You should move from a data warehouse to a lakehouse when your current architecture can no longer handle the variety, volume, or velocity of data your organisation produces — particularly when unstructured data, machine-generated sensor streams, or real-time processing requirements start pushing against the boundaries of what a traditional warehouse does well. For most data engineering teams in manufacturing, energy, and agritech, that tipping point arrives not all at once but through a series of compounding frustrations. The questions below unpack each of those signals and help you decide whether a migration makes sense for your situation right now.
What are the key signs your data warehouse is holding you back?
Your data warehouse is holding you back when the cost and complexity of forcing all your data into structured, schema-on-write tables outweigh the analytical value you get in return. The most common indicators are rising storage and compute costs, growing query latency on large datasets, and an engineering team that spends the majority of its time transforming raw data just to make it warehouse-compatible rather than delivering insights.
Beyond performance, watch for these practical warning signs:
- Your data engineers spend most of their time on ingestion and cleaning rather than analysis or modelling
- Semi-structured or unstructured data — JSON logs, images, sensor streams — is either excluded entirely or forced through expensive transformation pipelines
- Business units have started maintaining their own exports and shadow datasets because they no longer trust the central numbers
- Pipelines break overnight and nobody notices until a senior stakeholder flags an incorrect figure in a report
- Your ML team is waiting on clean, accessible data that never quite arrives in a usable form
If three or more of these resonate, your warehouse architecture is likely constraining your data engineering capacity rather than enabling it. Exploring dedicated data and software services can help you identify where the real bottlenecks lie and what a more scalable architecture could look like.
What is the difference between a data warehouse and a lakehouse?
A data warehouse stores structured, pre-processed data in a schema-on-write model optimised for SQL analytics and business intelligence. A lakehouse combines the low-cost, flexible storage of a data lake with the data management and query capabilities of a warehouse — allowing you to store raw data in open formats and apply structure at query time rather than at ingestion time.
The practical distinction comes down to three dimensions:
Storage and schema flexibility
A warehouse requires data to conform to a defined schema before it is loaded. This makes queries fast and governance straightforward, but it creates bottlenecks when data arrives in varied or unpredictable formats. A lakehouse stores data in open formats such as Parquet or Delta Lake and enforces structure through table formats and metadata layers, meaning raw data is preserved and accessible even before it is fully modelled.
Workload support
Warehouses are optimised for SQL-based BI and reporting workloads. Lakehouses are designed to support multiple workload types from a single storage layer — including SQL analytics, machine learning training, streaming ingestion, and data science exploration. This matters significantly in sectors like manufacturing and energy, where the same underlying data needs to feed dashboards, predictive maintenance models, and compliance reporting simultaneously.
When does a lakehouse actually make sense for your organisation?
A lakehouse makes sense when your organisation regularly works with data that does not fit neatly into rows and columns, when you need to support both BI and machine learning from a single source of truth, or when the cost of maintaining separate data lake and warehouse systems has become operationally unsustainable. It is not the right choice for every team at every stage.
Specifically, a lakehouse architecture is a strong fit when:
- You have active or planned machine learning use cases that require access to raw, historical, or unstructured data
- Your data volumes are large enough that the cost of loading everything into a warehouse is becoming prohibitive
- You operate in a sector with mixed data sources — transactional systems alongside IoT sensors, log files, or image data
- You need to maintain data lineage and support audit trails for compliance requirements such as the EU AI Act or NIS2
- Your current architecture involves multiple disconnected systems (a warehouse, a lake, and perhaps a half-built lakehouse already) that different teams distrust for different reasons
If your organisation runs clean, structured, predominantly relational workloads and BI is the primary output, a well-maintained warehouse may still be the right tool. The lakehouse is not an upgrade for its own sake — it is an answer to specific architectural constraints.
What are the risks of migrating too early — or too late?
Migrating too early introduces unnecessary complexity before your team has the data engineering maturity to manage it. Migrating too late means accumulating technical debt, shadow datasets, and growing distrust in your data — which becomes progressively harder and more expensive to unwind. Both timing errors carry real organisational cost.
Migrating before your organisation is ready typically means:
- Engineering teams overwhelmed by new tooling, open table formats, and governance responsibilities they have not yet built processes for
- Stakeholders who experience a period of reduced reliability while the new platform stabilises
- Governance structures that exist in documentation but are not yet enforced in code or pipeline design
Waiting too long, on the other hand, compounds the problem. Each additional year of fragmented architecture means more pipelines built on shaky foundations, more business units creating their own workarounds, and a larger migration scope when the move eventually becomes unavoidable. The data engineering effort required to untangle five years of accumulated workarounds is substantially greater than addressing the same problem in year two.
The right moment is when you have clear use cases that a warehouse cannot serve, a team with the skills to manage a more complex platform, and leadership alignment on what the migration is meant to achieve.
How does a lakehouse handle OT and IoT data differently?
A lakehouse handles operational technology (OT) and IoT data more naturally than a traditional warehouse because it is designed to ingest high-frequency, semi-structured, and time-series data at scale without requiring it to be fully transformed before storage. In industrial environments — manufacturing plants, energy grids, agricultural operations — this distinction is fundamental.
Traditional warehouses struggle with OT and IoT data for several reasons. SCADA systems, MES platforms, and historian databases produce data at frequencies and in formats that do not map cleanly to relational schemas. Forcing this data through ETL pipelines before loading it into a warehouse introduces latency, loses raw signal fidelity, and makes it difficult to go back to the source data when a model or analysis needs retraining.
