What is a modern data architecture? Opinions vary widely. Some define it by the adoption of the latest tools in a modern data stack architecture, while others argue it should be judged by how reliably it supports business-critical data flows and decision-making.
From a technology perspective, the market’s direction is clear. Tristan Handy, Founder and CEO at dbt Labs, points to two dominant vectors shaping modern data engineering:
And so now the big axis of innovation, I think, is in two places. One is in open standards, things like Delta and Iceberg, that’s at the file format or the table format level. And then the other one, obviously, is in AI.
But technology momentum is colliding with a less mature data reality inside most organizations:
- 83% of companies cite data integration challenges as a major barrier to legacy modernization.
- 63% are unsure whether their data management practices are sufficient for AI adoption.
- 60% of AI initiatives are expected to fail through 2026 due to a lack of AI-ready data.
Moving toward lakehouses, open formats, or AI-driven analytics without well-organized, governed datasets often amplifies existing problems rather than solving them. In practice, enterprise data architecture patterns must evolve in step with data maturity, organizational readiness, and business priorities.
Dylan Anderson, a Head of Data Strategy at Profusion, gives the following definition and warns his audience against overcomplicating the concept of a data platform:
A data platform is a generic, catch-all term that encompasses the many technologies that underpin the process of making data accessible to business users, leading to better decision-making and insights.
In his Substack article, Dylan also highlights that the core purpose of a data platform is to help businesses make sense of their data, an important lens when choosing the best data platform for enterprise needs.
Data maturity assessment: The first step before building a data platform
The first step is to assess the correlation between your business performance and the condition of your data infrastructure. Ideally, you would need a detailed list of questions to ask your data engineering team, grouped by sections (from financial to operational).
Question examples:
- How many distinct data storage systems exist in our organization? (1-5 / 6-15 / 16-30 / 30+)
- How many data sources and data pipelines feed our analytics environment? (< 10 / 10-50 / 50-100 / 100+)
Honest answers to the right questions help determine whether the organization is mature enough for advanced architectures such as a lakehouse, or whether foundational steps, such as legacy data warehouse replacement or consolidation, should come first. Common data maturity assessment frameworks, such as DAMA DMBOK2 and DCAM, define five levels of data maturity, ranging from ad hoc/reactive to optimized/strategic data management.
| Stage | Typical name(s) | What it means |
|---|---|---|
| Level 1 | Initial / Ad Hoc | Data practices are informal, inconsistent, and reactive |
| Level 2 | Managed / Repeatable | Basic standards and processes exist, but are applied unevenly |
| Level 3 | Defined / Coordinated | Organization-wide standards with documented processes |
| Level 4 | Proactive / Quantitatively Managed | Metrics & monitoring drive decisions; data quality is measured |
| Level 5 | Optimized / Strategic | Data is integrated into strategy, predictive, and automated workflows |
On each level, there should be a different data platform development roadmap. For level 1, it might be necessary to create an inventory of data sources and business datasets as a basic data platform. On level 2, it might be efficient to develop a central data warehouse for cross-company data consolidation. Whereas levels 3, 4, and 5 provide a solid foundation for enhancing your data platform with new capabilities, such as increasing storage capacity or tapping into advanced or AI-powered analytics.
Data warehouse vs data lake vs lakehouse: Architecture comparison
At the heart of the enterprise data platform architecture lies centralized data storage, which provides an organization with access to consolidated business data, enables cross-company analytics, and powers decision-making.
We’ve compiled a detailed table outlining the core characteristics of each data storage type, including cloud data warehouse selection criteria, data lake implementation specifics, and data lakehouse features.
