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Mastering CDC Data in Data Vault 2.0

Change Data Capture (CDC) is a powerful mechanism for tracking changes in source systems. However, when the primary key in your source system differs from the business key used in your Data Vault hub, you may encounter challenges in loading data into multi-active satellites. This article explores various strategies for handling CDC data in such scenarios, offering practical solutions to ensure accurate and efficient data loading.

Understanding the Challenge

In many source systems, the primary key is a technical identifier unknown to the business. Instead, the business key represents the meaningful identifier for a business object. In a typical scenario, the relationship between the primary key and the business key is one-to-one. However, in some cases, multiple records can be active for the same business key on the same date, resulting in multi-activity.

This situation arises when the primary key is unique at a given point in time, but the business key is not. For instance, you might have multiple customer IDs in your source system (primary keys) referring to the same customer (business key).

Solution 1: Verify Multi-Activity

Before diving into complex solutions, it’s crucial to verify whether the data is genuinely multi-active. In some cases, the appearance of multi-activity might be due to records being deleted and recreated with the same business key, resulting in different primary keys.

To check this, analyze the CDC data and other technical columns in the source system to determine the order of events. If a sequence of delete and create operations is detected, you may not be dealing with true multi-activity.

Solution 2: Create a Multi-Active Satellite with Delta Checking

If the data is genuinely multi-active, the most straightforward approach is to create a multi-active satellite. Perform delta checks on the combination of the business key and the multi-active attribute (e.g., customer ID). This ensures that only changes within specific multi-active groups are loaded into the satellite.

However, this approach necessitates a specialized point-in-time (PIT) table, as the CDC data provides changes at the finest granularity (row level). You’ll need to consider both the load date timestamp and the multi-active attribute when querying the satellite to retrieve the most recent delta.

Solution 3: Remodel with Satellites on Links

Another option is to remodel your Data Vault structure by placing the satellite on the link. In this approach, the multi-active attribute becomes a dependent child key in the link, and a standard satellite is created on this link. This simplifies the handling of multi-activity within the link itself.

However, it’s important to note that the satellite in this case describes the relationship between the customer and other components, rather than directly describing the business object hub. Evaluate whether this modeling change aligns with your downstream querying requirements.

Solution 4: Use the Primary Key as a Technical Hub

As a last resort, you can use the primary key from the source system as a technical hub. This involves creating a hub for the primary key values (e.g., customer IDs) and linking it to the real customer hub using a same-as link. While not the preferred method, this can be a workaround in situations where other solutions are not feasible.

Additional Considerations

• CDC Data vs. Full Extracts: When dealing with full data extracts, even if only a part of the multi-active component changes, it’s best practice to insert the full block of data with the newest load date timestamp. This simplifies downstream processes and eliminates the need for a specialized PIT table.
• Non-History Links: If the CDC data represents transactional events and is analyzed as such, consider loading it into non-history links instead of satellites. This approach aligns with the transactional nature of the data and facilitates aggregations and trend analysis.

Conclusion

Handling CDC data in Data Vault 2.0 when dealing with multi-active satellites requires a careful assessment of your specific use case and data characteristics. The solutions presented in this article offer various approaches to tackle this challenge, each with its own advantages and trade-offs. By understanding these strategies and selecting the most appropriate one, you can ensure accurate and efficient data loading in your Data Vault environment.

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Demystifying Data Warehouse and Data Vault

In today’s data-driven business landscape, the terms “data warehouse” and “Data Vault” are frequently tossed around. But what exactly are they, and why should businesses invest in them? This article aims to demystify these concepts, addressing common questions from a business perspective. We’ll delve into the reasons behind implementing a data warehouse or Data Vault, how to explain their value to non-technical stakeholders, and when companies typically start investing in these solutions.

Why Do We Need Data Warehouses and Data Vaults?

Before diving into the benefits of data warehouses and Data Vaults, let’s explore the challenges businesses face without them. Many traditional organizations grapple with:

• Limited Data Access: Data is often siloed, accessible only to specific departments, hindering cross-functional collaboration and insights.
• Lack of Structure: Ad hoc queries and a lack of standardized data processes lead to inefficiencies and unreliable results.
• Expensive Trial and Error: Decision-making based on incomplete or inaccurate data can be costly and time-consuming.
• Unreliable Data: Inconsistent data sources and ad hoc reporting can lead to errors and misguided decisions.

