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.
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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