488 lesson 2

What is the primary purpose of the data transformation process in data warehousing?

• To directly load data into an analytical store without any changes.

• To transform data after it has been loaded into an analytical store.

• To clean, filter, and restructure data before loading it into an analytical store.

• To ensure data privacy and security

Why are data models created in data warehousing?

• To simplify data for reporting and visualization purposes.

• To speed up data ingestion and processing.

• To eliminate the need for analytical data stores.

• To process streaming data in real-time.

What characterizes a data warehouse?

• A database optimized for transactional workloads.

• A file store for unstructured data.

• A schema designed for data analytics, with central fact tables and dimension tables.

• A system that enforces schema constraints on stored data.

What defines hybrid approaches in data storage?

• Storing raw data as files and exposing them as tables for SQL queries.

• Exclusively relying on data lakes for storage.

• Support a mix of structured, semi-structured, and unstructured data without enforcing schemas.

• Has Hadoop to enable relational storage and SQL querying

What is a significant disadvantage of batch processing in data analytics?

• Data errors are easily corrected.

• Data can be processed as individual units.

• Processing occurs at off-peak hours.

• Process halts due to data issues or program crashes.

How can stream processing be applied to real-world scenarios effectively?

• By waiting for data to accumulate before processing.

• By immediately triggering alarms and actions.

• By processing data offline in bulk.

• By aggregating data over extended time periods.

In terms of data size, what is Stream processing primarily intended for?

• Efficient handling of large datasets.

• Processing all data in the dataset.

• Micro batches with a few records.

• Complex analytics.