Data warehouses and Data Vaults address these challenges by providing a centralized, structured, and reliable repository for data. They enable:

• Data Integration: Combining data from various sources into a single source of truth supporting a comprehensive data strategy
• Enhanced Decision-Making: Empowering data-driven decision-making with accurate and timely insights.
• Historical Analysis: Enabling trend analysis and forecasting based on historical data.
• Improved Data Quality: Implementing data quality management processes to ensure accuracy and consistency.
• Scalability and Flexibility: Adapting to evolving business needs and data volumes.
• Auditability and Compliance: Maintaining data lineage and ensuring compliance with regulations like GDPR.

Explaining Data Vault to Non-Technical Stakeholders

When communicating the value of a Data Vault to commercial executives or non-technical stakeholders, it’s crucial to emphasize that it’s more than just a data model. Data Vault 2.0 is a comprehensive system of business intelligence, encompassing methodology, architecture, and modeling.

Highlight the key benefits Data Vault offers:

• Agility: Agile development methodologies enable quick responses to changing business requirements.
• Scalability and Flexibility: The architecture allows for seamless growth and adaptation.
• Consistency and Auditability: Data Vault ensures data accuracy, traceability, and compliance.

Use relatable examples to illustrate how Data Vault addresses specific business challenges. For instance, you could explain how it streamlines data integration from multiple sources, ensuring a single version of the truth for customer information.

When Do Companies Start Investing in Data Warehousing?

There’s no one-size-fits-all answer to this question. The ideal time to invest in data warehousing depends on several factors, including:

• Data Volume: The amount of data your company generates and the complexity of your data landscape.
• Business Needs: The extent to which your business relies on data for decision-making and operations.
• Strategic Goals: The importance of data-driven insights in achieving your company’s strategic objectives.

While larger enterprises with vast data volumes often invest in data warehouses early on, even smaller companies can benefit from them. Starting early, even with a smaller data warehouse, can be advantageous as it allows for gradual expansion and integration of external data sources as the business grows.

Conclusion

Data warehouses and Data Vaults are essential tools for businesses aiming to harness the power of their data. They address common data challenges, enable better decision-making, and offer a range of benefits that extend beyond mere reporting.

By understanding the key reasons for implementing these solutions and effectively communicating their value to stakeholders, you can build a strong case for investment and ensure that your organization reaps the rewards of a data-driven future.

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Why Hubs in Data Vault are Essential

Data Vault modeling is a powerful methodology for building robust and scalable data warehouses. One of its core components, the Hub, often raises questions among practitioners and stakeholders. Why do we need hubs? Can’t we just simplify the model by putting business keys directly into satellites? In this article, we delve into the reasons behind the existence of hubs and explore scenarios where deviating from the standard practice might be acceptable.

The Role of Hubs in Data Vault

Hubs play a pivotal role in Data Vault by storing a distinct list of business keys. These keys serve as unique identifiers for real-world entities, such as customers, products, or employees. Hubs provide several critical benefits:

1. 1. Data Integration: Hubs act as anchors for integrating data from disparate source systems. By consolidating different representations of the same entity into a single hub, you ensure consistency and accuracy across your data warehouse.
   2. Scalability: Hubs facilitate seamless scalability. When new data sources are introduced, you can simply add the business keys to the existing hub without the need for major model refactoring. This simplifies the onboarding of new data and reduces the risk of introducing inconsistencies.
   3. Auditability: Hubs maintain a clear lineage and audit trail for your data. The load timestamp in a hub functions as a “first seen” date, making it easy to track the evolution of your data over time.
   4. Granularity: Perhaps most importantly, hubs define the granularity of multiple downstream objects, including information marts and dimensions. This granularity is crucial for accurate reporting and analysis, making hubs indispensable for many use cases.

Why Not Put Business Keys in Satellites?

While hubs are generally considered best practice, there are rare instances where storing business keys in satellites might be justifiable. One such scenario is when a business key represents an entity that currently lacks descriptive data and is not actively queried.

