A data lake stores raw, unprocessed data in any format at massive scale, while a lakehouse combines that storage flexibility with the structured query performance and ACID transaction support traditionally found in a data warehouse. The key distinction is that a lakehouse adds a metadata and governance layer on top of lake storage, making data both accessible and reliable. The sections below walk through when each architecture fits, how they handle OT and IoT data, and how they compare to mesh and fabric patterns.
When does a data lake stop being enough?
A data lake stops being enough when the volume of raw data outpaces your team’s ability to make it trustworthy and queryable. If analysts are writing one-off scripts to extract usable subsets, if ML engineers are blocked waiting for clean data, or if pipelines break silently overnight and nobody notices until a report is wrong, the lake has become a liability rather than an asset.
The original promise of the data lake was compelling: land everything, figure out the structure later. For many organisations, “later” never arrived. What accumulated instead was an unindexed archive of files with no consistent schema, no quality guarantees, and no lineage. Business units stopped trusting shared data and started pulling their own exports. Three warehouses, two lakes, and a half-finished lakehouse became the norm rather than the exception.
The tipping point usually arrives when one of three things happens: data governance requirements become non-negotiable (think NIS2 or the EU AI Act), the ML team grows large enough to demand reliable feature pipelines, or the cost of maintaining bespoke extraction scripts exceeds the cost of rearchitecting the platform itself.
What is a lakehouse and how does it work?
A lakehouse is a data architecture that stores data in open file formats on low-cost object storage, then adds a transactional metadata layer that enables SQL queries, ACID guarantees, schema enforcement, and time-travel capabilities directly on that storage. It works by treating the data lake as the physical foundation while building warehouse-grade reliability and performance on top of it.
The technical mechanism relies on open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi. These formats maintain a transaction log alongside the data files, which means the system always knows the current valid state of a table, can roll back to a previous snapshot, and can enforce schema changes without rewriting all underlying files.
In practical terms, this means a data engineering team can write raw sensor readings into the lake, apply transformations and quality checks using tools like dbt or Spark, and serve the results to BI tools and ML frameworks from the same storage layer, without copying data into a separate warehouse. The compute and storage are decoupled, so each can scale independently, and multiple engines (Spark, Trino, DuckDB) can query the same tables simultaneously.
What are the key differences between a data lake and a lakehouse?
The key differences between a data lake and a lakehouse come down to four dimensions: transaction support, data quality enforcement, query performance, and governance. A data lake provides flexible, schema-on-read storage with no built-in reliability guarantees. A lakehouse adds ACID transactions, schema enforcement, and optimised query planning on the same storage layer.
- Transaction support: Data lakes have none by default. Concurrent writes can corrupt files. A lakehouse uses a transaction log to make every write atomic and isolated.
- Schema enforcement: Lakes accept any format at write time. A lakehouse can enforce schema at write time and evolve it in a controlled, versioned way.
- Query performance: Querying a raw lake requires full file scans. A lakehouse maintains statistics, partitioning metadata, and file compaction to make queries orders of magnitude faster.
- Data quality and lineage: Lakes provide no native lineage or quality tracking. A lakehouse, when combined with orchestration and observability tooling, supports end-to-end lineage from ingestion to consumption.
- Governance: Access control in a lake is typically applied at the bucket or folder level. A lakehouse enables table-level and column-level access policies, which is essential for compliance with regulations like GDPR or the EU AI Act.
For teams spending the majority of their time moving and cleaning data rather than delivering value, the lakehouse architecture is designed to shift that ratio by making reliability a property of the platform rather than a manual engineering task. Our software and data services are built around exactly this principle, helping organisations turn raw data into a reliable, governed asset.
Which architecture is better for OT and IoT data integration?
The lakehouse architecture is generally better suited for OT and IoT data integration than a conventional data lake because it can handle high-frequency time-series ingestion while still providing the schema enforcement and queryability that operational analytics requires. Raw sensor streams, SCADA event logs, and MES records need to land quickly, but they also need to be normalised, deduplicated, and made queryable without a separate transformation pipeline copying data into a warehouse.
