AWS DynamoDB vs Google Cloud Datastore: Top Differences

In the ever-evolving landscape of application development, choosing the right database system is very crucial. Modern applications prefer NoSQL databases because they are flexible and scalable. These include AWS DynamoDB and Google Cloud Datastore as major contenders.

But which one suits your project most? This article explains the fundamental differences between DynamoDB and Cloud Datastore so that you can be able to make a good choice.

What is DynamoDB?

DynamoDB is a high-performance storage system for web-sized distributed data with elastic scaling using Amazon’s infrastructure. Users do not have to care about hardware setup once they decide to use it. The database can grow or shrink its resources according to the load on the server, so it delivers predictable performance even when there are millions of operations per second in progress at any time point. In addition, data can be loaded very quickly from this repository whenever reading or writing requests are made, hence it is perfectly used by systems that demand dynamic information manipulation with great efficiency.

What is Cloud Datastore?

Cloud Datastore, which is a part of Google Cloud Platform (GCP), is an incredibly scalable NoSQL document database service. It simplifies data management for software developers with automatic scaling and flexible data modeling. Consequently, through this high consistency, Cloud Datastore maintains the integrity of data across distributed systems. This feature allows it to process huge volumes of information and support its workload as well as scalability for various projects from small ones to corporate.

AWS DynamoDB vs Google Cloud Datastore: Top Differences

Let’s deeply understand the critical aspects of AWS DynamoDB vs Google Cloud Datastore:

1. Data Model

DynamoDB

It employs a key-value data model. Each item is uniquely identified by a primary key, which can be a simple string or a hash/range combination. Additionally, secondary indexes can be created on non-key attributes for faster retrieval. However, these secondary indexes come with limitations. They can only be created on a single attribute or a combination of two attributes from the same item. Complex queries involving multiple attributes or joins can be challenging and impact performance.

Cloud Datastore

Cloud Datastore Leverages a document data model. Documents are stored in JSON format, allowing for flexible schema design. This facilitates representation of complex relationships between points of data since documents may contain nested structures where necessary. Another aspect supported by Cloud Datastore is entities which allow organizing related documents into logical groups.

2. Consistency

DynamoDB

It offers tunable consistency options. You can choose between:

• Eventually Consistent Reads (default): Faster but may return outdated data, suitable for scenarios where the most recent data isn’t critical.
• Strongly Consistent Reads: Slower but guaranteed to return the latest data, ideal for applications requiring real-time data accuracy.

Cloud Datastore

It provides strong consistency by default. All reads are guaranteed to return the latest data, ensuring consistency across all replicas. This consistency model offers high data integrity but can impact performance for high-traffic applications encountering frequent writes. Cloud Datastore also offers an option for eventual consistency reads, but it’s rarely used due to its limitations compared to strongly consistent reads.

3. Scalability

DynamoDB

DynamoDB is generally highly scalable. Its storage capacity and throughput automatically scale based on application needs. You can provision read capacity units (RCUs) and write capacity units (WCUs). The number of read operations per second your table can handle is represented by RCUs while WCUs represent the number of write operations per second respectively. To avoid under- or over-provisioning, you need to monitor the system carefully since provisioning provides predictable performance.

Cloud Datastore

It automatically scales storage and throughput based on usage. Unlike DynamoDB, there is no manual configuration needed for scaling units in Cloud Datastore. In order to be cost-effective when handling unpredictable workloads, pricing model for operations used in cloud datastore is pay-per-use type. However, this lack of fine-grained control over scaling can make it challenging to achieve the same level of predictable performance as DynamoDB.

4. Pricing

DynamoDB

It employs a pay-per-use pricing model based on:

• Consumed read and write capacity units (RCUs and WCUs)
• Storage used

This model can be cost-effective for applications with predictable workloads but can become expensive for applications experiencing bursts of traffic or requiring high write volumes.

Cloud Datastore

It offers a tiered pricing structure. You pay for:

• Storage used
• Operations performed on the data (reads, writes, deletes)

Cloud Datastore provides a free tier with limited storage and operations, suitable for small projects or initial development phases. Paid tiers offer increased storage and operation quotas with a pay-as-you-go structure. This can be attractive for budget-conscious projects in the initial stages or applications with unpredictable workloads.

5. Security

DynamoDB

Dynamo DB provides robust security features, including:

• Encryption at rest and in transit: Data is encrypted both when stored on disk and during transmission between your application and DynamoDB.
• IAM (Identity and Access Management): You determine who accesses the tables of your DynamoDB and what actions they can do with them.
• Granular permissions for data operations: This allows fine-grained permissions to be established for users or groups to perform read, write or delete operations on specific tables or attributes.

Cloud Datastore

Cloud Datastore includes a user authentication as well as authorization through Google Cloud IAM along with encryption at rest during it’s transit. Like Dynamo DB, Cloud Datastore enables one to define roles for different users and groups to control access to data and functions.