For example, consider an employee dataset that includes the vehicle identification number (VIN) of the employee’s company car. If there’s no additional information about the car and no immediate need to query it, treating the VIN as a descriptive attribute within the employee satellite might be acceptable.

However, if the need to query or analyze data related to company cars arises in the future, a refactoring strategy called “Hub It Out” can be employed. This involves extracting distinct VIN numbers from the employee satellite into a new hub, creating links between the employee and car hubs, and potentially adding satellites with descriptive data about the cars.

Important Considerations about Hubs

While the above scenario demonstrates a valid exception, it’s crucial to remember that storing business keys in satellites should be the exception, not the rule. Hubs offer numerous benefits in terms of data integration, scalability, auditability, and granularity, making them essential for most Data Vault implementations.

Before deviating from the standard practice, carefully assess whether the potential benefits of storing business keys in satellites outweigh the potential drawbacks, such as increased storage costs, redundancy, and a less elegant data model.

Conclusion

In conclusion, hubs are fundamental building blocks in Data Vault modeling, providing a range of benefits that contribute to the overall integrity, scalability, and usability of your data warehouse. While there are rare cases where storing business keys in satellites might be justifiable, it’s crucial to carefully weigh the pros and cons before adopting this approach. By adhering to Data Vault best practices and understanding the specific requirements of your use case, you can ensure that your data warehouse is optimized for performance, maintainability, and long-term success.

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Calculating ROI for Data Warehouse and Data Vault Investments

Investing in a data warehouse or Data Vault is a strategic decision with the potential to transform how organizations leverage their data. However, quantifying the return on investment (ROI) for such projects can be challenging, as it often involves intangible benefits alongside measurable cost savings. This article delves into the key considerations for evaluating ROI in data warehousing and Data Vault initiatives.

Understanding the Dual Nature of Benefits

The benefits of data warehouse and Data Vault investments can be broadly categorized into two types:

1. Tangible Benefits: These are easily measurable outcomes, such as increased revenue, reduced costs, and improved efficiency. For example, a data warehouse project might lead to a significant increase in sales due to enhanced customer personalization or a reduction in storage costs due to optimized data management.
2. Intangible Benefits: These are less quantifiable but equally valuable outcomes, such as improved decision-making, enhanced customer satisfaction, and faster time to market. While these benefits are harder to measure directly, they play a crucial role in driving long-term value for the organization.

Evaluating ROI: A Two-Pronged Approach

To effectively assess ROI, consider both the tangible and intangible aspects of your data warehouse or Data Vault investment.

1. Quantifying Tangible Benefits

• Cost Savings: Identify areas where your data warehouse is reducing costs. This could include savings in data storage, data integration, or operational expenses due to automation.
• Revenue Generation: Explore how your data warehouse is contributing to revenue growth. This might involve improved customer service, new product development, or optimized pricing strategies.
• Efficiency Gains: Measure how your data warehouse has streamlined processes and reduced manual data handling, leading to time and resource savings.

2. Assessing Intangible Benefits

• Cost of Inaction: Flip the question and ask yourself, “What are the costs of not having a data warehouse?” This can help quantify the value of risk mitigation, improved decision-making, and faster response to market changes.
• Pain Point Valuation: Assign a monetary value to the pain points your data warehouse addresses. If your investment solves a customer satisfaction issue or a delivery time problem, estimate the financial impact of these improvements.
• Benchmarking: Compare your performance against industry benchmarks to gauge how your data warehouse is helping you achieve a competitive advantage.

Conclusion

Evaluating ROI for data warehouse and Data Vault projects requires a holistic approach that considers both tangible and intangible benefits. By carefully analyzing cost savings, revenue generation, efficiency gains, and the value of addressing pain points, you can build a comprehensive picture of the return on your investment. Remember that the true value of a data warehouse extends far beyond monetary gains, encompassing strategic advantages that drive long-term business success.

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As part of our ongoing Data Vault Friday series, our CEO, Michael Olschimke, engages with a relevant and practical question from our audience about multiple business keys in one source column.

Data Vault Business Keys

In the realm of Customer Relationship Management (CRM) and Master Data Management (MDM), integrating data from diverse systems is a common challenge. This is particularly true when dealing with external identifiers, such as social security numbers, VAT IDs, or passport numbers, which can also serve as business keys in other systems. This article explores how to effectively model such scenarios within a Data Vault framework to streamline data integration and data analysis.

