform. We are capable of delivering and leveraging our frontend code to build most of our new functionalities directly within the frontend which we enrich through backend sidecars connected to each Parrot and each CouchDB.

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Pros of Amazon DynamoDB
Pros of Google Cloud Datastore
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
  • 3
    No sql
  • 3
    To store data
  • 2
    Serverless
  • 2
    No Stored procedures is GOOD
  • 1
    ORM with DynamoDBMapper
  • 1
    Elastic Scalability using on-demand mode
  • 1
    Elastic Scalability using autoscaling
  • 1
    DynamoDB Stream
  • 7
    High scalability
  • 2
    Serverless
  • 2
    Ability to query any property
  • 1
    Pay for what you use

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Cons of Amazon DynamoDB
Cons of Google Cloud Datastore
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
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    What is 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.

    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 Google Cloud Datastore?
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    What are some alternatives to Amazon DynamoDB 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.
    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.
    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.
    Amazon S3
    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    See all alternatives