Data Processing 3rd-Party Platform

Context:
- It can range from being a Fault-Tolerant Data Processing Framework to being Fault-Intolerant Data Processing Framework.
- It can range from being a Centralized Data Processing Platform to being a Distributed Data Processing Platform (such as a cluster-based data processing platform).
- It can range from being a Data Batch Processing Platform to being a Data Stream Processing Platform.
- It can range from being an On-Premise Data Processing Platform to being a Cloud-based Data Processing Platform.
- It can be used to create a Data Processing Platform.
- It can be designed around a Data Processing Architecture.
- …
Example(s):
- a Big Data Platform, such as: Apache Spark or a Map/Reduce Platform.
- a Data Stream Processing Framework, such as Apache Spark Streaming.
- a Data Integration Platform, such as an ETL platform.
- a Data Querying Platform, such as a Database Platform (such as AWS Redshift).
- a Data Analytics Platform.
- a Machine Learning Platform.
- …
Counter-Example(s):
- a Compute-Intensive Framework.
- a Computing Platform.
See: DBMS Platform, File System, NLP Platform.

References

http://lintool.github.io/bigdata-2016w/
- QUOTE: Over the past few years, we have seen the emergence of "big data": disruptive technologies that have transformed commerce, science, and many aspects of society. These developments are enabled by infrastructure that allows us to distribute computations across hundreds or even thousands of commodity servers. One important advance that has made all this possible is the development of abstractions for data-intensive computing that allow programmers to reason about computations at a massive scale, hiding low-level details such as synchronization, data movement, and fault tolerance.