Why is schema drift in Data Vault harder than just a Git diff?
Managing schema drift in a Data Vault isn't just about detecting a file change. A simple diff shows what changed, but Data Vault time travel requires generating how—the specific new entities, cutover-aware loaders, and updated orchestration needed to preserve all past history while seamlessly introducing the new model.
With VaultSpeed, this entire process is automated, turning complex source changes into safe, auditable, and history-preserving Data Vault evolutions.
How VaultSpeed Solves Data Vault Schema Drift
Applies a Governed Ruleset: VaultSpeed uses a ruleset covering hundreds of specific change types to map any source modification (like splits, merges, or key changes) to the correct, construct-aware DV pattern and loader behavior.
Generates Minimal Delta Packages: It creates only the necessary new hubs, links, and satellites, plus the required loaders, while keeping all existing entities intact so past snapshots remain fully queryable.
Automates Orchestration Updates: The tool generates "cutover-aware" load patterns and automatically updates orchestration, ensuring that existing data flows and downstream reporting artifacts (like PITs and bridges) do not break.
Manages Loader Behavior: VaultSpeed also handles parameter changes (e.g., CDC strategy, referential integrity) that affect loader logic across all layers, automatically regenerating all affected jobs—a task that could take hundreds of days to remap manually.
Frequently asked questions (FAQ)
What’s the risk of handling Data Vault drift manually?
Manually scripting drift or using generic templates often applies the wrong logic (e.g., treating a table split like a simple rename). This breaks historical "time travel," invalidates downstream dependencies, and fails to update complex loader logic, leaving you with a model that can't reconstruct its own past.
Does VaultSpeed support schema drift automation on platforms like Snowflake or Databricks?
Yes. VaultSpeed is platform-independent. Its rules engine and delta generation automatically produce the correct, optimized native SQL, dbt, or Spark code for your specific data platform. This ensures the same governed, time-travel-aware logic runs seamlessly on Snowflake, Databricks, Azure Synapse, and others.
How does this "delta package" generation integrate with a Git-based CI/CD workflow? Do I get SQL files I can commit and run, or is it a black-box deployment?
This is a critical integration point. A true enterprise tool will generate platform-native delta code (DDL, DML, orchestration scripts) that can be committed to a Git repository. Your existing CI/CD pipeline can then pick up these scripts to be tested and promoted across environments (dev, test, prod) just like any other code, giving you full control over deployment.
What happens when the automation's "governed ruleset" doesn't cover a specific, complex edge case? How do you handle overrides or customizations without breaking the automation?
This is the main weakness of simple template-based systems. An enterprise-grade platform's ruleset is designed to cover hundreds of change types, such as splits, merges, and key changes. For true edge cases, the system should allow you to customize loader behavior or model mappings, ideally while remaining within the governed framework so your customizations are not overwritten by the next automated delta generation.
How can the tool guarantee it won't break my downstream Business Vault or reporting layers when it "automates orchestration updates"?
The automation's guarantee typically covers the artifacts it manages, like its own generated PITs and bridges. It ensures these specific artifacts are updated to be "cutover-aware". For custom-built layers, you would typically use the tool's metadata repository or API to identify which downstream objects you need to manually check or adapt, though the goal is to minimize this by ensuring the core Data Vault remains consistent.
How does the automation handle destructive changes, like a dropped source column, while preserving "time travel"?
A simple diff would just note the column is gone. A proper DV automation engine, applying a governed ruleset, would interpret this change and only modify the new satellite loaders to stop loading that attribute. All historical data in the satellite remains intact and queryable, perfectly preserving the "time machine". This avoids breaking historical queries while correctly reflecting the new source structure going forward.
You claim VaultSpeed generates "cutover-aware load patterns". What does that actually mean for my ETL/ELT jobs during a deployment?
It means VaultSpeed generates the delta code (new tables, new loaders) and automatically updates the orchestration flows. When deployed, the new flows run alongside the old ones. The cutover-aware logic ensures that data flowing into the old model structure completes, while new data begins populating the new structure, all without requiring a full reload or creating a data gap.
How does VaultSpeed handle a change in loader logic (like a new CDC parameter) versus a structural change (like a new table)?
VaultSpeed manages both. A structural change (like a split) generates minimal new DV entities and loaders. A parameter change (like CDC or referential integrity) is different; VaultSpeed identifies all existing jobs affected by that parameter—across all layers—and automatically regenerates them to use the new logic. This avoids the massive manual effort of finding and updating every dependent job yourself.
If VaultSpeed is platform-independent, how does it generate optimized code for Snowflake vs. Databricks? Isn't "one size fits all" code usually inefficient?
VaultSpeed's rules engine separates the logical DV model from the physical code generation. When you generate code, you select your target platform. The engine then applies platform-specific templates to produce optimized, native SQL, dbt, or Spark code that is designed for that platform's specific architecture (e.g., using Snowflake's tasks and streams vs. Databricks' Delta Live Tables)
