What is a data lakehouse? Architecture, benefits, and use cases

For many years, organisations faced two distinct options. They could either employ a data warehouse for fast queries on structured data, or a data lake to hold large volumes of unstructured data. Each system satisfied one requirement but made other tasks more complex. The lakehouse architecture merges these two approaches into a single system. Much of the current AI work now depends on this integrated layer.

A data lakehouse blends the low cost and flexibility of a data lake with the performance and governance of a data warehouse. This single system handles raw data, processed datasets, and analytics tasks, thereby eliminating the need for separate platforms. A metadata layer applied to open file formats governs schemas, versioning, and transactions, reducing the operational costs of maintaining two separate systems.

A data lakehouse merges the low-cost, flexible storage of a data lake with the query performance and governance of a data warehouse into a single system. Raw data, processed datasets, and analytics workloads stay in one location, eliminating the need to copy data between platforms or pay to synchronise two separate systems. At Shipshape Data, we construct data foundations to ensure AI is reliable. The lakehouse pattern is commonly chosen because it often provides the most direct way to create a single, governed data layer for machine learning models.

This guide describes what a lakehouse is, how its architecture works, what benefits it provides, and what potential problems it presents. After reading this, you should be able to decide whether a lakehouse is appropriate for your organisation or if your current system is sufficient.

The reasons for the lakehouse's existence

We did not create a third type of data platform without reason. The lakehouse was developed because the two existing options were each incomplete. Most teams used both platforms at the same time to compensate for each other's limitations.

That process proved costly and time-consistent.

To understand why the lakehouse gained popularity, you must examine the specific limitations of both the data warehouse and the data lake.

The capabilities and limitations of warehouses

Data warehouses are built to run structured queries on organised data quickly and repeatedly. This process supports most business intelligence operations. To use this system, all data must adhere to a predefined schema. You must model and transform the data before storing any of it.

This approach works well for organised tabular data. However, it does not handle most of the data modern businesses collect, such as log files, sensor readings, images, audio, PDFs, customer chat transcripts, and clickstreams. This type of data does not fit easily into columns, so the data warehouse will either reject it or require conversion into a less detailed format. Additionally, data warehouse storage and processing incur costs, since you pay for performance. When data volumes reach petabytes, or when you need to store data that may not be used in reports, the costs become excessive.

What lakes promised, and how they went wrong

Data lakes provided a solution to those problems. All data, no matter its format, could be placed on inexpensive object storage. Structured tables, JSON files, Parquet files, and raw binaries could be stored together in a single bucket with minimal preparation. Storage costs remained low and the system stayed completely flexible.

In practice the situation is more complex. Lacking the schema enforcement and query optimisation of a data warehouse, a data lake can become slow to query, hard to trust, and almost impossible to govern. Teams may lose awareness of the data it contains. Data quality can decline because there is no validation during ingestion. This is the "data swamp" that people often describe, and it does occur. We have seen several cases where a data lake turned into a repository for unused data, and analysts reverted to using spreadsheets.

Data lakes offer various storage options, yet they lack the performance and governance needed for production analytics.

The two-system tax

So most organisations did the pragmatic thing and ran both. Raw data lands in the lake, gets transformed by ETL pipelines, and the cleaned output gets loaded into the warehouse for the analysts. It works. It also means you pay for storage twice, maintain a fleet of pipelines whose only job is to shuffle data sideways, and live with the lag between when data arrives and when it becomes queryable. Every one of those pipelines is a thing that can break at 2am.

The lakehouse was created to solve this specific problem. Rather than requiring synchronisation of two separate platforms, this system merges the capabilities of both into a single architecture. It handles diverse data types while delivering the query performance and governance standards typical of a data warehouse.

How a data lakehouse works

The process is simple. A lakehouse adds a metadata layer atop standard object storage. This metadata layer turns a collection of files into a system that functions like a database. You store your data once in open formats such as Parquet, Delta Lake, or Apache Iceberg. The metadata layer handles schemas, versioning, and the administrative tasks needed for fast and secure queries. Because only one copy exists, the same dataset can support a SQL dashboard, machine learning training, and streaming tasks without needing to create multiple clones.

