Defining the Error Mart

When working with data platforms that follow the Data Vault methodology, one often hears about components like the Raw Vault, Business Vault, and Information Marts. But among these well-known layers is a lesser-discussed yet critical structure: the Error Mart.

In this blog post, we take a comprehensive look at what an Error Mart is, what its main objectives are, and the best practices for designing one. This insight is based on an informative session led by Michael Olschimke, CEO of Scalefree, during a recent Data Vault Friday.

What is an Error Mart?

In traditional data warehousing approaches like Kimball, an Error Mart is used to store metrics about errors—for example, how many ETL jobs failed or which tables didn’t load successfully. These are primarily KPIs used for monitoring and are typically stored in what’s known as a Metrics Mart.

However, in the context of Data Vault 2.0, the Error Mart has a different, more tactical role: it acts as a catch-all for rejected records that fail to load during any of the staging or integration processes.

This could be due to a mismatch in expected data types, missing columns, or unexpected structural changes in the source data. These issues most frequently arise during:

• Initial data ingestion from files, APIs, or real-time feeds
• Loading data into the staging area or raw Data Vault
• Applying hard rules based on schema assumptions

The Main Goal of an Error Mart

The primary goal of the Error Mart is to ensure that all incoming data—the good, the bad, and the ugly—is captured and traceable, even if it can’t immediately be loaded into the intended layer (such as the Raw Vault).

It’s a technical safety net that provides:

• A secure location for rejected records
• The ability to analyze and correct issues manually
• A reprocessing workflow that ensures full data capture

The Error Mart is not meant for business logic errors (e.g., someone underage purchasing a product); rather, it handles technical discrepancies that prevent data from moving through the pipeline.

How Is It Structured?

Traditionally, one might think of creating multiple error tables to match each data model. However, Michael Olschimke recommends a single flexible structure—a table that stores rejected records as JSON strings. This allows you to capture various unexpected formats without predefined schemas.

Each record should be accompanied by key metadata:

• Load date – Timestamp of ingestion
• Record source – Source system or interface
• Process identifier – The job or transformation that failed

This setup ensures that every error is auditable, traceable, and eventually resolvable.

Best Practices for Designing an Error Mart

Here are some key considerations when building your Error Mart:

1. Flexibility in Structure

Since rejected data often doesn’t conform to expected schemas, use a structure that can handle variability. A single table using JSON or Parquet formats offers great flexibility, especially when stored in a data lake.

2. Avoid Over-Engineering

There’s no need to create one table per error type. One well-documented and meta-tagged table is usually sufficient.

3. Logging and Auditing

Implement a companion log table or file to track which records have been reprocessed. Instead of deleting processed error records, use a status flag or separate tracking log to preserve data lineage and maintain transparency.

4. Trigger Monitoring and Alerts

Your system should monitor the Error Mart for unprocessed records. Set up alerts via email, log monitoring tools like CloudWatch or Greylog, or build dashboards that notify the data team when action is required.

5. Make It the Data Team’s Responsibility

A critical mindset shift: processing records in the Error Mart is not a business responsibility—it’s yours as the data engineering team. Do not offload this to end users.

6. Reprocessing Workflow

Once the technical root cause is identified (e.g., an overly strict field length), update the hard rules, reload the rejected data from the Error Mart into the target layer, and mark it as processed in your log.

7. Error Mart in Every Layer

While most errors occur in the initial stages (staging, Raw Vault), you should prepare to capture errors at every layer—Business Vault and Information Mart included.

8. Binary Data Considerations

If your incoming data includes blob fields, you can mime-encode them and store them alongside the error JSON or separately in the data lake.

Why the Error Mart Matters in Data Vault Architecture

Data Vault is built on the premise of complete and auditable data capture. To meet this principle, you must have a strategy for handling unexpected or failed data loads. The Error Mart acts as that strategy.

It’s not just a dumping ground for bad records—it’s a crucial feedback mechanism that helps you refine your ingestion and transformation rules, ensuring every piece of data, no matter how ugly, makes it into the platform.

Without an Error Mart, you risk data loss, broken lineage, and ultimately, lower trust in your data platform.

Conclusion

In summary, the Error Mart is an essential part of a resilient Data Vault architecture. It gives your data team the tools to identify, correct, and reprocess problematic data while maintaining auditability and trustworthiness.

