An analyst must identify data extraction methods that fit the source's structure, available access, update frequency, and required population. A daily customer export and a continuously changing order database present different completeness risks.
Structured data have predefined fields, such as invoice columns. Semistructured data contain tags or named attributes with variable arrangements. Unstructured data, such as scanned correspondence, require parsing or field extraction before ordinary tabular analysis.
Several interfaces support these extraction techniques.
- Structured Query Language (SQL) expresses requests against relational data. A direct query can select specified columns and qualifying records when database access exists.
- An application programming interface (API) exposes a system's defined operations and data to other software. Its extraction capabilities depend on the available endpoints and permissions.
- Change data capture (CDC) identifies source changes for downstream processing. Log-based implementations can capture inserted, updated, and deleted records when configured to include those events.
The methods differ in access and completeness exposure.
A full extraction copies the entire defined population at a point in time. An incremental extraction retrieves records added or changed since a recorded checkpoint, reducing repeated movement while creating...
Common mistakes
- Checkpoint advanced too soon. A failed load becomes an omission when the next incremental extract starts beyond its unprocessed records.
- Dictionary mistaken for enforcement. A required relationship written in documentation remains unenforced when the schema lacks the necessary constraints.
- Historical and current addresses confused. The address diagnosis depends on whether the field represents current contact information or a transaction-date snapshot.
Bottom line
- Extraction methods must fit source access, change behavior, update frequency, and completeness requirements.
- Warehouses integrate reporting data, lakes accommodate diverse formats, and marts serve narrower subjects; star and snowflake describe dimensional relationships.
- Life-cycle controls govern creation, processing, use, storage, archival, and disposition through assigned owner, steward, and custodian responsibilities.
- Relational integrity depends on enforced keys and value constraints, while dictionaries explain meaning and normalization addresses dependency anomalies.
Exam shortcut
First translate the information need into a population and one-row meaning. Mark “every,” “matched only,” “current,” and “as of” before inspecting SQL. Trace one unmatched record, one boundary-date record, and one multiply matched record. These expose exclusion, cutoff, and duplication errors quickly. Check the left table before trusting left join, then inspect where for conditions on nullable right-side fields. For normalization, identify what determines each attribute.
The full lesson (about 7,739 words, 52 min read) adds 2 worked examples, all 6 common mistakes, a self-check, free in the app.
Learning objectives
- I.B1
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