OT data presents specific engineering challenges that a plain lake struggles with. SCADA systems and historian databases often produce out-of-order events, duplicate readings on reconnect, and schema variations across firmware versions. Without ACID support, late-arriving data can overwrite or corrupt existing records. With a lakehouse table format, merge operations handle late data correctly, and time-travel lets engineers audit exactly what the platform knew at any given moment, which matters for process compliance and incident investigation.
The OT-IT integration layer itself, the connectors that normalise historian tags, SCADA alarms, and MES production orders into a common schema, sits upstream of the lakehouse. Building that bridge correctly is where most of the engineering complexity lives. We work on exactly this kind of integration daily in manufacturing, energy, and agritech environments, connecting the factory floor to the data platform so that operational data becomes a first-class citizen alongside enterprise data sources.
Should you migrate from a data lake to a lakehouse?
You should migrate from a data lake to a lakehouse if your current lake suffers from reliability problems, governance gaps, or query performance issues that are slowing down analytics and ML workloads. Migration is worth the investment when the cost of maintaining workarounds, such as nightly copies into a warehouse, bespoke quality scripts, or manual reconciliation, exceeds the engineering effort of adopting an open table format.
Migration does not have to be a full replacement. The most practical approach is incremental: adopt an open table format like Iceberg or Delta Lake on new pipelines first, prove the pattern on one domain, then migrate high-value existing tables progressively. Because lakehouse formats sit on the same object storage you likely already use, there is no need to move raw data. The migration is primarily about adding the metadata layer and updating write paths.
The decision also depends on your team’s current tooling. If you are already using Spark, dbt, or a cloud-native platform like Databricks, Snowflake, or BigQuery, lakehouse capabilities are either already available or a configuration change away. If your lake is a collection of custom scripts writing Parquet files to S3 with no orchestration, the migration is more substantial but also more impactful. Our managed services can support your team through exactly this kind of platform transition, from initial assessment through to production-ready operation.
How does a lakehouse compare to a data mesh or data fabric?
A lakehouse is a storage and compute architecture, while a data mesh and data fabric are organisational and governance patterns. They operate at different levels of abstraction and are not mutually exclusive. A lakehouse can serve as the technical foundation for a mesh or fabric implementation.
Data mesh
A data mesh distributes data ownership to domain teams, each of which is responsible for producing and maintaining its own data products. The mesh pattern addresses the organisational bottleneck of a central data team that cannot keep up with the needs of every business unit. Technically, each domain might run its own lakehouse or warehouse, with a shared catalogue and interoperability standards connecting them. The mesh is primarily a people and process decision, not a technology one.
Data fabric
A data fabric is a design concept that uses metadata, automation, and AI-assisted discovery to create a unified, self-service layer across heterogeneous data sources. Where a mesh decentralises ownership, a fabric focuses on connecting disparate systems intelligently, often through a knowledge graph or active metadata layer. A fabric can sit on top of multiple lakehouses, warehouses, and operational databases simultaneously.
For most mid-to-large organisations in manufacturing, energy, or agritech, the practical sequence is: build a reliable lakehouse foundation first, establish clear data ownership and quality standards, and then evaluate whether a mesh or fabric pattern is warranted by the scale of your domain complexity. Adopting mesh or fabric patterns on top of an unreliable data lake solves the wrong problem. If you want to explore which architecture fits your current stage, speak with our data engineers about the tradeoffs in your specific context.
Frequently Asked Questions
How do I choose between Apache Iceberg, Delta Lake, and Apache Hudi for my lakehouse?
The choice largely depends on your existing ecosystem. Delta Lake integrates most tightly with Databricks and Spark-heavy environments, making it a natural fit if you’re already on that stack. Apache Iceberg has the broadest multi-engine support (Spark, Trino, Flink, DuckDB) and is the strongest choice for vendor-neutral or cloud-agnostic architectures. Apache Hudi was purpose-built for high-frequency upsert workloads, making it particularly well-suited for CDC (change data capture) pipelines and streaming ingestion scenarios common in IoT environments. All three are mature, production-ready formats, so the decision is more about ecosystem alignment than raw capability.