Understanding the Challenge

When a CRM system doubles as an MDM platform, it often houses an “External Identifiers” entity. This entity stores relationships between customers and various external systems, encompassing both external identifiers (like social security numbers) and internal IDs (such as customer IDs in core systems).

The complexity arises when some of these identifiers also function as business keys in other systems, each with its own Data Vault hub. The goal is to combine customer-related data from different domains while maintaining the integrity of these relationships.

Modeling Strategies

1. Multi-Active Satellite: One approach involves modeling the “External Identifiers” entity as a multi-active satellite attached to the customer hub. This approach accommodates multiple keys or external identifiers linked to a single customer. By including the ID type (e.g., VAT, SSN) in the satellite’s descriptive data, you can distinguish between different identifier types within the group.
2. Joining Data: If a separate hub exists for a specific business key (e.g., VAT), you can directly join the data from the MDM system’s satellite to the corresponding hub. This approach facilitates data integration and enables easier queries.
3. Business Vault Links: To optimize join performance, especially with complex business keys or multi-column identifiers, you can create exploration or business links in the business vault. These links implement conditional logic, establishing connections between the customer hub and the relevant business key hubs based on the ID type.

Data-Driven Modeling

A data-driven modeling approach is essential in these scenarios. Start by capturing raw data from the source systems without applying business logic. In the raw Data Vault, treat business keys as descriptive fields within the satellite. Subsequently, in the business vault, you can implement the necessary business logic through links and relationships to integrate data from different domains effectively.

Hub It Out Pattern

The Hub It Out pattern, often used in refactoring, can also be applied here. If a new data set with descriptive data for a specific business key (e.g., corporate car VIN numbers) becomes available, you can extract those values from the existing satellite and create a new hub. Then, establish links between the customer hub and the new hub based on the existing relationships.

Considerations

• Hash Keys: Consider using hash keys for improved join performance, especially when dealing with complex or variable-length business keys.
• Data Virtualization: Where possible, virtualize data downstream from the raw Data Vault satellite to simplify the deletion of personal data.

Conclusion

In conclusion, integrating CRM and MDM data involving external identifiers that double as business keys requires a thoughtful modeling approach within the Data Vault framework. By leveraging multi-active satellites, joining data, creating business vault links, and adhering to a data-driven modeling philosophy, you can efficiently combine customer-related data from disparate domains.

Remember that the goal is to create a flexible and adaptable data model that caters to the evolving needs of your business. By employing these strategies and considering the specific requirements of your environment, you can unlock the full potential of your CRM and MDM systems, enabling seamless data integration and enhanced analytical capabilities.

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Virtualized Load End Date in Data Vault

In the world of data warehousing, optimizing query performance is crucial, especially in complex data models like Data Vault 2.0. One common challenge is dealing with virtualized load end dates in reference tables, which can hinder join elimination and impact query execution times. In this article, we delve into this issue and explore potential solutions to enhance query performance.

Understanding the Problem

The scenario involves joining dictionaries to satellite tables based on code attributes, where reference tables lack a point-in-time (PIT) table. This necessitates joining reference satellite entries, using a virtualized load end date, which prevents join elimination.

The problem lies in the complex join condition arising from the virtualized load end date calculation. This complex condition prevents the SQL optimizer from utilizing indexes effectively, leading to performance bottlenecks.

Solution Approaches

1. Snapshot Satellites

One solution is to implement snapshot satellites in the business vault. These satellites use the snapshot date as the timeline in their primary key, aligning with the granularity of outgoing information. This approach is efficient for handling different granularities in incoming and outgoing data and simplifies business logic implementation.

2. Materializing Reference Tables

Another option is to materialize the reference tables. While this can help with query performance, it introduces challenges when dealing with personal data, as deleting such data becomes more complex.

3. Consolidated Reference Table

Consider using a consolidated reference table to capture reference data for different domains with similar structures. This simplifies the data model and potentially keeps the table in memory, reducing disk access.

4. Extending the PIT Table

You could extend the PIT table of the relevant hub or dimension to include reference data. However, this increases redundancy and may not be suitable if numerous reference tables or attributes are involved.

