Research division data ingestion: Which tool should be used by data engineers to ingest customer data into the data store in the AnalyticsPOC workspace?

Research division data ingestion: Which tool should be used by data engineers to ingest customer data into the data store in the AnalyticsPOC workspace?

• A. dataflow
• B. notebook
• C. dataset
• D. stream analytics

Answer: (A)

Dataflow is used to ingest data into the AnalyticsPOC workspace’s data store. It serves as the ETL/ELT pipeline that connects to various data sources, applies transformations, and loads the data into the workspace for analytics. This makes it the right tool for bringing customer data into the data store with options for batch or incremental loading, scheduling, and repeatable pipelines. Notebooks are for interactive data science work and exploratory analysis, not reliable data ingestion pipelines. Datasets are the stored, prepared tables you query after the data is ingested, not the mechanism that brings data into the store. Stream Analytics handles real-time streaming scenarios, which is useful for continuous data streams, but the scenario describes general ingestion into the data store, where a dataflow pipeline is the appropriate choice.

Dataflow is used to ingest data into the AnalyticsPOC workspace’s data store. It serves as the ETL/ELT pipeline that connects to various data sources, applies transformations, and loads the data into the workspace for analytics. This makes it the right tool for bringing customer data into the data store with options for batch or incremental loading, scheduling, and repeatable pipelines.

Notebooks are for interactive data science work and exploratory analysis, not reliable data ingestion pipelines. Datasets are the stored, prepared tables you query after the data is ingested, not the mechanism that brings data into the store. Stream Analytics handles real-time streaming scenarios, which is useful for continuous data streams, but the scenario describes general ingestion into the data store, where a dataflow pipeline is the appropriate choice.