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Amazon DynamoDB vs Google BigQuery: What are the differences?
Introduction
In this article, we will compare Amazon DynamoDB and Google BigQuery, two popular data storage and analysis solutions, to understand their key differences and use cases.
Data Structure and Querying Language: Amazon DynamoDB is a NoSQL database that supports key-value and document data models, making it suitable for unstructured and semi-structured data. On the other hand, Google BigQuery is a fully-managed, serverless data warehouse that excels in processing structured data using SQL-like queries.
Scalability and Performance: DynamoDB offers automatic scaling with seamless data distribution, allowing it to handle massive workloads and provide high throughput with low latency. BigQuery also scales automatically and can enable petabyte-scale querying, but its performance may vary and require careful partitioning and optimization for optimal results.
Storage and Cost Model: DynamoDB charges for provisioned capacity and additional features like backups and Global Tables. BigQuery follows a pay-per-query pricing model based on the amount of data processed, with separate charges for storage and queries. DynamoDB's pricing can be advantageous for steady or predictable workloads, while BigQuery's model is more suitable for ad-hoc and analytical workloads.
Data Integration and Ecosystem: DynamoDB integrates tightly with other Amazon Web Services (AWS) services, allowing seamless integration and data ETL workflows. BigQuery has connectors with various Google Cloud Platform tools and external data sources, simplifying data ingestion and analysis from different systems.
Security and Access Control: DynamoDB provides fine-grained access control with AWS Identity and Access Management (IAM) policies, allowing granular control over table and item-level permissions. BigQuery offers similar access control mechanisms but with Google Cloud IAM, enabling secure data sharing and collaboration within organizations.
Data Analytics Capabilities: While DynamoDB provides basic querying capabilities, BigQuery offers more advanced analytics features like window functions, nested and repeated data handling, and machine learning integrations. BigQuery is optimized for complex analytical queries, making it suitable for data exploration, ad-hoc analysis, and business intelligence.
In Summary, Amazon DynamoDB is a NoSQL database with flexible data models, excellent scalability, and deep integration with AWS services. On the other hand, Google BigQuery is a versatile data warehouse that excels in structured data processing, ad-hoc querying, and advanced analytics capabilities.
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.
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution
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 BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0
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