If you’re implementing a Data Vault, don’t treat the Error Mart as an afterthought. Design it with flexibility, transparency, and process integration in mind. And remember: it’s your job to make sure no record gets left behind.

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Why Are Data Models Important?

Before diving into the specifics, let’s understand the purpose of data modeling. Imagine building a house. You wouldn’t start hammering wood together randomly—you’d begin with a sketch, then a blueprint, and finally the construction. Data models serve a similar purpose for databases and data systems.

They help ensure everyone involved (business users, developers, engineers) shares the same understanding of how data is organized, connected, and accessed.

1. What Is a Conceptual Data Model?

The conceptual model is your high-level business map. It’s like the sketch of your house drawn on a napkin. It’s not concerned with technology or database structures. Instead, it focuses on the business concepts and how they relate.

In simple terms, it answers questions like:

• What are the key things we care about? (e.g., Customers, Products, Orders)
• How are they related? (e.g., Customers purchase Products)

Here’s a basic example:

• Entities: Customer, Product, Purchase
• Relationships: A Customer makes a Purchase; a Purchase involves a Product

You don’t list detailed fields or attributes yet—just the big picture. It’s usually created during the early discussions between business stakeholders and data professionals. It helps everyone align on the language and goals before jumping into technical design.

When Do You Use a Conceptual Model?

You use this at the start of a project, especially when:

• You’re gathering requirements from the business
• You’re building a shared understanding with non-technical stakeholders
• You want to clarify business rules and entities

2. What Is a Logical Data Model?

Once you understand the business concepts, it’s time to turn those into a more detailed, technology-independent design. That’s the job of the logical model.

The logical model focuses on the structure of data—what fields each entity has, how they’re connected, and what kind of data they store. But it still doesn’t worry about the actual database platform or syntax.

Continuing the earlier example, a logical model might say:

• Customer has attributes like First Name, Last Name, Email
• Product has attributes like Product ID, Name, Price
• Purchase links Customer and Product using unique IDs

In the context of Data Vault (a methodology for modeling enterprise data warehouses), this is where you define your:

• Hubs: Core business entities (e.g., Customer, Product)
• Links: Relationships between entities (e.g., Purchase)
• Satellites: Descriptive data about Hubs and Links (e.g., customer name, address)

You also classify data here. For instance, you might mark some attributes as sensitive for privacy reasons or note how frequently they change.

When Do You Use a Logical Model?

This comes after your conceptual model, typically during solution design or system architecture planning. You use it when:

• You need to define how data should be structured and connected
• You’re planning your Data Vault architecture
• You want to define metadata for automation tools like dbt, Wherescape, or Coalesce

3. What Is a Physical Data Model?

Now we get technical. The physical model is the actual implementation in a database system. It’s like building the house based on the blueprints.

This includes:

• Create table statements
• Insert/load scripts
• Indexes and constraints (e.g., foreign keys)
• Platform-specific configurations (e.g., Snowflake vs. Oracle)

In the physical model, you decide:

• How data is stored (table structures, partitions)
• How data is accessed (views, security layers)
• How data is secured (row-level or column-level permissions)

For example, if your logical model says an attribute is sensitive, your physical model might enforce this by putting that attribute in a separate satellite or restricting access using database roles.

When Do You Use a Physical Model?

This comes last in the chain. You use it when:

• You’re ready to implement in a database
• You’re generating SQL from your metadata (often using automation tools)
• You’re deploying the actual tables, views, and loading processes

Summary: The Three Layers Compared

Aspect	Conceptual Model	Logical Model	Physical Model
Purpose	High-level business understanding	Detailed data structure without technology	Actual implementation in a specific database
Focus	Entities & relationships	Attributes, keys, classifications	SQL scripts, schemas, storage
Audience	Business users & analysts	Data architects & engineers	DBAs & developers
Examples	Customer purchases Product	Customer has Name, Email; Link to Product	CREATE TABLE Customer (…); GRANT SELECT ON ViewX

The Modeling Process: From Concept to Code

One of the key takeaways from Michael Olschimke’s explanation in the Data Vault Friday session is that these models are not alternatives—they’re steps in a process.

1. Start with the conceptual model to understand the business.
2. Create a logical model based on the incoming data and requirements.
3. Generate the physical model using automation tools or code templates tailored to your database platform.