Storage and metadata, kept separate on purpose

Object storage forms the base of the stack. You may use Amazon S3, Azure Data Lake Storage, or Google Cloud Storage. All data is stored in open formats to reduce costs and avoid proprietary lock-in. Because these formats are standard, multiple engines can read your data, preventing you from being restricted to a single vendor.

Above that sits the metadata layer, and it does the heavy lifting. It tracks schemas, partitions, file locations, and column statistics, so when a query arrives, the engine knows precisely which files to open and how to read them. It also maintains a transaction log, a running record of every change, which lets you query an older version of a table, audit who changed what, and prevent two writers from stepping on each other.

Transactions, the part lakes never had

Plain data lakes did not support concurrent writes or guarantee ACID transactions, which include atomicity, consistency, isolation, and durability, and this is why they were unsuitable for production analytics. If a write operation was incomplete, a table could be left in an inconsistent state. The lakehouse architecture solves this problem by integrating transaction support into the metadata layer. This allows multiple processes to read and write data simultaneously without causing corruption.

This mechanism shows why the system is reliable. When records are written or updated, a lakehouse avoids editing files in place. It creates new files and modifies the transaction log in one atomic step. If a job aborts before completion, it leaves orphaned files that the log does not reference. As a result, queries exclude those files. Your reads always retrieve a consistent snapshot, even when writes happen concurrently.

An overview of the architecture by layer

Each vendor offers a lakehouse built from three layers, each serving a specific function. Although the layer names and capabilities vary across products, the overall structure stays consistent. Grasping these layers lets you compare platforms accurately. You can assess each platform's offerings at each level rather than depending on a generic diagram. A detailed explanation appears in our note on data lakehouse architecture and its layers.

The storage layer

Your data resides as open files on object storage systems like S3, Azure Blob Storage, or Google Cloud Storage. These files are formatted as Parquet, Delta Lake, or Iceberg. This layer prioritises durability and low cost over high speed. This method lets you store petabytes of data-from raw ingestion to final analytical tables-without incurring the high costs of traditional data warehouses.

The metadata and catalogue layer

Above the storage lies the layer that tracks schemas, partitions, table versions, and statistics, maintains the transaction log, and enforces ACID guarantees. It also hosts the catalogue, which tells a query engine where the desired data resides. When this layer is correct, you gain time travel across historical versions, schema evolution that does not break downstream jobs, and proper versioning. If it is misconfigured, you return to a swamp with additional steps. This is the layer where data architecture decisions truly prove their value.

The compute and processing layer

At the top sit the engines: Apache Spark, Trino, Presto, or a vendor's own runtime, handling query execution, transformations, and machine learning. A useful property is that compute scales independently of storage. You can apply substantial horsepower to a heavy analytical job during the day and scale it down overnight, while your storage bill remains unchanged because storage is a separate concern. You pay only for the work you actually perform, not for idle capacity reserved for potential use.

Benefits and trade-offs, honestly

The lakehouse is an effective architecture, yet it demands significant investment. It provides simpler operations and lower costs, although the technology is less mature than traditional data warehouses and needs more technical expertise from your team. You should assess this pattern based on your specific workloads instead of marketing claims. This presents an objective assessment of the advantages and disadvantages.

What you get

The primary benefit is eliminating the dual-system tax. You no longer have to maintain a separate data lake and data warehouse, removing the need for ETL jobs that synchronise the two systems. This change cuts infrastructure costs and removes a common source of technical failures. Because your teams use a single data source, they do not need to reconcile conflicting numbers between systems. This prevents a specific category of reporting errors.

You can keep all data types in one location while preserving query speeds comparable to a data warehouse. Structured tables, JSON logs, images, and streaming sensor feeds reside together and can be accessed via SQL, models, or real-time jobs. Specifically, this change provides:

• One governed copy of your data, rather than duplicate storage on two platforms
• You will have fewer pipelines to build and monitor because you do not need to move data between a lake and a warehouse.
• Data is more current because analytics can process new information immediately instead of waiting for overnight batch processing.
• The system supports structured and unstructured data together, which is required for AI workloads.
• Computing and storage resources scale independently, which ensures that costs align with actual usage.

