A data warehouse stores structured, cleaned data for reporting. A data lake stores raw data of any type at low cost. A data lakehouse adds warehouse-style reliability to data lake storage, so one copy of the data can serve both analytics and machine learning. A streamhouse, the newest of the four, keeps the current state of the business continuously available to applications and AI agents. The first three are built to analyze what has already happened. The streamhouse is built to act on what is happening now. This guide compares all four and explains how to decide which ones your organization needs.
| Data warehouse | Data lake | Data lakehouse | Streamhouse | |
|---|---|---|---|---|
| Primary job | Reporting and business intelligence | Storing raw data for exploration and machine learning | Analytics and machine learning on one open copy of the data | Running the business on current data |
| Data types | Structured | Structured, semi-structured, and unstructured | Structured, semi-structured, and unstructured | Events and changing business state |
| Schema | Applied on write | Applied on read | Enforced through open table formats | Managed in motion through schema governance |
| Data freshness | Batch, typically hours to days | Batch | Batch or micro-batch | Continuous, as events happen |
| Main consumers | Analysts | Data scientists and engineers | Analysts, data scientists, and engineers | Applications and AI agents |
| Service level | Analytical | Analytical | Analytical | Production |
| Typical technology | Cloud data warehouses, SQL engines | Object storage such as Amazon S3 | Apache Iceberg, Delta Lake, or Apache Hudi on object storage | Change data capture, Apache Kafka, Apache Flink, open table formats |
A data warehouse is a central store of structured data that has been cleaned, modeled, and loaded for analysis. The concept dates to the late 1980s and became the standard foundation for business intelligence in the 1990s. Data is transformed before it is loaded, a model known as schema on write, so every report works from the same consistent definitions.
That consistency is the warehouse’s strength. Finance teams, sales dashboards, and regulatory reports depend on numbers that match across the business. The tradeoff is flexibility. A warehouse handles structured, tabular data well, but images, logs, and documents fit poorly, and loading new sources means modeling them first. Most warehouses are also refreshed in batches, which makes them a record of the business as of the last load.
A data lake stores raw data in its original format, usually on low-cost object storage. The term appeared around 2010 as organizations started collecting data faster than they could model it. A lake accepts anything: database extracts, application logs, sensor readings, images, and documents. Structure is applied only when someone reads the data, a model known as schema on read.
This makes the data lake a natural home for data science and machine learning, where teams want the raw material, not a pre-shaped version of it. Bühler, a global mechanical engineering group, runs a log management platform built by Mimacom that also serves as a data lake, with Apache Kafka collecting around 220 GB of data a day from 850 source systems. The weakness of a data lake is governance. Without consistent structure, ownership, and quality controls, a lake becomes hard to trust and harder to query, a state often called a data swamp.
The difference comes down to when structure is applied. A data warehouse shapes data before it is stored, so it is fast and consistent for known questions but slow to absorb new sources. A data lake stores data first and shapes it later, so it takes in anything quickly but leaves each team to make sense of it. Warehouses serve analysts who need reliable answers. Lakes serve data scientists and engineers who need raw material. For years, many organizations ran both side by side and copied data between them, which is the problem the lakehouse set out to solve.
A data lakehouse combines the low-cost, flexible storage of a data lake with the reliability and performance of a data warehouse. Databricks popularized the term around 2020. The enabling technology is the open table format, such as Apache Iceberg, Delta Lake, or Apache Hudi, which adds ACID transactions, schema enforcement, and versioning to files on object storage.
The result is one open copy of the data that SQL engines, BI tools, and machine learning frameworks can all work on. That removes the common pattern of copying data out of a lake into a separate warehouse for reporting. IBM watsonx.data is one example of a lakehouse built on open formats.
A data lakehouse is a data lake with a management layer on top. Both store data on low-cost object storage in open file formats. The lakehouse adds open table formats, which track every change as a transaction, enforce a schema, and let engines query the data with warehouse-level reliability. In practice, many organizations reach a lakehouse by upgrading an existing lake rather than replacing it, converting their most important datasets to a format such as Apache Iceberg first.