Each step builds on the one before it, guiding you from abstract ideas to concrete implementation.

Final Thoughts

If you’re new to data engineering, start small. Talk to the business. Sketch out their world. Then, gradually evolve those ideas into structured data models and finally into code. Understanding the differences between conceptual, logical, and physical models will make you a more effective engineer—and a better bridge between business and tech.

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Soft-Deleting Records in Data Vault

When working with Data Vault in real-world enterprise data warehousing projects, managing soft-deleted records is more than just a theoretical exercise—it’s a necessity. While many books and training examples offer simplified scenarios, real-world implementations must take into account complex requirements such as status tracking from multiple source systems. In this article, we dive into how to virtualize dimensions in a Data Vault model with proper handling of status tracking satellites, ensuring that soft deletions are effectively managed and the integrity of historical records is preserved.

The Problem with Simplified Examples

Most Data Vault tutorials focus on core concepts: Hubs, Links, and standard Satellites. These foundational examples are useful for learning but fall short when we need to address data that has been logically removed or soft-deleted. In real-world systems, this is a common occurrence, and ignoring it risks producing inaccurate analytics and flawed dimension models.

The key challenge is to determine whether a business entity—like a customer, product, or concept—is still “active” in the eyes of the business. This gets even trickier when the data comes from multiple source systems, each with its own deletion logic. That’s where Status Tracking Satellites (also known as Effectivity Satellites) come in.

Scenario Overview: Multi-Source Concept with Soft Deletes

Consider a scenario where a Concept (e.g., “Customer”) is fed by two different source systems. Here’s a breakdown of the Data Vault objects involved:

• Concept_PIT – A Point-In-Time (PIT) table indexing data across all satellites.
• Concept_SAT_S_source1 – A standard satellite with descriptive data from Source 1.
• Concept_SATST_source1 – A status tracking satellite for Source 1.
• Concept_SAT_S_source2 – A standard satellite from Source 2 with more descriptive attributes.
• Concept_SATST_source2 – A status tracking satellite for Source 2.

In this setup, each source system tracks its own deletions, independently of the other. That means a Concept could be deleted in one source but still be active in another. Properly modeling and querying this requires careful integration of all status indicators.

What Do Status Tracking Satellites Actually Do?

Contrary to some misunderstandings, status tracking satellites are not used to track the deletion of descriptive attributes. That’s the role of standard satellites. Instead, they track whether the entire business key has been logically deleted in the source system. For example, if a customer row is completely removed from a source table, the status tracking satellite records that deletion event.

This distinction is important: you might null out a field in the source system without deleting the record. The standard satellite captures that. But if the whole customer row is deleted, only the status tracking satellite will catch it.

How to Use the PIT Table

The PIT table is key to virtualizing dimensions. It acts as a bridge, linking together the various satellites—both descriptive and status tracking—by capturing the effective row per business key and timestamp. Your virtual dimension view selects from the PIT table and joins all relevant satellites using hash keys and load dates.

In this case, you treat the status tracking satellites just like standard satellites. You join them on hash keys and load dates using PIT indexes. This lets you bring in any flags, such as IsActive or deletion timestamps, as part of your view logic.

Business Rules for Determining Active Status

One of the biggest questions in designing this architecture is: How do we determine if a business key is still active?

There are a few approaches:

• Single-source dominance: If the customer is deleted in the primary (golden) source, the business key is considered deleted.
• All-source consensus: The business key is only considered deleted if it’s removed from all source systems.

Each approach has its pros and cons. The decision should be based on your business rules and requirements. You can use simple boolean flags like IsActive or more advanced logic combining multiple status indicators from different sources.

Should You Remove Deleted Entities from Dimensions?

This is a hot topic. Some organizations want to remove soft-deleted entities from their dimension tables entirely. While that may sound clean, it can create problems downstream—especially in fact tables where foreign key references still exist for those deleted entities.

The recommended approach? Flag them instead of deleting them. This preserves history and maintains referential integrity. It also helps analysts understand that, yes, the product or customer was deleted—but it still contributed to revenue or other KPIs in the past.

Virtualizing the Dimension View

Your final dimension view should:

• Select from the PIT table for the appropriate concept.
• Join to all descriptive satellites using the hash key and PIT load dates.
• Join to all status tracking satellites similarly, treating them like descriptive sources.
• Derive an IsActive or IsDeleted flag using business logic.

