As organizations accelerate digital transformation, managing data effectively becomes a strategic imperative. CIOs and data leaders face mounting pressure to modernize data ecosystems for agility, compliance, and AI-driven insights. For those navigating enterprise resource planning (ERP) migrations and modernization initiatives, one question often arises: How do we preserve historical data while enabling advanced analytics and artificial intelligence (AI)?
The answer lies in understanding the evolving roles of data warehouses, data lakes, and data lakehouses.
Most organizations find they need elements of all three. Data warehouses provide trusted, fast reporting (often critical for auditors and executives), data lakes provide a sandbox for vast raw data (fueling innovation), and data lakehouses aim to bridge these needs in one system. This convergence is especially valuable during ERP migrations or digital transformation, when companies desire to preserve legacy (historical) data while adopting modern, AI-ready analytics. Leveraging a unified data architecture can help mitigate compliance risks and boost analytics capabilities.
Why Reviewing Data Architecture Matters
Despite the rise of hybrid technology solutions, understanding the strengths and limitations of each approach is critical for informed decision making. Regulatory compliance, scalability, and AI readiness hinge on having a solid foundation.
Data architecture can be a strategic lever for growth.
A fragmented data strategy can lead to:
- Compliance risks during audits
- Higher operational costs from legacy systems
- Limited analytics capabilities that slow decision making
A unified approach can help improve:
- Business continuity during ERP migration
- Predictive insights for strategic planning
- Scalable architecture for future growth
Data Warehouse vs. Data Lake vs. Data Lakehouse
Modern platforms increasingly blend features of all three. Rather than viewing data warehouses and data lakes as mutually exclusive, leading solutions often combine these, sometimes using a data lake as the back-end for a warehouse.
Modern platforms like Microsoft Fabric, Azure Synapse Analytics, and Databricks use cloud data lake storage as their underlying data layer, often leveraging the Delta Lake table format for ACID transactions and performance. ACID transactions are a set of properties that help ensure database transactions are processed reliably, confirming atomicity, consistency, isolation, and durability. Snowflake also separates storage from compute and uses cloud object storage as its back-end (essentially a managed data lake), but it uses a unique format instead of Delta Lake.
Data Architecture Comparison
| Feature | Data Warehouse | Data Lake | Data Lakehouse |
|---|---|---|---|
| Governance | Mature frameworks; enhanced with tools like Purview | Requires governance layer, e.g., Unity Catalog | Integrated governance baked into architecture |
| Cost | Varies by workload and indexing; higher for large scale | Low storage cost; compute costs depend on query patterns | Balanced; combines low-cost storage with optimized compute |
| Scalability | Scales well for structured/semi-structured data | Highly scalable if paired with an appropriate lakehouse | Scalable across structured and unstructured data |
| AI Readiness | High when paired with machine learning (ML) frameworks and modern tools | High when paired with an appropriate lakehouse | High; designed for unified analytics and ML |
| Ideal Use Cases | BI, compliance reporting, ERP analytics | Storage of any amount of any type of data, either for current use or archiving | Unified analytics, advanced AI/ML, multi-format data |
The Rise of Lakehouse Architecture
Lakehouse architectures have become essential to the modern data platform because of the flexibility they provide. The lakehouse design can provide unlimited data storage, powerful compute, vast interconnectivity, and strong data governance options. It can be used in an on-demand pattern where compute and costs are reduced except when needed, or it can fill the traditional always-available role of a SQL warehouse. Importantly, leading lakehouse options can execute both traditional SQL code and Python code, opening up vast AI/ML capabilities.
This evolution directly addresses a critical CIO challenge: balancing the flexibility needed for AI and ML workloads with the governance and speed required for BI. Lakehouses are designed to support efforts to break down data silos, align data engineering with analytics, and build scalable architecture for advanced use cases, while helping reduce complexity and cost.
Two widely used platforms in this space include Microsoft Fabric and Databricks.
Technical Insights
- Data warehouses and data lakes are not simply resources to compare and contrast; they are tools that can be used separately or combined.
- The distinction between “structured” and “unstructured” is increasingly blurred. If you create a Delta Lake and use Databricks or Azure Synapse Analytics, your data can be structured and governed.
- Performance depends on efficient indexing, choosing the correct storage types, e.g., row versus columnar storage, and proper hardware allocation.
- Governance features like Databricks Unity Catalog and Microsoft Purview should be recognized.
- Both Microsoft Fabric and Databricks support Python, Scala, and advanced data engineering; their capabilities are more similar than different.
