Data Fabric
Ask which capability is actually being offered — cataloguing, virtualization, lineage, policy enforcement — rather than whether to adopt the concept.
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Valid Time and System Time
Distinguish business validity from recorded system versions, preserve both when needed, and avoid mistaking a load timestamp for complete history.
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Point-in-Time Join
Join each fact to the intended historical value, validate per-row match counts, and check availability time before using the result as a feature.
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Slowly Changing Dimensions
Choose overwrite or version history per attribute, define interval boundaries, and understand what a type 2 dimension cannot recover.
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Fact and Dimension Tables
Separate measured events from descriptive context, declare the grain, and choose aggregation rules before joining facts and dimensions.
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Database Normalization
Use dependencies to reduce update, insertion, and deletion anomalies, while keeping historical facts at the grain where they belong.
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Wide and Long Data
Distinguish wide and long data by the measurement dimension, preserve missing values, and know when pivoting loses information.
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Designing End-to-End Data Architecture
How consumer requirements determine data contracts, logical layers, physical deployment, consistency boundaries, and recovery decisions.
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Architecture Decision Record
Record an architecture decision with its context, alternatives, consequences, status, and evidence that would justify replacing it.
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Incremental Extraction Watermark
Track incremental extraction progress, handle timestamp ties and late commits, and advance the checkpoint only after the destination commits.
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Reverse ETL
Move modeled data into operational tools with stable keys, field ownership, deletion rules, and retry handling that respects downstream effects.
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Data Mart
Design a data mart for a defined business subject, with explicit row grain, metric meaning, freshness, and a safe way to publish replacements.
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Medallion Architecture
Understand bronze, silver, and gold as refinement roles, choose useful boundaries, and separate layer names from quality and transaction guarantees.
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Data Contract
Define a data contract with meaning, keys, units, ownership, quality rules, and a change process that consumers can actually depend on.