Here’s a simplified example of what that SQL might look like:

SELECT 
  pit.BusinessKey,
  sat1.Description1,
  sat2.Description2,
  CASE 
    WHEN st1.IsActive = 1 AND st2.IsActive = 1 THEN 1
    ELSE 0
  END AS IsActive
FROM Concept_PIT pit
LEFT JOIN Concept_SAT_S_source1 sat1 
  ON pit.HashKey = sat1.HashKey AND pit.LoadDate1 = sat1.LoadDate
LEFT JOIN Concept_SAT_S_source2 sat2 
  ON pit.HashKey = sat2.HashKey AND pit.LoadDate2 = sat2.LoadDate
LEFT JOIN Concept_SATST_source1 st1 
  ON pit.HashKey = st1.HashKey AND pit.LoadDateST1 = st1.LoadDate
LEFT JOIN Concept_SATST_source2 st2 
  ON pit.HashKey = st2.HashKey AND pit.LoadDateST2 = st2.LoadDate

This query structure ensures that all available data is consolidated and evaluated according to business-specific logic to determine the final dimension state.

Final Thoughts

Handling soft deletes in a Data Vault using status tracking satellites is a robust and scalable solution for real-world enterprise systems. The key is to treat these satellites as regular descriptive tables, include them in your PIT tables, and let business logic drive how deletions are interpreted in the dimension views.

Instead of deleting records from your dimensions—which can break fact table relationships—simply flag them. This gives you the full power of historical traceability while still providing clear information about current entity status. As always, your implementation should follow the business rules defined by your organization or your client.

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Understanding the Nature of Address Data

In many systems, address data doesn’t come in a uniform format. Some systems may embed it directly in a contact or customer table—think “billing address” and “shipping address” fields in Salesforce—while others provide a separate table for addresses and even a relationship table showing links between addresses and business entities.

Let’s look at how to tackle both of these situations using Data Vault best practices.

Case 1: Addresses as Attributes Inside Another Table

If your source delivers addresses as part of another table (e.g., contact data with billing and shipping fields), the Raw Data Vault should model the data exactly as it comes. For example:

• Create a Contact Hub with a business key for contacts.
• Attach a Satellite containing billing and shipping address fields like city, street, and ZIP code.

Even if multiple contacts share the same address, duplication is acceptable in the Raw Vault—it’s a reflection of how the source system delivers the data. Optimization or deduplication can happen in the Business Vault or information marts.

Case 2: Addresses in a Separate Table

When your source system contains a dedicated address table, you have two main modeling options:

Option A: Treat Address as a Hub

• Create an Address Hub using a business key. If no natural key exists, use a surrogate/technical key.
• Attach a Satellite to store descriptive fields (e.g., street, city, ZIP).
• Use a Link to relate addresses to other Hubs like Contact, Customer, or Lead.

This pattern is especially useful in industries like insurance, where addresses are treated as critical business objects (e.g., accident location).

Option B: Treat Address as Reference Data

• Store addresses in a flat reference table.
• Use an ID (like address_id = 55) as a code in a descriptive Satellite on related Hubs.

This is simpler but comes with limitations—it doesn’t track historical changes. For example, if a street name changes, the system won’t retain that history.

Tracking Changes in Reference Data

To address the historical limitation, consider modeling reference data with:

• A Reference Hub (no hashed keys, just the code, load date, and record source).
• A Reference Satellite to track changes over time using load date as part of the primary key.

This design allows you to maintain a history of changes in descriptive reference data without violating Data Vault principles.

Handling Many-to-Many Relationships Between Addresses and Entities

Things get more interesting when addresses are shared across multiple business objects (e.g., one address used by both a Lead and a Customer). If your source includes a bridge table (many-to-many), follow this approach:

• Create an Address Hub.
• Create individual Hubs for each business object (Lead, Customer, Contact).
• Establish a Link using the bridge table to represent relationships.
• Track relationship history using Effectivity Satellites.

If the bridge table uses a generalized object like “Customer” to reference multiple types (Contact, Account, Lead), you’ll need:

• A Generic Customer Hub.
• Use raw business keys or technical IDs (UUIDs).
• Create Links between the Customer Hub and the Address Hub.
• Use Satellites to track effectiveness (i.e., from when to when an address is associated).