| Dimension | Data warehouse | Data lake | Lakehouse |
|---|---|---|---|
| Primary purpose | High-performance analytics and BI on curated data | Low-cost storage for raw, semi-structured, and unstructured data | Unified analytics, BI, ML, and AI on governed data |
| Typical data types | Structured, schema-on-write | Structured, semi-structured, unstructured (schema-on-read) | Structured and semi/unstructured with table semantics |
| Storage layer | Proprietary managed storage | Object storage (S3, ADLS, GCS) | Object storage with open table formats |
| Table semantics (ACID) | Native, strong ACID | None by default, BASE | Yes (via Iceberg/Delta/Hudi) |
| Schema management | Strict, predefined schemas | Flexible, often inconsistent | Flexible with enforced schemas and evolution |
| Query performance | Excellent for SQL/BI workloads | Variable; depends on engine and optimization | Near-warehouse performance with proper optimization |
| Concurrency | High (designed for many BI users) | Limited without additional layers | High with modern engines and caching |
| BI & reporting | Best-in-class | Requires extra layers/tools | Strong; supports BI directly on lake data |
| ML/AI workloads | Limited, indirect | Strong (raw and feature engineering) | Strong (shared data for BI, ML, and AI) |
| Governance & security | Built-in, mature | External tooling required | Centralized governance via catalogs |
| Data lineage & discovery | Native | External tools required | Native or catalog-driven |
| Interoperability | Low (vendor-specific) | High (open files) | High (open tables and multiple engines) |
| Cost model | Higher, predictable, vendor-managed | Lowest storage cost, hidden ops cost | Lower storage cost and compute-based pricing |
| Vendor lock-in risk | High | Low | Medium-low (depends on catalog/engine choice) |
| Common failure mode | Too rigid, expensive at scale | “Data swamp” with poor quality | Over-engineering without governance discipline |
| Best fit | BI is dominant, and data is stable | Flexibility and raw data access matter most | You need one platform for BI, ML, AI, and sharing |
Data warehouse: When structured analytics and BI workloads dominate
A modern data warehouse is a well-organized, centralized data storage for storing structured historical data from the entire organization. The main purpose of this storage is data integration from multiple sources to enable online analytical processing (OLAP) for data analytics, business intelligence, and reporting. Data warehouses maintain ACID transactions (atomicity, consistency, isolation, durability) to ensure that data is stored and transferred safely.
Another common concept is an enterprise data warehouse (EDW), which provides enterprise-wide data storage for comprehensive analytics.
For instance, in the healthcare industry, an EDW (e.g., Amazon Redshift) consolidates data from multiple sources, such as electronic health record (EHR) systems, picture archiving and communication systems (PACS), and laboratory information systems (LISs). The centralized warehouse then applies consistent schemas, business logic, and governance controls, enabling reliable analytics across clinical outcomes, resource utilization, and financial performance, capabilities that are difficult to achieve when data remains fragmented across operational systems.
A data warehouse is the oldest form of centralized data storage, and some claim that it’ll soon become obsolete. But here’s what Bill Inmon, a famous computer scientist and the “father of the data warehouse”, wrote on the matter:
So when does data warehouse die? Data warehouse dies whenever the corporation does not need to look at enterprise data. Come the day when marketing, sales, finance and accounting do not need to look across the enterprise and understand what is going on in the corporation, that is the day when data warehouses are not needed.
A data warehouse remains a core component of many enterprise data architecture patterns, especially where governance, consistency, and BI performance are critical.
When to choose: Consistent data workflows are a priority, and BI is the core data analytics solution.
Data lake: Flexibility for unstructured data and advanced analytics
The data lake emerged to address limitations of the data warehouse, such as the inability to store growing volumes of unstructured and semi-structured data from social media, IoT devices, third-party services, and server logs. A data lake (e.g., Amazon S3) allows storing vast amounts of data of different types in a single source of truth without the need to transform the data first, as was necessary in a data warehouse.
With the advent of the data lake, it became common to store data in the cloud as volumes grew and storage costs rose. At this point, object data storage emerged, allowing companies to “dump” their enterprise data and figure out later what to do with it.
Unlike ACID compliance of the data warehouse, a data lake follows the BASE (basically available, soft state, and eventually consistent) principle, which prioritizes data availability over consistency. This principle largely led many data lakes to become “data swamps” filled with raw, poorly queryable data. That’s why companies couldn’t fully abandon their well-structured data warehouses and switch entirely to easily scalable, yet disorganized, data lakes.
When to choose: If data volume is constantly increasing and cost-efficient object storage is the priority.
Data lakehouse: Unified architecture for AI-ready enterprises
When Databricks coined the term “lakehouse”, they promised to deliver the data warehouse’s performance and ACID compliance alongside the data lake’s flexibility. An engineering community is certain that they delivered upon the promise. The introduction of open table formats for metadata management, such as Apache Iceberg, Apache Hudi, and Delta Lake, created an opportunity for data warehouse-like data querying while providing vast storage for raw data, as in data lakes.
Even though many companies can use data warehouses and data lakes together, lakehouses are more cost-efficient because they eliminate duplicate data, optimize storage, and reduce data ingestion latency across systems. Due to these benefits, 67% of business leaders plan to run all their analytics on data lakehouses within the next three years.
When to choose: This architecture decreases time-to-insight and is considered a better option for AI/ML workloads. In fact, 85% of organizations use data lakehouses to support their AI development initiatives. But you can cooperate with a data lakehouse implementation partner if you need an all-in-one platform and have a data engineering capacity to set it up.
You don’t have to limit yourself to one solution; you can even combine all three data platform architecture patterns if business goals justify it and the data infrastructure allows.
In general, each data storage platform serves the same purpose: to ensure your data is easily accessible for analytics. The differences appear once we ask how quickly this data becomes available and how to prepare it.