5. Materializing Snapshot Satellite

Materializing the snapshot satellite is another alternative, especially if reference tables do not contain personal data. This simplifies data deletion when necessary.

6. Virtualization

If possible, consider virtualizing downstream data from the raw data vault satellite. This eliminates the need to delete personal data in materialized views, simplifying data management.

Additional Tips

• Limit reference satellites: In scenarios where only the latest snapshot is needed, limiting reference satellites to type 1 dimensions can simplify maintenance, although it restricts the availability of historical data.
• Partitioning: Partitioning reference and PIT tables by relevant codes or dates can improve query performance and storage management.

Conclusion

Optimizing query performance in Data Vault 2.0 requires careful consideration of various factors, especially when dealing with virtualized load end dates in reference tables. The solutions discussed in this article offer different approaches to tackle this challenge, each with its pros and cons. Choosing the most suitable approach depends on the specific requirements of your data warehouse environment.

By implementing these solutions and following the additional tips, you can enhance query performance, improve data management, and ensure the efficient delivery of information in your Data Vault 2.0 environment.

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In this week’s Data Vault Friday, Michael Olschimke, our CEO, addresses a thought-provoking question about join elimination in SQL Server, specifically concerning PIT (Point-in-Time) tables in Data Vault. Let’s explore this topic in depth.

Understanding PIT Tables and Their Role in Data Vault

In Data Vault 2.0, PIT tables are crucial for efficient data retrieval. They are designed to reference the most recent changes (deltas) in satellite tables linked to a hub or link. This setup is instrumental in creating dimension tables that reflect a snapshot of your data at a specific point in time.

The Query at Hand

The question we received revolves around the challenge of optimizing queries using PIT tables. The scenario is familiar: you’ve built dimensions from PIT tables, which join to satellite entries valid at the snapshot time. However, when users query only a few attributes, unnecessary joins to multiple satellites can significantly slow down performance. The goal is to ensure join elimination works effectively, allowing the SQL optimizer to bypass unnecessary joins and thus speed up query execution.

The Join Elimination Dilemma

Join elimination is a technique where the SQL optimizer skips joins that aren’t needed for the final result. This is particularly useful in data warehousing scenarios where large dimension tables are involved. In SQL Server, achieving efficient join elimination, especially with PIT tables, involves several considerations:

Inner Joins vs. Left Joins

Inner joins are commonly used but can be problematic because SQL Server requires foreign keys between tables for optimal join elimination.
Left joins, on the other hand, can sometimes make join elimination easier for the optimizer, as they are less restrictive.

Foreign Keys

For SQL Server to perform join elimination efficiently, there must be a foreign key relationship between the PIT table and the satellite tables.
This foreign key doesn’t have to be active but must exist to inform the optimizer about the relationships.

Column Considerations

Ideally, the foreign key should be based on a single column. In Data Vault, where primary keys often consist of composite keys (e.g., hash key and load date), this can complicate matters.

Practical Steps for Efficient Join Elimination

Here are some practical steps to ensure join elimination works effectively in SQL Server:

Use Left Joins

By using left joins instead of inner joins, you can simplify the optimizer’s task. In many cases, left joins with equi join conditions perform as efficiently as inner joins.

Implement Foreign Keys

Ensure that foreign keys exist between your PIT tables and satellite tables. Even if these keys are inactive, they provide the necessary metadata for the optimizer.

Simplify Foreign Key Structures

Where possible, simplify the foreign key structures to single columns. This can help the optimizer in performing join elimination more efficiently.

Testing and Verification

Given the complexity of join elimination, thorough testing is essential. Ensure that your SQL Server setup supports the required features and verify the execution plans to confirm that unnecessary joins are indeed being eliminated. Tools like SQL Server Profiler and Execution Plan Viewer can be invaluable for this purpose.

Tailoring Information Marts for Performance

Another critical aspect is the design of your information marts. Traditional data warehousing often involved creating large, comprehensive information marts. However, in the Data Vault paradigm, it’s more efficient to create smaller, purpose-specific marts. Here’s why:

Performance Optimization

Smaller marts tailored to specific reports or queries minimize the amount of data processed, thus speeding up query execution.

Virtualization

By virtualizing information marts in SQL Server, you can dynamically assemble the necessary data without physically storing it, reducing storage requirements and increasing flexibility.