Resolving Ambiguity with Conditional Relationships

Sometimes, the source system generalizes business objects (e.g., Microsoft CRM’s Customer entity could be an Account, Lead, or Contact). In these cases, build a generic Customer Hub first. Then, in the Business Vault, apply conditional logic to determine if a Customer is actually a Lead, Contact, or Account—but only in the Business Vault.

This conditional logic would take the form of queries that check whether a UUID from the generic Customer Hub exists in the Lead Hub. If so, you can establish a Business Vault Link between the generic Customer and the Lead.

Guiding Principles for Modeling Address Data

• Stay Data-Driven: Model what you see, not what you think should be there.
• Don’t Add Conditional Logic to the Raw Vault. It belongs in the Business Vault.
• Use Hubs for real business objects like Address when they are shared across systems or have standalone value.
• Use Reference Data when addresses are just descriptive codes without relationships.
• Track History with Effectivity or Reference Satellites if needed.

Ultimately, the choice between treating address data as a business object or reference data depends on your use case. If you’re dealing with complex, shared addresses with historical importance, model them as Hubs. If not, use reference tables or Satellites. But always be consistent and avoid conditional logic in the Raw Vault.

Conclusion

Modeling address data in a Data Vault architecture isn’t one-size-fits-all. Whether it’s Salesforce, SAP, or Microsoft CRM, the goal is to be faithful to your data, follow the architecture’s guiding principles, and maintain flexibility. By doing so, you ensure scalability, auditability, and long-term maintainability of your data warehouse solution.

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Persistent Staging Area vs Transient Staging Area

Data architecture decisions can make or break the efficiency, flexibility, and scalability of your analytics platform. One such decision revolves around staging areas—more specifically, whether to use a Persistent Staging Area (PSA) or a Transient Staging Area (TSA). Both serve as critical components in the data pipeline, but they serve different needs and use cases.

In this post, we’ll explore the pros and cons of each approach, examine when to use a PSA versus a TSA, and explain how they align with modern data strategies such as Data Vault 2.0. Whether you’re a data engineer, architect, or BI consultant, this guide will help you make a more informed choice for your data warehouse design.

Why Do You Need a Staging Area?

Before diving into PSA vs TSA, let’s understand the purpose of a staging area:

• Source System Isolation: Quickly extract data from operational systems to reduce their load.
• Performance Optimization: Decouple extraction and loading processes to improve efficiency.
• Preprocessing: Perform data validation, cleansing, and simple transformations—though in ELT architectures like Data Vault 2.0, this is minimized.

What Is a Persistent Staging Area (PSA)?

A PSA stores multiple historical batches of source data, often indefinitely. It serves as a historical repository and is commonly implemented on a NoSQL database or data lake using formats like JSON or Parquet.

Advantages of PSA

• Full Reload Capability: If your raw Data Vault needs rebuilding (e.g., due to modeling mistakes), PSA allows reloading from historical data without re-accessing source systems.
• Schema Flexibility: Semi-structured formats can easily accommodate changes in source schemas without breaking processes.
• Decoupling Ingestion and Integration: You can ingest all available data and model the Data Vault incrementally, enabling agile development.
• Support for Advanced Analytics: Data scientists can query the data lake directly for machine learning and data mining.
• Virtualized Raw Data Vault: Enables virtualization techniques for accessing raw historical data efficiently.

Drawbacks of PSA

• Higher Storage Costs: Storing all historical data requires more disk space, though archiving and cold storage can help mitigate this.
• Compliance Effort: Implementing GDPR compliance (e.g., “right to be forgotten”) is more complex with persistent data storage.
• Setup Complexity: Requires thoughtful structure to ensure efficient incremental loading into the Data Vault.

What Is a Transient Staging Area (TSA)?

In a TSA, data is only available temporarily, usually for the duration of the ETL process. After the data is loaded into the target system, the staging area is cleared, often by truncating tables. TSA is typically implemented on relational databases.

Advantages of TSA

• Simplicity: Easy to implement with a 1-to-1 copy of the source structure.
• Lower Storage Requirements: Since data is temporary, there’s no need for extensive disk space.
• Improved ETL Performance: Smaller data volumes and simpler management often lead to faster ETL cycles.
• Minimal Compliance Burden: Personal data is automatically deleted after each cycle, simplifying GDPR requirements.

