Cloud Bigtable

Context:
- It can (typically) handle massive amounts of data across thousands of commodity servers.
- It can (often) be used for applications where read and write speeds are critical, such as financial analytics and IoT.
- It can range from being used for personal projects to enterprise-level applications.
- It can integrate with other Google Cloud services for analytics and machine learning projects.
- It can support multiple programming languages, including C++, Java (Programming Language), Python (Programming Language), Go (Programming Language), and Ruby (Programming Language).
- ...
Example(s):
- Bigtable, 2024.
- ...
Counter-Example(s):
- AWS DynamoDB, which is Amazon's NoSQL database service.
- Azure Cosmos DB, which is Microsoft's globally distributed, multi-model database service.
- AlloyDB, which is not a wide-column store but a PostgreSQL-compatible database service by Google.
See: Google Cloud, Google, Google Cloud Platform, C++, Java (Programming Language), Python (Programming Language), Go (Programming Language), Ruby (Programming Language), Proprietary Software, Wide-Column Store, Key-Value Store, NoSQL.

References

(Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Bigtable Retrieved:2024-4-11.
- Bigtable is a fully managed wide-column and key-value NoSQL database service for large analytical and operational workloads as part of the Google Cloud portfolio.

http://en.wikipedia.org/wiki/BigTable
- QUOTE: BigTable is a compressed, high performance, and proprietary database system built on Google File System, Chubby Lock Service, SSTable and a few other Google technologies. It is not distributed outside Google, although Google offers access to it as part of its Google App Engine.
  … BigTable maps two arbitrary string values (row key and column key) and timestamp (hence three dimensional mapping) into an associated arbitrary byte array. It is not a relational database and can be better defined as a sparse, distributed multi-dimensional sorted map. BigTable is designed to scale into the petabyte range across "hundreds or thousands of machines, and to make it easy to add more machines [to] the system and automatically start taking advantage of those resources without any reconfiguration".^[1]

(Chang et al., 2006) ⇒ Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. (2006). “Bigtable: A Distributed Storage System for Structured Data.” In: Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation.
- QUOTE: Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. … Bigtable is designed to reliably scale to petabytes of data and thousands of machines. Bigtable has achieved several goals: wide applicability, scalability, high performance, and high availability. Bigtable is used by more than sixty Google products and projects, including Google Analytics, Google Finance, Orkut, Personalized Search, Writely, and Google Earth. These products use Bigtable for a variety of demanding workloads, which range from throughput-oriented batch-processing jobs to latency-sensitive serving of data to end users. The Bigtable clusters used by these products span a wide range of configurations, from a handful to thousands of servers, and store up to several hundred terabytes of data.