The main difference is openness. A data warehouse stores data in its own managed format and is optimized for SQL on structured data. A data lakehouse stores data in open table formats that many engines can read, and it handles semi-structured and unstructured data alongside tables. A lakehouse also tends to cost less to store large volumes, because it runs on object storage.
The two still share an important trait. Both are analytical systems that mostly work in batches. They tell you what happened, and they do it well, but neither is designed to sit on the production path of an application that has to respond the moment something changes.
A streamhouse is a data architecture that captures, transports, transforms, governs, and serves the current state of a business continuously, so that production applications and AI agents can act on it. Five data streaming companies, Aiven, Confluent, Redpanda, StreamNative, and Ververica, published an open, vendor-neutral definition on September 15, 2026.
The definition sets three requirements. A streamhouse is real-time, so data stays current as events happen. It is production-native, built to the service levels of the systems that depend on it. And it is decentralized, connecting to data where it already lives rather than requiring consolidation first.
The lakehouse and the streamhouse answer different questions. The lakehouse answers “what happened, and what does it tell us?” for analysts and data scientists. The streamhouse answers “what is the state of the business right now?” for software that has to act on it. The simplest way to put it: the lakehouse explains what happened, and the streamhouse powers what happens next.
That difference shows up in four places. The consumers change, because applications and AI agents query a streamhouse continuously instead of people querying a lakehouse periodically. Freshness changes, because a streamhouse processes events as they occur instead of in scheduled jobs. The service level changes, because an outage in a streamhouse stops production systems, while an outage in a lakehouse delays a report. And the shape of the architecture changes, because a streamhouse connects existing systems instead of collecting everything in one place.
The two are designed to work together. A streamhouse can write its streams into open table formats such as Apache Iceberg, so the lakehouse receives current tables without a separate batch pipeline. Analysts keep their lakehouse, and it gets fresher data as a side effect.
Most enterprises don’t pick one of the four. They run a combination, and the useful question is which workload belongs where. Start from who uses the data and how quickly they need it:
If you are planning AI agents for production use, the streamhouse question comes first. An agent is only as good as the context it can see, and the other three architectures were built for people reading reports, not software making decisions.
For enterprises on IBM, the two architectures connect directly. Confluent’s data streaming platform, now part of IBM, provides the streamhouse layer: it captures changes from systems such as IBM Z, SAP, Oracle, and Salesforce, streams and processes them with Apache Kafka and Apache Flink, and governs them in motion. Tableflow then materializes those streams as Apache Iceberg tables that a lakehouse such as IBM watsonx.data can query, so analytics and agents work from the same current data.
Mimacom is an IBM Silver Business Partner with a long-standing Confluent and Apache Kafka practice, and our teams have run streaming platforms in production for banks, retailers, manufacturers, and transport operators.
A data warehouse is built for structured data. Data is cleaned and modeled into tables before it is loaded, which is what makes warehouse reporting consistent. Many modern cloud warehouses can also store semi-structured data such as JSON, but unstructured data such as images, audio, and documents belongs in a data lake or a lakehouse.
Databricks describes its platform as a data lakehouse rather than a data warehouse. It stores data in open table formats on cloud object storage, mainly Delta Lake, and also supports Apache Iceberg. It does offer SQL warehouses, which are compute resources for running BI and SQL workloads on lakehouse data, so it can serve many workloads that once required a separate warehouse.
Choose a lakehouse when you need to analyze structured and unstructured data together, when machine learning teams and BI teams should work from the same copy of the data, or when storage cost and open formats matter. A data warehouse remains a strong choice for tightly governed, structured reporting where consistency matters more than flexibility.
Mimacom helps enterprises design data architectures around the decisions they need to support, including streamhouses on IBM Confluent that keep AI agents and applications working from current data. Explore our Confluent consulting services or contact us to plan your next step.