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

Introduction:

Google Cloud Bigtable and Google Cloud Datastore are both NoSQL databases offered by Google Cloud Platform. While they share some similarities, there are key differences between the two that make them suitable for different use cases.

1. Scalability and Performance:

Google Cloud Bigtable is a distributed, highly scalable database built to handle massive workloads. It can scale horizontally to handle petabytes of data and can provide high throughput and low-latency access. On the other hand, Google Cloud Datastore is designed for smaller workloads and may not provide the same level of scalability and performance as Bigtable.

2. Data Model:

Google Cloud Bigtable is a wide column store database, where data is organized into column families. It supports a key-value data model, where each row can have multiple columns and column families can have multiple versions. On the contrary, Google Cloud Datastore is a document-oriented database, where data is stored as entities with properties. It provides a hierarchical key-value data model, allowing for complex data structures and relationships between entities.

3. Strong Consistency vs. Eventual Consistency:

In Bigtable, reads and writes are eventually consistent, meaning that there might be a delay before changes are visible. Datastore, on the other hand, provides strong consistency, ensuring that immediately after a write, the changes are visible during subsequent reads. This difference in consistency models affects the way applications handle concurrent updates and guarantees data integrity.

4. Queries and Indexing:

Bigtable does not provide traditional querying capabilities. Instead, it supports low-level operations like single row lookups and key range scans. It also allows for secondary indexes for efficient data retrieval. In contrast, Datastore offers a powerful query API with support for filtering, sorting, and aggregating data. It automatically builds indexes for properties used in queries, making it easier to search and retrieve data.

5. Cost and Pricing Model:

Bigtable is billed based on usage, including the amount of storage and data operations performed. It is designed for heavy workloads and may come with higher costs compared to Datastore. Datastore, on the other hand, provides a budget-friendly pricing model suitable for smaller applications or prototypes. It offers a generous free tier and uses a combination of storage and operations to calculate costs.

6. Managed vs. Non-Managed Service:

Google Cloud Bigtable is a managed service, meaning that Google takes care of the underlying infrastructure and handles tasks like maintenance, backups, and security patches. In contrast, Google Cloud Datastore can be deployed either as a managed service or as a self-managed service using Datastore mode in Firestore. This gives developers more control over the deployment and management of the database.

In Summary, Google Cloud Bigtable is a highly scalable database with a wide column store and eventual consistency, providing high performance for massive workloads. On the other hand, Google Cloud Datastore is a document-oriented database with a strong consistency model and a powerful query API, suitable for smaller workloads and applications requiring complex data structures.