Key Considerations
- Consider Microsoft Fabric if you are heavily invested in the Microsoft Azure ecosystem and benefit from the pre-built connections between Microsoft Fabric and other Azure resources.
- Consider Databricks if you are looking for a combination of developer-friendly tools, time-tested capabilities, and cloud/tool independence.
- Both platforms offer robust data engineering and analytics; differences are often in ecosystem alignment and governance approach.
Further, recent advancements from Databricks and Microsoft enable hybrid transactional and analytical processing within the lakehouse architecture, which traditionally focused on analytical workloads. This allows organizations to run applications and perform real-time analytics on the same data. These advancements can help bridge the gap between transactional and analytical systems.
Preserving Legacy Data During ERP Migration
While Microsoft Fabric and Databricks illustrate lakehouse unification in a general sense, Solver’s Data Warehouse is a more specialized technology solution tailored for financial and operational reporting, particularly in the context of ERP. Solver’s Data Warehouse is a component of Solver’s extended performance management suite, often used alongside ERP and accounting systems. It provides a practical example of how a unified data approach can help ease ERP migrations.
Migrating from legacy ERP systems like Microsoft Dynamics GP to Dynamics 365 Business Central often creates fragmented historical data. This is where Solver’s Data Warehouse can become critical by providing:
- Centralized Data Preservation: Can consolidate historical data from multiple sources into a secure, unified repository, supporting compliance and accessibility for audits and trend analysis.
- Advanced Analytics & Decision Support: Integrates with Power BI and Solver’s planning tools to deliver automated reporting, predictive insights, and role-based dashboards.
- Seamless Modernization: Can act as a bridge during ERP migration, maintaining continuity for strategic planning while supporting modernization initiatives.
Potential Benefits
- Reduce migration risk
- Maintain audit readiness
- Enable faster, more accurate forecasting
A structured data warehouse may be an ideal way to merge legacy and current data for accurate, meaningful reporting.
Dimensions: Driving Efficient Reporting
Dimensions, such as accounts, customers, and regions, are the backbone of efficient reporting. Solver enables up to 500 dimensions, supporting advanced customization and cascading filters.
Potential Benefits
- Faster reporting cycles for finance and operations
- Improved decision agility through updated data models
- Scalable architecture for future growth
Strategic Takeaways for CIOs
Modern enterprises thrive on continuity. Consolidating historical and real-time data in the cloud is essential for generating predictive insights that guide strategic choices.
To achieve both scalability and performance, lakehouse architectures such as Microsoft Fabric and Databricks offer a powerful path forward. These platforms can help bridge the gap between structured and unstructured data, enabling flexibility without sacrificing speed. Importantly:
- Optimization Matters: Performance in both warehouses and lakehouses depends on efficient indexing, preferred storage type, e.g., row versus columnar storage, and proper hardware allocation, not just architecture labels.
- Feature Parity: Microsoft Fabric and Databricks share core capabilities. Both support Spark-based processing, Python, Scala, and advanced data engineering. Differences often lie in ecosystem alignment (Microsoft-centric versus multi-cloud) rather than fundamental functionality.
- Governance: Tools like Databricks Unity Catalog and Microsoft Purview can provide enterprise-grade governance.
When migrating ERP systems, risk and compliance shouldn’t be afterthoughts. Tools like Solver can help mitigate migration challenges, supporting financial integrity and regulatory alignment throughout the process.
Lastly, governance and integration should be front and center. Strong governance and seamless integration safeguard business agility and allow organizations to pivot smoothly as technology and priorities evolve.
Features to Consider
- Cost vs. Agility: Consider the total cost of ownership, not just storage.
- Security & Compliance: Confirm governance frameworks align with regulatory requirements.
- AI Readiness: Position architecture for predictive analytics and generative AI.
How Forvis Mazars Can Help
As an award-winning, certified partner of both Microsoft and Solver Global, professionals at Forvis Mazars can help CIOs design and implement data strategies that drive business value. Our consultants can help you:
- Architect data warehouse and lakehouse solutions tailored to your business goals
- Integrate Solver for unified reporting and planning
- Deploy Microsoft Fabric or Databricks for next-generation analytics
Ready to modernize your data strategy? Connect with us today to request a demo and learn how we can help you unlock the full potential of your data.
Related Content
- Case study – Streamlining Accounting Processes: How NEC Financial Services Uses Business Central & Solver
- Video – Business Central With Solver xFP&A: Transforming Reporting
- Using a Data Warehouse to Merge History & Current Data
- Let Dimensions Drive Reporting in Solver
- Using Solver as a Data Source for Power BI
- Modern Data Architecture: Mine, Refine, & Turn Data Into Digital Gold