What are the most common mistakes teams make when building a lakehouse for the first time?
The most frequent mistake is treating the lakehouse as a drop-in replacement for a data warehouse and skipping the data modelling work entirely. A lakehouse gives you the tools to enforce quality and structure, but it doesn’t do that automatically — you still need clear ownership, defined schemas, and agreed quality thresholds per domain. Another common pitfall is neglecting file compaction and table maintenance: open table formats accumulate small files over time, and without scheduled optimisation jobs, query performance degrades significantly. Starting with one well-governed domain and proving the pattern before scaling is the most reliable way to avoid both traps.
Can a lakehouse handle real-time or near-real-time data, or is it primarily a batch architecture?
Modern lakehouse architectures support streaming ingestion natively. Tools like Apache Flink, Spark Structured Streaming, and Kafka Connect can write directly to Iceberg, Delta Lake, or Hudi tables with low-latency guarantees, typically in the seconds-to-minutes range. This makes the lakehouse viable for near-real-time use cases such as operational dashboards, anomaly detection, and live equipment monitoring. True sub-second latency still favours dedicated streaming stores or time-series databases, but for the vast majority of OT and IoT analytics workloads, a streaming-enabled lakehouse eliminates the need for a separate real-time layer.
How does a lakehouse handle data governance and compliance requirements like GDPR?
A lakehouse significantly strengthens your compliance posture compared to a raw data lake by enabling table-level and column-level access controls, full schema versioning, and auditable transaction logs. For GDPR specifically, the time-travel and metadata capabilities make it feasible to implement right-to-erasure workflows: you can identify, overwrite, or delete specific records and verify the change is reflected across all downstream snapshots. Pairing the lakehouse with a data catalogue (such as Apache Atlas, DataHub, or Unity Catalog) adds lineage tracking and data classification, which are essential for demonstrating compliance under GDPR, NIS2, or the EU AI Act.
What team skills and roles do I need to successfully implement and operate a lakehouse?
A functional lakehouse implementation typically requires data engineers who are comfortable with distributed compute frameworks (Spark or Flink), SQL-based transformation tooling (dbt), and cloud object storage. A platform or DevOps engineer is needed to manage infrastructure, orchestration (Airflow, Prefect, or Dagster), and table maintenance jobs. As the platform matures, a data governance lead or data steward role becomes important to maintain cataloguing standards and access policies. Smaller teams can start lean by leveraging managed platforms like Databricks or AWS Lake Formation, which abstract away much of the infrastructure complexity and lower the barrier to entry.
How do I measure whether my lakehouse migration has actually been successful?
Success should be measured against the specific pain points that motivated the migration in the first place. Concrete metrics to track include: reduction in pipeline failure rates and mean time to detection for data quality issues, query performance benchmarks on key analytical workloads before and after, the percentage of data consumers using the governed lakehouse layer versus pulling their own raw exports, and time-to-delivery for new data products. A meaningful leading indicator is analyst and ML engineer satisfaction — if they’re spending less time hunting for clean data and more time delivering insights, the architecture is doing its job.
Is a lakehouse cost-effective compared to keeping a separate data lake and data warehouse?
In most cases, yes — primarily because a lakehouse eliminates the storage duplication inherent in a lake-plus-warehouse setup, where data is ingested into the lake and then copied into the warehouse for reliable querying. Object storage (S3, ADLS, GCS) remains significantly cheaper than warehouse storage per terabyte, and decoupling compute from storage means you only pay for query compute when you need it. The cost comparison does depend on query patterns: if your workload is dominated by highly concurrent, low-latency SQL queries, a dedicated cloud data warehouse may still be more cost-efficient for that specific layer. Many organisations end up with a hybrid — a lakehouse as the canonical storage and transformation layer, with a small warehouse serving high-concurrency BI workloads.