Without a doubt D.
Whenever we want to process timeseries data look for BigTable.
Also you want to perform analystics in Box 4 ..look for BigQuery
Only D provides this option.
The correct process for building a pipeline to process time-series data. Here's how each of the components is used:
1. Cloud Pub/Sub: receives and distributes time-series data from different sources.
2. Cloud Dataflow: processes the data by applying transformations and analytics.
3. Cloud Bigtable: stores and manages the processed data as a NoSQL database.
4. BigQuery: provides a SQL-like interface to analyze the data and extract insights.
By combining these components, you can create a scalable and reliable pipeline to process and analyze time-series data in real time.
D. Datastore is an old name - new name is firestore in datastore mode. Processing time series data is best done in BigTable which supports timeseries small data in realtime and particulalry for IoT
(non relational)
The correct answer is D
This diagram shows a typical pipeline for processing time-series data:
1. **Cloud Pub/Sub:** A messaging service that allows you to send and receive messages between independent applications.
2. **Cloud Dataflow:** A fully-managed service for transforming and processing data streams.
3. **Cloud Bigtable:** A wide-column, distributed NoSQL database that is optimized for storing and analyzing large amounts of data.
4. **BigQuery:** A fully-managed, petabyte-scale analytics data warehouse that enables businesses to analyze all their data very quickly.
In this pipeline, the following happens:
1. Time-series data is sent to Cloud Pub/Sub.
2. Cloud Dataflow reads the data from Cloud Pub/Sub and performs any necessary transformations or processing.
3. Cloud Dataflow writes the transformed data to Cloud Bigtable.
4. BigQuery queries the data in Cloud Bigtable to generate insights.
IoT = Unstructured data -> eliminated Datastore + Timeseries requirement = BigTable
Ingestion point = Pub/Sub (Firebase messaging is a push notification service to client)
Process data + realtime or batch = Data flow
Analytics = BigQuery
Hence D.
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