Technology stack selection: Databricks, Snowflake, and BigQuery
We’ve written a detailed guide on data platform vendor evaluation. In this section, we’ll provide a more general overview, focusing on the most recent feature developments (to gauge each company’s innovation pace), core use cases, and real-life ROI examples.
BigQuery vs Databricks vs Snowflake comparison
| Dimension | Snowflake | BigQuery | Databricks |
|---|---|---|---|
| Primary architectural goal | Make analytics consumption simple, governed, and scalable | Remove infrastructure management from analytics entirely | Unify data engineering, analytics, and AI on one platform |
| TCO dynamics (in practice) | Predictable, but can grow with concurrency and data duplication | Very cost-efficient at scale, but requires discipline around query patterns | Potentially lower long-term TCO for AI-heavy workloads, higher ops responsibility |
| Cost risk profile | Over-provisioned virtual warehouses and always-on workloads | Poorly optimized SQL, excessive scans, careless joins | Inefficient Spark jobs, oversized clusters, weak workload isolation |
| Operational ownership model | Analytics team–owned, minimal platform engineering | Central analytics team with light platform ops | Requires a true data platform/platform engineering function |
| Time to first value | Fast for analytics and dashboards | Very fast for centralized analytics | Slower upfront, faster payoff at scale |
| Organizational maturity fit | Mid → high maturity analytics orgs | Early → mid maturity or cloud-native orgs | Mid → advanced data & AI maturity |
Databricks: When AI/ML workloads drive architecture decisions
The Databricks Data Intelligence Platform is a data lakehouse solution that not only consolidates enterprise data but also offers a wide range of AI/ML processing and analytics capabilities. One of the Gartner reviews sums up what the platform offers and what its limitations are:
DB delivers an outstanding unified lakehouse that lets engineering, BI, and ML teams work from the same governed data, cutting pipeline sprawl and hence speeding up projects. Performance is excellent on Apache Spark, clusters spin up fast, and support has been consistent in response and knowledge. Caveat: steep learning curve for newcomers and tight control on costs.
Unification has its costs, as it makes the platform difficult to manage and can lead to accumulated expenses as data processing capacity increases.
Recent features
Databricks continues to expand beyond traditional analytics and data warehousing solutions toward a unified AI and data platform. The company has recently introduced Agent Bricks (a no-code AI agent builder), Lakebase (a serverless transactional database for processing more than 10,000 queries per second), and enhanced integrations with OpenAI and Anthropic models to support AI-centric workloads directly within the platform.
Use cases
- Large-scale data engineering and transformations with Delta Lake and Apache Spark integration.
- Integrated AI/ML pipelines (feature engineering, model training/serving) leveraging unified compute and storage.
- For business cases, where advanced analytics and AI workflows should co-exist with traditional reporting.
ROI example
After surveying multiple Databricks clients, Nucleus Research’s findings confirm that Databricks delivers a 482% ROI over three years, with a four-month payback period. Surveyed companies also admit a 52% reduction in time-to-production of their data and AI projects.
Snowflake: SQL engine powered with AI capabilities
Snowflake is a unified data platform that integrates with Apache Iceberg and Delta Lake for flexible data management and to help enterprises avoid vendor lock-in. Similar to Databricks, Snowflake supports multiple cloud providers, including GCP, AWS, and Azure.
Recent features
Snowflake’s AI Data Cloud continues to evolve with innovations showcased at Snowflake Summit 2025. These include advances in AI-ready capabilities, enhanced ingestion options, and governed data sharing across organizations.
The partnership between Snowflake’s Cortex AISQL and Anthropic supports agentic AI workflows directly inside Snowflake’s secure data cloud, enabling natural-language analytics and autonomous insights.
Use cases
- Enterprise BI and reporting, which require high concurrency and predictable performance.
- Secure data sharing across organizational boundaries through Snowflake Marketplace and private data exchanges.
- SQL-centric analytics teams seeking a managed platform with minimal operational overhead.
- Organizations that prioritize data governance and compliance with built-in access controls and audit capabilities.
ROI example
Pfizer switched from multiple fragmented data storage systems, which included several data lakes, legacy databases, and scattered files across workspaces and systems, to Snowflake. As a result, they achieved 57% in TCO savings, cut compute costs by 28%, and increased the pace of analytics by four times.
BigQuery: GCP-native AI data platform
Google positions BigQuery as an autonomous data and AI platform that automates the data lifecycle from ingestion to AI. Features include built-in AI integrations (e.g., Gemini in BigQuery) and BigQuery ML for in-warehouse machine learning.
Recent features
BigQuery now supports managed AI functions that allow users to embed AI capabilities directly within SQL workflows for richer analytics and inference.
