Amazon S3 vs Google BigQuery vs SummitDB: What are the differences?

Key Differences between Amazon S3, Google BigQuery, and SummitDB

1. Scalability: Amazon S3 is a highly scalable object storage service that can scale to handle trillions of objects. It provides automatic scaling and simple storage management. On the other hand, Google BigQuery is a fully managed, highly scalable, and serverless data warehouse. It handles the scalability aspect by dividing large datasets into smaller, manageable parts and distributing them across multiple machines. SummitDB, on the other hand, is a lightweight, embedded database that is not designed for the same level of scalability as S3 or BigQuery. It is better suited for smaller-scale applications.

2. Querying and Analytics: Amazon S3 is primarily used as an object storage service and does not offer built-in querying and analytics capabilities. It is mainly used for storing and retrieving data objects. On the other hand, Google BigQuery is a powerful analytics platform that provides SQL-like querying capabilities. It can handle complex analytical queries on large datasets efficiently. SummitDB, being a lightweight embedded database, also provides querying capabilities but is not as feature-rich as BigQuery.

3. Pricing Model: Amazon S3 follows a pay-as-you-go pricing model, where you pay for the storage space and the data transfer. There are different pricing tiers based on the storage class and usage patterns. Google BigQuery, on the other hand, has a separate pricing model based on the amount of data queried and the storage space used. It charges for data storage, streaming inserts, and querying. SummitDB, being an open-source database, is free to use and has no associated costs.

4. Data Replication: Amazon S3 replicates objects across multiple geographically distinct data centers to ensure high durability and availability. It provides strong data consistency for both read and write operations. Google BigQuery also replicates data across multiple locations, ensuring data redundancy and availability. SummitDB, being embedded within an application, does not have built-in data replication capabilities. Data replication needs to be handled by the application itself if required.

5. Data Types and Query Language: Amazon S3 does not enforce any specific data schema and can store any type of data as objects. It does not support complex data types and does not have a query language. On the other hand, Google BigQuery supports structured, semi-structured, and nested data types. It has a powerful query language called BigQuery SQL that allows complex data transformations and aggregations. SummitDB supports a limited set of data types and has its own query language similar to SQL.

6. Real-Time Processing: Amazon S3 is a storage service and does not provide real-time processing capabilities. It focuses on data storage and retrieval. Google BigQuery, on the other hand, supports real-time data streaming and can process streaming data in near real-time with the use of services like Cloud Pub/Sub. SummitDB does not have built-in real-time processing capabilities and does not natively support streaming data.

In Summary, Amazon S3 is a scalable object storage service without built-in querying capabilities, while Google BigQuery is a highly scalable data warehouse with powerful querying and analytics features. SummitDB, on the other hand, is a lightweight embedded database that is not designed for the same level of scalability or querying capabilities as S3 or BigQuery.