🔍 Data Lineage & Governance Language
11 exercise sets. Master the vocabulary for data lineage tracking, metadata management, data catalogs, and governance communication.
Data Lineage Basics
Upstream vs. downstream datasets, lineage graphs, column-level lineage, impact analysis, and OpenLineage specification vocabulary.
Data Catalog Vocabulary
DataHub, Atlan, OpenMetadata, Alation — business glossary, data domain, data steward, certification, and data product vocabulary.
Metadata Management Vocabulary
Technical vs. business metadata, operational metadata, schema metadata, metadata-driven architecture, and data classification labels.
Data Quality & Lineage Vocabulary
Data quality dimensions, expectations, data SLA, anomaly detection — vocabulary for communicating data quality in governance contexts.
Governance Communication Vocabulary
Data governance frameworks, governance committee, RACI for data, federated governance, data mesh governance, data policy vocabulary.
Data Observability Vocabulary
Data downtime, freshness, volume, distribution, schema change detection, data incident — vocabulary for data health and observability tools.
Impact Analysis Vocabulary
Downstream dependencies, breaking change propagation, "this schema change will affect N pipelines" — vocabulary for communicating impact before making changes.
Data Catalog Navigation Vocabulary
Finding datasets, reading catalog entries, data owner, last updated, quality score, certified status — vocabulary for navigating modern data catalogs.
Data Stewardship Communication
Who is the data owner, stewardship workflow vocabulary, requesting access, data classification — language for data governance communication.
GDPR Right-to-Erasure via Lineage
Delete all records for user X, propagate deletion downstream, compliance documentation language — GDPR erasure workflows using data lineage.
Data Lineage Review Vocabulary
The lineage graph shows, column-level lineage confirms, trust score, presenting lineage findings — vocabulary for lineage reviews and incident reports.
Frequently Asked Questions
What is a 'metadata graph' in the context of data lineage exercises, and how does it relate to visualizing transformations?
A metadata graph represents the flow of data through various systems by connecting source tables with their derived tables via defined transformations. It's built using metadata extracted from databases and ETL tools, allowing you to visually map dependencies and understand the impact of changes at any point in the lineage. This is crucial for exercises focused on tracing data back to its origin.
I'm struggling with 'business glossary' integration – how do I use it within a lineage exercise to explain data meaning?
The business glossary provides definitions and terms associated with your data assets, which you can link directly into your lineage diagrams. This allows you to show the context of a field ('customer ID') beyond its technical name, enhancing understanding for non-technical stakeholders and adding valuable documentation during your exercises.
Can I use SQL queries within these language exercises to demonstrate data quality checks at different stages of lineage?
Absolutely! These exercises often involve using SQL to represent data validation rules applied across transformations. For example, you could create a query to check for null values after a join or to verify that calculated fields meet specific criteria, demonstrating data governance practices.
What's the purpose of 'golden source' in relation to a lineage exercise – is it just one single database?
A golden source represents the most trusted and authoritative data copy within your organization, often identified as the primary source for reporting. In an exercise, you'll trace data back from various downstream systems to this golden source, validating the accuracy of transformations and ensuring consistency across the entire lineage.
How do I represent 'impact analysis' during a data governance exercise using lineage diagrams?
Impact analysis involves determining which systems or reports would be affected if a change is made to a source table. Lineage exercises help visualize this by showing the downstream dependencies, allowing you to identify and assess potential consequences of modifications – essential for risk management.
What's 'differential lineage', and why is it useful in these language exercises?
Differential lineage focuses on changes to data over time, tracking how transformations evolve. This is particularly relevant when debugging data issues or understanding the impact of schema updates, allowing you to pinpoint exactly what changed and its effect within your lineage diagrams.
How can I use a language like Python (or similar) in these exercises to automate the generation of lineage diagrams?
You can utilize scripting languages to parse metadata from various sources – databases, ETL tools – and programmatically generate lineage diagrams. This automates the process of building complex visualizations based on your data's transformation history, significantly improving efficiency during your learning exercises.
What is 'data masking' or 'redaction' and how does it appear in a data lineage exercise?
Data masking refers to techniques like replacing sensitive data with realistic, but fake, values for non-production environments. In lineage exercises, you'll see masked fields represented in diagrams, demonstrating where these protections are applied throughout the transformation pipeline.
Can I model data governance policies (e.g., 'change control') within a lineage exercise using specific language features?
Yes, you can represent policy enforcement through the lineage diagrams themselves. For example, if a change to a source table requires approval, you could visually denote that transformation as having a 'pending' status or link it to a workflow process represented in your chosen language.
How do I represent data 'schemas' and their evolution within the lineage diagram – is it just table names?
Schemas are critical for understanding data structure. Lineage exercises involve tracking how schemas (table definitions, column types) change across transformations, showing the impact of schema modifications on downstream systems and highlighting potential compatibility issues.