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Amazon DynamoDB vs Google Cloud Bigtable: What are the differences?
Introduction
In this article, we will discuss the key differences between Amazon DynamoDB and Google Cloud Bigtable. Both DynamoDB and Cloud Bigtable are NoSQL database services offered by Amazon Web Services (AWS) and Google Cloud Platform (GCP), respectively. While they are similar in some aspects, they differ significantly in terms of features, scalability, pricing, and integration capabilities.
Scalability: DynamoDB is designed to scale horizontally with built-in automatic partitioning and load balancing. It can seamlessly handle massive workloads and easily scale up or down based on demand. On the other hand, Cloud Bigtable also offers horizontal scalability, but it requires manual sharding and configuration adjustments for handling larger datasets and high traffic.
Data Consistency: DynamoDB guarantees eventual consistency by default and provides strong consistency as an option, allowing developers to choose the level of consistency required for their applications. Cloud Bigtable, on the other hand, only provides eventual consistency, which means there may be a delay before changes to data are propagated across all replicas.
Data Model: DynamoDB uses a key-value data model with support for complex data types such as lists, maps, and sets. It also provides secondary indexes for efficient querying. Cloud Bigtable, however, is a wide column-store database that organizes data by row keys. It does not support secondary indexes but offers column families for efficient storage and retrieval of large amounts of data.
Querying and Indexing: DynamoDB supports rich querying capabilities using its Query API, which allows retrieval of data based on attributes other than the primary key. It also provides Global Secondary Indexes for efficient querying across different attribute values. Cloud Bigtable, on the other hand, requires designing the schema based on the anticipated query patterns as it does not offer advanced indexing and querying capabilities like DynamoDB.
Integration: DynamoDB integrates seamlessly with various AWS services, such as AWS Lambda, AWS AppSync, and Amazon Redshift, enabling developers to build scalable and fully managed applications. Cloud Bigtable integrates well with other Google Cloud services like BigQuery, Dataflow, and Dataproc, enabling data processing and analysis in a serverless environment.
Pricing: DynamoDB pricing is based on provisioned throughput and storage consumption, and customers pay for the resources they provision. It offers different pricing models with on-demand and provisioned capacity options. Cloud Bigtable pricing is based on the number and size of nodes in the cluster, with separate charges for storage consumption. It offers sustained usage discounts for long-running workloads.
In summary, Amazon DynamoDB and Google Cloud Bigtable differ in terms of scalability, data consistency, data model, querying capabilities, integration options, and pricing models. Choosing the right database service depends on specific use cases, requirements, and the existing tech stack.
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?
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.
Pros of Amazon DynamoDB
- Predictable performance and cost62
- Scalable56
- Native JSON Support35
- AWS Free Tier21
- Fast7
- No sql3
- To store data3
- Serverless2
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
Pros of Google Cloud Bigtable
- High performance11
- Fully managed9
- High scalability5
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of Google Cloud Bigtable
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What is Amazon DynamoDB?
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