Google cloud big data is a system that stores and processes a large amount of data. It is natural to host a large amount of data architecture in the cloud. That is why it provides unlimited data storage and simple options. Moreover, we use it for most parallelized big data processing and analysis. In addition, a big data platform is a type of IT solution. That merge the attributes and abilities of several big-data application within a single solution. It is a business class platform that enables a company to develop, operate, and manage a big-data architecture.
Google provides many services to us. One of the best services is the Google Cloud Platform. This platform support big-data storage and analysis. BigQuery is most important to perform a high-performance SQL-compatible engine. In addition, that used to perform analysis on large data volumes in a few seconds. Google Cloud Platform (GCP) provides some other services. These services are Dataflow, Dataproc, and Data Fusion. Furthermore, by using these services you can create a complete cloud-based big data architecture.
Google provides a reference architecture for large-scale data on Google Cloud. This architecture is more than 100,000 events in one second. Moreover, this architecture is built on Google BigQuery. Google suggests hot paths and cold paths building for big-data architecture.
Hot Path
A hot path is a data stream that needs near real-time processing.
Cold Path
A cold path is a data stream that can fix after a short gap.
Some advantages are,
- It can store logs for all events without considering quotas.
- It decreases costs because only some events need to handle other than streaming inserts that are more expensive.
- The hot path sustains into BigQuery by using a streaming insert that enables continuous data flow.
- The cold path sustains into Google Cloud Storage for loading in batches to BigQuery.