6. Query Capabilities

DynamoDB

DynamoDB Supports querying data using:

• Primary Key: Fastest and most efficient way to retrieve data in DynamoDB.
• Secondary Indexes: Can be created on non-key attributes to enable faster retrieval based on those attributes. However, these indexes have limitations on complexity and can impact performance for intricate queries.
• Expression-based queries: Allow for more complex filtering and sorting of data, but can be challenging to write and optimize for performance.

Cloud Datastore

Cloud Datastore offers a more robust query language called GQL (Google Cloud Query Language). GQL allows for querying documents based on various criteria, including:

• Filtering data based on property values and comparisons
• Performing logical operations (AND, OR, NOT)
• Sorting results based on specific properties
• Projecting only specific fields from documents
• Text search capabilities for searching within textual data stored in documents

While Cloud Datastore offers a more powerful query language, it’s essential to consider the trade-off. Complex GQL queries can be computationally expensive and impact performance. For simple queries, DynamoDB’s primary key and secondary indexes might be sufficient and more performant.

7. Integrations

DynamoDB

DynamoDB integrates seamlessly with other AWS services like:

• Lambda: Lambda is a service which provides an ability of running code within response to events in Dynamo DB such as putting, deleting, updating etc., .
• S3 (Simple Storage Service): S3 can be used for storing large files or backups of your DynamoDB data.
• Kinesis: Kinesis is stream based service that captures data streams from Dynamo DB to process it in real-time.

Cloud Datastore

Cloud Datastore works hand-in-glove with other GCP services like:

• App Engine: App Engine is a platform as a service environment that allows developers create web applications right from the cloud.
• Cloud Functions: Cloud Functions is a serverless compute service like AWS Lambda that can be triggered by events in Cloud Datastore.
• BigQuery: BigQuery is a data warehouse for big data analytics. For more analysis, Cloud Datastore can easily export its data to BigQuery.

Choosing Factor

There’s no single “winner” between DynamoDB and Cloud Datastore. The optimal choice hinges on your specific project requirements and priorities. Here’s a breakdown to guide your decision:

1. Prioritize Performance and Scalability

If your application demands high throughput, low latency, and predictable performance under varying loads, DynamoDB is a strong contender. Its key-value model and granular capacity unit control provide a solid foundation for high-performance applications.

2. Flexibility and Evolving Data Models

If your application’s data structure is likely to change frequently, or if you require flexible schema design, Cloud Datastore’s document model offers greater adaptability. Its automatic scaling and strong consistency by default can also be advantageous.

3. Cost Considerations

Cloud Datastore’s free tier and tiered pricing structure are attractive to frugal projects particularly in the early phase. Nevertheless, DynamoDB’s pay-per-use model may save money for applications with fixed usage patterns.

4. Existing Cloud Infrastructure

Take your existing cloud infrastructure into account. In case you have heavily invested in Amazon Web Services (AWS), using DynamoDB for easy integration with other AWS services will simplify your development process. On the other hand, Cloud Datastore is suitable for projects that are already using Google Cloud Platform services.

5. Development Expertise and Support

Your development team’s experience and comfort level matters. If your team is quite knowledgeable about AWS and associated services such as DynanoDB, this could become an obvious choice. However, developers accustomed to GCP might prefer working with Cloud Datastore.

AWS DynamoDB vs Google Cloud Datastore: Difference Table

Aspects	DynamoDB	Cloud Datastore
Data Model	Key-value	Document
Consistency	Tunable (Eventual or Strong)	Strong (by default, eventual consistency also available)
Scalability	Manual scaling with RCUs and WCUs	Automatic scaling
Pricing	Pay-per-use (RCUs, WCUs, Storage)	Tiered pricing (Storage, Operations)
Security	Encryption at rest and in transit, IAM	Encryption at rest and in transit, IAM
Query Capabilities	Primary Key, Secondary Indexes, Expression-based queries	GQL (Google Cloud Query Language)
Integrations	Lambda, S3, Kinesis	App Engine, Cloud Functions, BigQuery
Choosing Factor	Performance, predictable scalability	Flexible schema, strong consistency, cost-effective for unpredictable workloads

Conclusion

Choosing the right database depends on your integration needs and requires a careful assessment of your project’s needs. If you’re heavily invested in the AWS ecosystem and rely on services like Lambda and S3, DynamoDB offers a more streamlined integration experience. Conversely, Cloud Datastore aligns well with projects already using Google Cloud Platform services, particularly for data analysis with BigQuery. Typically by understanding their core differences, strengths, and weaknesses, you can make an informed decision that empowers your application’s success and also can make a good decision while choosing between AWS DynamoDB and Google Cloud Datastore.