Conformed Dimensions

When dimensions are based on the same PIT tables, they are inherently conformed, allowing for seamless joins across different information marts.

Conclusion

Join elimination is a powerful technique for optimizing queries in SQL Server, especially when working with Data Vault 2.0 and PIT tables. By using left joins, implementing foreign keys, and simplifying key structures, you can enhance the performance of your data retrieval processes. Additionally, designing smaller, purpose-specific information marts tailored to specific use cases can further boost efficiency.

If you have more questions or need further clarification, feel free to use the form at https://sfr.ee/DVFriday to submit your queries. You can also join our monthly webinars on WhereScape and dbt.

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Reference Data

Welcome to another episode of Data Vault Friday! I’m Michael Olschimke, CEO of Scalefree. Today’s question comes from our online form, and it’s about deriving dimensions from reference data in the raw Data Vault. Specifically, the questioner has several lookup reference tables that they add as Hub or reference tables while creating the raw Data Vault. For example, they have a region table that includes a region code, description, language code, and valid from/to dates.

Understanding Degenerate Dimensions

The query mentions a “degenerated dimension.” To clarify, a degenerate dimension is a dimension attribute, such as the region code, included in a fact table without any additional descriptions. This attribute exists within the fact table itself and doesn’t have a separate dimension table.

Building the Model

To illustrate this, let’s start with a basic structure. Imagine you have a non-historized link containing transaction data, and it references Hubs such as Hub Customer and Hub Product. Additionally, you have reference tables, like a region reference table with a region code and associated descriptions. Here’s a simplified model:

1. Non-historized Link: Contains transaction data.
2. Hub Customer and Hub Product: Reference customer and product data.
3. Reference Hub for Region: Contains the region code.
4. Reference Satellite for Region: Contains the descriptions, language codes, and valid dates.

This setup allows for capturing changes in reference data, making the model auditable and maintaining historical accuracy.

Creating a Degenerate Dimension

To create a degenerate dimension from the reference data, follow these steps:

1. Include the Code in the Fact Table: Add the region code directly into your transaction data (the non-historized link).
2. Determine the Required Attributes: Decide if you only need the region code or additional attributes like the region name.
3. Create a Fact View: If you only need the region code, simply create a fact view that includes this code.
4. Prejoin Additional Attributes: If you need additional attributes, prejoin the reference Hub and Satellite to get the region name or other details based on the timeline of your facts.

Handling Time-Based Data

When dealing with time-based data, it’s essential to identify the correct version of your reference data. If you want the latest description of the region (a Type 1 dimension), you can join the latest entry. For a Type 2 dimension (tracking changes over time), join based on the fact timestamp to match the correct version of the region name.

Performance Considerations

Reference tables typically contain a relatively small amount of data, which allows most joins to be efficient. However, if performance becomes an issue, you can consider creating a Point-in-Time (PIT) table in the Business Vault. This table can precompute the current description for each region on a daily basis, making joins faster and more efficient.

Conformed Dimensions

If you prefer to use a conformed dimension, convert your reference table into a dimension table. Use the primary key of the reference table (e.g., region code) as the dimension identifier. This approach involves joining the reference Hub and Satellite to create a dimension view that can be used in your fact tables.

Implementation Steps

1. Turn Reference Table into Dimension: Join the reference Hub and Satellite to create a dimension view.
2. Use Reference Code as Dimension Key: The region code becomes the dimension key.
3. Create Fact View: Include the dimension key in your fact view and join the necessary attributes from the dimension view.
4. Configure in Dashboard: Set up relationships between your facts and dimensions in your dashboard application for seamless data visualization.

Conclusion

In summary, deriving dimensions from reference data in a Data Vault involves understanding your needs for degenerate or conformed dimensions, handling time-based data appropriately, and ensuring efficient joins. By following these steps, you can create a robust and scalable data model that meets your analytical needs.

Thank you for joining us for this Data Vault Friday session. If you have more questions, submit them at sfr.ee/dvfriday. For additional learning, check out our webinars at Scalefree.to/webinars. If you need answers before next Friday, visit the Data Vault Innovators community we set up with Ignition Data.

Until next time, keep those data questions coming, and we’ll see you next Friday!