Drawbacks of TSA

• No Historical Reload: If a problem occurs, you must re-extract data from the source, which might be unavailable or changed.
• No History Preservation: Only one batch is available at a time—no record of previous data states.
• Immediate Schema Adaptation Needed: If the source schema changes, you must adapt your data vault model right away or risk losing new attributes.

PSA vs TSA: A Side-by-Side Comparison

Aspect	Persistent Staging Area (PSA)	Transient Staging Area (TSA)
Data Retention	Long-term, historical	Short-term, per batch
Storage Requirements	High (can be optimized)	Low
Complexity	Medium to High	Low
Compliance Effort (e.g., GDPR)	High	Low
Schema Change Handling	Handled flexibly	Needs immediate updates
Data Reload Capability	Yes, full history available	No, requires source system access
Auditability	High	Low
Tool Support	Ideal for data lakes, supports tools like dbt	Compatible with tools but limited scope

When Should You Choose PSA Over TSA?

Choosing between PSA and TSA depends on your project requirements. Here’s a quick guide:

Choose PSA if:

• You need historical data retention and full reload capabilities.
• You want flexibility in schema evolution without breaking processes.
• Your organization performs advanced analytics or machine learning.
• You’re implementing a long-term, scalable, agile data architecture (like Data Vault 2.0).

Choose TSA if:

• You’re working with resource-constrained environments.
• You want a simpler, lightweight setup.
• Data historization isn’t critical for your use case.
• You need rapid ETL performance without added complexity.

How Does This Fit with Data Vault 2.0?

Data Vault 2.0 supports both PSA and TSA. However, the modern recommendation leans towards PSA, particularly with NoSQL or data lake implementations. The ability to decouple ingestion from modeling, manage schema changes gracefully, and maintain full history aligns well with agile and resilient data vault practices.

Tools like dbt do not require a PSA explicitly but benefit from having consistent data available in the stage, regardless of its persistence.

Final Thoughts

Both PSA and TSA play important roles in data architectures. Understanding their strengths and trade-offs allows you to design data pipelines that meet your current and future needs. In most modern, data-driven organizations, a Persistent Staging Area offers the flexibility and robustness needed to manage data complexity, scale analytics efforts, and maintain auditability across the board.

However, don’t rule out TSA for its simplicity and speed, especially in prototyping or when storage is limited.

Ultimately, your choice should align with your business goals, compliance needs, and data maturity level.

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Soft-Deletes in Dimensions

Data Vault practitioners often encounter the challenge of handling soft deletes in dimensional modeling. While source systems may hard delete records, a data warehouse must maintain historical integrity. This is where effectivity satellites and PIT (Point-in-Time) tables become crucial. Let’s explore how to apply soft deletes effectively in Data Vault dimensions.

Why Soft Deletes?

In a data warehouse, deleting a record outright can lead to inconsistencies, particularly when historical data or fact tables reference that entity. Instead of deleting, we mark records as inactive while preserving history.

Effectivity Satellites for Status Tracking

An effectivity satellite helps track changes in a record’s lifecycle, including soft deletes. This satellite contains:

• A deletion flag (e.g., isDeleted or isActive).
• A deletion timestamp (deletedDate).
• Load date timestamps to track changes.

This setup allows toggling between active and inactive states as records are deleted and restored in the source system.

One Satellite per Source System?

Each source system may handle deletions differently. If multiple sources feed the same entity, separate effectivity satellites per source ensure accurate tracking.

Adding Effectivity Satellites to PIT Tables

The PIT table aligns different satellite timelines, allowing efficient query performance. Including effectivity satellites in PIT tables ensures:

• Accurate status tracking across snapshots.
• Seamless integration with dimensional views.
• Faster lookups by pre-aligning timelines.

Virtualized Dimensions Without PIT Tables

Without a PIT table, virtualized dimensions must align effectivity data dynamically. This requires:

• Joining effectivity satellites on-the-fly.
• Using window functions like LEAD or LAG for tracking state changes.
• Referencing a date dimension for time-based joins.

While possible, this approach is less efficient than using PIT tables.

Final Thoughts

Soft deletes in Data Vault dimensions are best managed through effectivity satellites and PIT tables. By storing deletion timestamps and flags, we retain history while allowing records to be marked inactive instead of being removed.

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