Data Integration

The need to manage data movement efficiently is a primary driver for:

• Data Integration and Interoperability

• Data Storage and Operations

• Data Warehousing and Business Intelligence

• Document and Content Management

• Data Security

The acronym ETL most commonly stands for:

• Export Transform Log

• Extract Transform Load

• Extend Trim Load

• Efficient Trace Logging

• Extract Transpose Leverage

Mapping requirements and rules for moving data from source to target enables:

• load

• extract

• transformation

• analysis

• backups

When integrating two data stores using batch or real-time synchronous approaches, results in a difference in:

• data quality

• lethargy

• source of truth

• timestamping

• latency

If two data stores are able to be inconsistent during normal operations, then the integration approach is:

• Streaming

• Synchronous

• Faulty

• Asynchronous

• Uncontrolled

A 'Content Distribution Network' supporting a multi-national website is likely to use:

• a replication solution

• an extract transform and load solution

• a database backup and restore solution

• an archiving solution

• a records disposal solution

Data that is used infrequently or not at all may be moved to an alternative data store. This is called:

• replication

• analysis

• archiving

• auditing

• authentication

Three common interaction models for data integration are:

• point to point, hub and spoke, publish and subscribe.

• point to point, wheel and spoke, public and share.

• plane to point, harvest and seed, publish and subscribe.

• straight copy, curved copy, roundabout copy.

• record and pass, copy and send, read and write.

A Data Integration approach that updates a Data Warehouse with small changes from Operational systems is called:

• CDC

• ELT

• SOA

• EII

• ETL