Mass Ingestion and Data Synchronization Flashcards
6 cards from real IICS Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Mass Ingestion and Data Synchronization flashcards as text
What is 'Mass Ingestion' in IICS primarily designed for?
Answer: High-volume, efficient ingestion of data from databases or files into cloud targets with minimal configuration
Mass Ingestion is optimized for high-volume bulk data loading from databases or files into cloud data warehouses or data lakes with minimal setup.
Which two types of Mass Ingestion tasks does IICS provide?
Answer: Mass Ingestion Files and Mass Ingestion Databases
IICS offers Mass Ingestion Files (for file-to-cloud loads) and Mass Ingestion Databases (for database-to-cloud loads) as the two primary task types.
In IICS Mass Ingestion Databases, what does 'initial load' refer to?
Answer: The first historical bulk load of all data from the source database into the target
An initial load performs a full historical bulk extract of source table data into the target, establishing the baseline dataset.
What technology does IICS Mass Ingestion Databases use to capture ongoing changes from the source after the initial load?
Answer: Change Data Capture (CDC) using database transaction logs
Mass Ingestion Databases uses CDC, reading database transaction logs to capture inserts, updates, and deletes in near real-time after the initial load.
Which cloud storage targets are commonly supported by IICS Mass Ingestion Files?
Answer: Amazon S3, Azure Data Lake Storage, Google Cloud Storage, and similar cloud object stores
IICS Mass Ingestion Files can land data in major cloud object stores including Amazon S3, Azure Data Lake Storage Gen2, and Google Cloud Storage.
In IICS Mass Ingestion, what does 'schema drift handling' allow?
Answer: Automatically propagating source schema changes (new columns, type changes) to the target without manual intervention
Schema drift handling automatically detects and propagates structural changes in the source schema to the target, reducing manual reconfiguration.