A lakehouse addresses this by allowing raw sensor readings, event logs, and time-series streams to land in object storage in their native format. From there, data engineering teams can apply transformations, quality checks, and schema enforcement progressively — a pattern often called the medallion architecture (bronze, silver, gold layers). This means the raw data is always available for reprocessing, which is critical when compliance requirements or model drift demand a fresh look at historical signals.
For organisations where the factory floor and the data platform have historically been separate worlds, the lakehouse architecture provides a practical foundation for OT-IT integration — making sensor data a first-class citizen in the same ecosystem as business data. If you are looking for ongoing support to keep that integration running reliably, managed services can provide the operational backbone your team needs.
What should you do before starting a lakehouse migration?
Before starting a lakehouse migration, you should complete a thorough audit of your existing data landscape, align on a clear set of use cases that justify the move, and ensure your data engineering team has the skills and tooling knowledge to operate the new platform in production — not just build it. A migration that skips these steps tends to reproduce the same problems in a more expensive environment.
A practical pre-migration checklist includes:
- Catalogue your current data sources — including structured databases, semi-structured feeds, OT systems, and any shadow datasets maintained by individual business units
- Define your target use cases — identify the specific analytical, ML, or operational workloads the lakehouse needs to support, and confirm that a warehouse genuinely cannot serve them
- Assess your governance readiness — data ownership, access controls, quality rules, and lineage tracking should be defined before migration, not after
- Choose your open table format and cloud platform — Delta Lake, Apache Iceberg, and Apache Hudi each have different trade-offs; your choice should reflect your existing cloud environment and team expertise
- Plan for observability from day one — pipeline monitoring, alerting, and data quality checks should be built into the architecture, not bolted on later
- Run a pilot on a contained use case — prove the architecture on a single data domain before migrating your most critical pipelines
If you are unsure where your current architecture stands or which platform decisions make the most sense for your sector, working with a hands-on data engineering partner who has built lakehouses in manufacturing, energy, and agritech environments can save significant time and prevent costly architectural missteps. The goal is not to migrate — it is to end up with a platform your entire organisation trusts. Talk to our team if you want to think through your specific situation before committing to a direction.
Frequently Asked Questions
How long does a typical data warehouse to lakehouse migration take?
The timeline varies significantly depending on the complexity of your existing architecture, the number of data sources, and your team’s familiarity with lakehouse tooling. A focused pilot on a single data domain can be completed in 4–8 weeks, while a full enterprise migration across multiple business units and OT systems typically takes 6–18 months. The most reliable approach is to phase the migration — starting with a contained, high-value use case to validate the architecture before moving critical pipelines.
Can we run our data warehouse and lakehouse in parallel during the migration?
Yes, and in most cases you should. Running both systems in parallel during a transition period allows your teams to validate that the lakehouse produces consistent results before decommissioning warehouse-dependent pipelines and reports. The key is to define a clear cutover plan for each workload so the parallel-running phase does not become permanent — maintaining two platforms indefinitely is expensive and defeats the purpose of consolidating your architecture.
Which open table format should we choose — Delta Lake, Apache Iceberg, or Apache Hudi?
The right choice depends on your cloud environment, existing tooling, and primary workloads. Delta Lake integrates tightly with Databricks and is a strong default if your team is already working in that ecosystem. Apache Iceberg has broad multi-engine support and is often preferred in environments using AWS Glue, Snowflake, or Apache Spark independently. Apache Hudi is particularly well-suited for high-frequency upsert workloads common in OT and IoT contexts. Avoid choosing based on hype alone — evaluate each format against your specific query patterns and the expertise already present in your team.
How do we maintain data governance and access control in a lakehouse environment?
Governance in a lakehouse requires more intentional design than in a traditional warehouse, because the openness of object storage does not enforce access boundaries by default. Implement a metadata and cataloguing layer — such as Apache Atlas, Unity Catalog, or AWS Lake Formation — to manage data ownership, access policies, and lineage tracking centrally. Define data ownership and quality rules before migration rather than after, and enforce them in code through pipeline contracts and automated quality checks rather than relying solely on documentation.
What skills does our data engineering team need to operate a lakehouse in production?
Beyond standard SQL and Python proficiency, your team will need working knowledge of distributed processing frameworks such as Apache Spark, familiarity with open table formats and their transaction log mechanics, and experience designing and monitoring pipeline observability. Cloud platform expertise — particularly around object storage, IAM policies, and cost management — becomes significantly more important than in a managed warehouse environment. If your team has gaps in these areas, factor in upskilling time or external support before committing to a production migration timeline.
Will a lakehouse actually reduce our infrastructure costs, or just shift them?
A lakehouse can meaningfully reduce costs compared to a warehouse at scale, primarily because object storage (S3, ADLS, GCS) is substantially cheaper than warehouse compute-attached storage, and compute can be scaled independently and shut down when not in use. However, poorly governed lakehouses can generate unexpected costs through excessive small file proliferation, unoptimised query patterns, or runaway Spark jobs. Cost realisation depends on disciplined architecture and ongoing platform management — the savings are real, but they require active effort to capture and maintain.
What is the medallion architecture and do we need to implement it?
The medallion architecture organises data into three progressive layers: bronze (raw ingested data in its original format), silver (cleaned, validated, and conformed data), and gold (aggregated, business-ready datasets optimised for reporting and ML). It is not a strict requirement, but it is a widely adopted pattern for good reason — it preserves raw data for reprocessing, makes data quality responsibilities explicit at each layer, and gives different consumers access to the right level of refinement for their use case. For teams ingesting OT and IoT data alongside transactional sources, the medallion structure provides a practical and scalable way to manage the full data lifecycle.