In the estates we have migrated to a lakehouse, the most significant cost reduction is seldom found in the storage category.

We previously spent many staff hours and maintained multiple pipelines to ensure both systems remained consistent.

Where it will bite

This technology is less mature than established data warehouse platforms. There are fewer pre-built integrations available, and there is a smaller community of experienced practitioners to consult. Because there is less established documentation to follow, you must develop your own practices. The role requires knowledge of data engineering, cloud storage, and distributed computing. This is a wider range of skills than the specific database expertise required for a traditional warehouse.

Performance is also a factor. A lakehouse does not always perform better than a dedicated warehouse for complex analytical queries or high-concurrency workloads. You will need to spend time adjusting file layouts, partition strategies, and indexing to achieve high performance. Some teams complete this work and are satisfied with the results. Other teams find that their specific query patterns perform better on a dedicated warehouse and conclude that the transition is not beneficial. Both results are valid. It is useful to determine which outcome applies to your situation before you commit to a change.

Where a lakehouse is useful

This pattern is useful when one dataset must support multiple types of work simultaneously. You should consider this approach if your current system requires teams to copy data, results in pipeline delays, or forces a choice between data variety and query speed. The lakehouse architecture is most effective when you face these specific operational limitations rather than as a theoretical improvement to a system diagram.

Analytics and business intelligence on live data

You can use the same tables for both a real-time dashboard and a historical trend report, without needing to copy data. A marketing team can analyse customer behaviour, and a data science team can train a model using the same records, avoiding disagreements about outdated data. Business intelligence tools connect using standard SQL, providing business users with a familiar experience similar to working with a data warehouse, but without requiring a separate system.

Streaming data also offers advantages in this context. Feeds from IoT sensors, application logs, and transactions flow into the lakehouse continuously and become queryable almost immediately. This is more efficient than keeping data in a queue for a nightly batch process. This capability allows use cases such as fraud detection, supply chain monitoring, and live customer experience management. These functions need current data rather than a report from the previous day.

Machine learning and AI

This yields a clear benefit. Data science teams can retrieve raw and processed data in any required format. They can employ structured tables for statistical analysis or unstructured text for language models without waiting for a data engineer to create a specific dataset for each experiment. Since the information is stored in "accessible and versioned" formats, the same data serves both exploration and production. Our AI and ML data readiness checklist gives more detail on the practical requirements for data to be "accessible and versioned".

This workflow links the entire process. Training pipelines use historical data to build models, and production systems use the same platform for live predictions. This creates a consistent path from the initial experiment to the deployed model, removing the need to rebuild the data layer at different stages. Eliminating repeated data preparation often shortens the timeline of a machine learning project by several weeks. This is the component of a data pipeline architecture that teams most frequently overlook.

So, is a lakehouse right for you?

A lakehouse combines flexible storage with the performance of a data warehouse. This architecture removes the need to maintain two separate systems and lets you run business intelligence and machine learning on a single governed platform. Whether you should adopt this pattern depends on your specific data volumes, your current infrastructure, your team's skills, and the types of workloads you manage.

You should evaluate your current setup. If you maintain a separate data lake and data warehouse, duplicate data between them, or cannot move AI prototypes into production because of data layer issues, a lakehouse will resolve these problems. If your data is small, entirely structured, and your current warehouse meets your needs, you do not need to migrate.

We define the layers, formats, and governance needed to build a functional lakehouse. Our process starts with an assessment of your current data and an evaluation of whether a lakehouse architecture is appropriate for your specific workloads. We build the technical foundation needed to ensure AI applications remain stable in production environments. If you want to determine if this architecture is suitable for your organisation, talk to us to receive an objective analysis.

If your team is considering this option, our data consultancy can explain the specific requirements and the initial steps you should take.

Frequently asked questions