Plus, Earth Engine in BigQuery became generally available, enabling satellite and geospatial data integration for advanced analytics directly in BigQuery.
Use cases
- Organizations already invested in Google Cloud Platform seeking seamless integration with other GCP services such as Vertex AI, Looker, and Cloud Storage.
- Analytics teams that require serverless, pay-per-query pricing without managing compute resources.
- Companies processing large-scale geospatial data, leveraging BigQuery’s native GIS functions.
- Marketing and advertising analytics, particularly for organizations using Google Ads and Google Analytics data.
ROI example
Stanford University migrated its research data infrastructure to BigQuery and Google Cloud, consolidating previously siloed datasets across departments. The migration reduced query times from hours to seconds for complex genomics research workloads, enabling researchers to iterate on hypotheses faster. Stanford reported a 60% reduction in infrastructure management overhead.
Selecting the right platform is only part of the equation. Many organizations face the more immediate challenge of transitioning from legacy infrastructure to these modern platforms. The migration path (e.g., data lakehouse or data warehouse migration services) you choose can determine whether you realize platform benefits within months or years.
Migration strategies for legacy data platforms
Data platform migration is a challenging but ultimately rewarding step an organization should take if their data management issues are stalling growth. For instance, 41% of organizations have migrated from data warehouses to data lakehouses, and 23% from legacy data lakes.
Typically, migrations cover:
- data warehouse → cloud warehouse
- data lake → data lakehouse
- Snowflake ↔ BigQuery ↔ Databricks
- legacy → modern platform
General migration strategies that would fit any of them are:
- Lift-and-shift. Move data and schemas with minimal transformation.
- Phased migration. Migrate workloads, domains, or use cases one by one while old and new platforms run in parallel.
- In-place modernization. Modernize storage or table formats without copying all data (e.g., registering existing data into new table formats).
- Workload-based migration. Migrate by workload type (e.g., BI first, then ML; historical data first, then streaming; read-heavy workloads before write-heavy ones)
- Schema-first vs data-first migration. Schema-first: migrate models, then data. Data-first: migrate raw data, remodel later.
- Domain-driven migration. Migrate data by business domain (sales, finance, operations, product).
- Cold data vs hot data split. Migrate historical (“cold”) data differently from actively used (“hot”) data.
- Re-platform and optimize. Redesign models, pipelines, and governance during migration.
| Migration strategy | Why choose it |
|---|---|
| Lift-and-shift | Fastest migration with minimal change |
| Phased migration | Lowest risk, business continuity |
| In-place modernization | Avoid data duplication, reduce cost |
| Workload-based migration | Prioritize high-value workloads |
| Schema-first / data-first | Control vs flexibility trade-off |
| Domain-driven migration | Clear ownership and accountability |
| Cold vs hot data split | Faster ROI, lower migration cost |
| Re-platform and optimize | Long-term efficiency and scale |
The optimal strategy depends on your starting point, risk tolerance, and resource constraints. Organizations with mature data governance and documented pipelines often succeed with phased migration, maintaining business continuity as they progressively shift workloads. Companies facing urgent cost pressures or end-of-life deadlines may need to lift and shift to exit legacy platforms quickly, accepting technical debt that must be addressed post-migration.
Governance and compliance requirements: Building compliant data architectures
Data breaches increased by 22% year over year in 2025, with GDPR fines reaching a staggering €1.2 billion. These figures highlight a growing gap between how fast organizations deploy AI and how well their data architectures control access, usage, and accountability. AI systems amplify risk by replicating data across training pipelines, inference layers, and automated decision workflows, often faster than governance controls can keep pace.
Governance and compliance are not the same thing. Governance defines who can access data, for what purpose, and under which conditions. Compliance is the ability to prove that those rules meet regulatory requirements (GDPR, HIPAA, PCI DSS). When embedded into the data architecture by design, through classification, fine-grained access control, lineage, and auditability, even large, previously ungoverned data lakes can be transformed into secure, compliant platforms.
Secure data architectures enforce these controls at runtime. They include centralized logging, monitoring, and audit trails to detect anomalies and support investigations, along with consistent encryption, masking, and data minimization to limit exposure of sensitive information.
Bottom line
Your data platform decisions should be driven by your business model. If your data is siloed, fragmented, and of poor quality, adopting the most advanced lakehouse architecture will not solve the underlying problems. You will simply have a more expensive platform containing the same unreliable data.
Whether you are modernizing a legacy warehouse, implementing your first lakehouse, or optimizing an existing platform, the principles remain consistent. Align architecture to business needs. Invest in governance and quality. Build for the AI-enabled future. And never lose sight of the ultimate purpose: turning data into decisions that drive your business forward.
