Data lakehouse use cases: real examples and when you need one

What a data lakehouse is

A lakehouse has three parts: files in object storage (usually Parquet), an open table format on top of them (Apache Iceberg, Delta Lake or Apache Hudi) that adds transactions, schemas and table history, and one or more engines that query those tables with SQL or code. AWS’s definition, October 2026: “A data lakehouse is a unified data architecture that combines data warehouses and data lakes, providing analytics capabilities such as structuring, governance, and reporting.”

What separates it from a plain data lake is the table format: without it, a dashboard can read a half-written file and two writers can overwrite each other. What separates it from a warehouse is ownership of the files: they sit in your bucket, in an open format, and any engine that speaks the format can read them. The data lakehouse architecture guide covers the layers in detail; this page is about what teams use it for.

Data lakehouse use cases, with real examples

Most pages list the same workloads without a single named case. The table below keeps only examples with a named company and a figure. All of them come from one page: MinIO’s “Data Lakehouse Examples in Action” (dated July 2025), which summarises published case studies. The figures are self-reported by the companies and collected by MinIO, a storage vendor; we have not verified them.

Two things stand out. Every named case is a very large company, and most started from Hadoop or a sprawl of copies, so the gain is partly from consolidation, not from the format alone. And the one modest-volume case, Datto, was not a volume problem at all: queries scanned thousands of small JSON files, and the fix was a layout that let the engine skip them. Small files are also what frequent loads produce on a lakehouse table, which we measured next.

What frequent loads do to a lakehouse table, measured

Streaming and frequent loads are on every use-case list. They come with a cost the lists leave out. We built a real Apache Iceberg table locally (Apple M4 Pro laptop; pyiceberg 0.8.1 writing with a SQLite-backed SQL catalog to the local file system; pyarrow 18.1.0; DuckDB 1.1.3 to run the query). The data is a synthetic 2,000,000-row sales table with 17 columns and one year of dates. We appended it in 500 batches of 4,000 rows, the way a loader that writes every few minutes would, and then compacted the table by rewriting it with its own contents:

Writing the 500 appends took 8.8 seconds. The table’s metadata grew larger than its data (131.8 MB against 77.3 MB), and every query ran 3.1x slower until someone compacted it. Compaction did not remove the old snapshots either: the history stays, with its files, until a separate expiry job deletes it. On a lakehouse that loads often, compaction and snapshot expiry are jobs someone has to schedule and watch. A managed warehouse does this housekeeping for you.

What a BI analyst actually builds on a lakehouse

The dashboards are the ones you build on any warehouse: spend and ROAS by channel, revenue by store and day, cohort retention, funnel conversion. On a lakehouse they run on a SQL engine (a Databricks SQL warehouse, Trino, Athena, or a warehouse reading the lake tables), and the BI tool connects to that engine exactly as it would to a warehouse.

What the lakehouse does not do is model the data. It stores the raw layer cheaply; it does not turn seven ad platforms, each with its own schema, into one table. On our synthetic demo data that step takes 143,230 raw connector rows to 15,466 modelled rows before any report reads them; the lakehouse architecture guide traces it layer by layer. That work is the same on a lakehouse as on a warehouse. “Self-service” comes from the modelled layer, not from the storage format. A BI team that wants less dependence on data engineering needs well-described modelled tables more than it needs open files; a lakehouse adds engineering work (catalogs, compaction, file layout) before it removes any.

Checks are the other half. In the same demo, every row-level check on orders passes while the ad spend feed stops five days before the orders do, which the implementation guide shows with its acceptance queries. No storage format would flag that. A data quality check on data freshness would.

Best practices that hold for self-service on a lakehouse, short:

• Expose modelled tables, not raw ones, with a description and an owner on each.
• Check freshness per source at the raw layer, before the join.
• Decide the layout before the first dashboard: partitions follow the filters people actually use (challenge 5 below).
• Keep one engine as the reporting endpoint, so a number does not change with the tool that reads it.

Implementation challenges, specifically

The generic list (governance, cost, skills) applies to any data project. These are the ones specific to a lakehouse:

1. Who owns the catalog. Two engines writing one table need one catalog both trust; the vendors’ docs name different ones: AWS Glue Data Catalog for Redshift, Unity Catalog for Databricks, Snowflake for Snowflake-managed tables (details).
2. Version and feature support differ. Athena’s docs state “Athena supports Apache Iceberg version 1.4.2”; check that every engine handles the features a shared table uses.
3. Read-only paths still exist. Google’s BigQuery docs describe the older Iceberg external tables as “read-only”; check which route each engine uses.
4. Housekeeping is yours to schedule. In our 500-append test the metadata outgrew the data and queries ran 3.1x slower until compaction; old snapshots stay until an expiry job removes them. Compaction and snapshot expiry need a schedule, an owner and an alert when they fail.
5. Partitioning is a trade, not a default. In our local Iceberg build, partitioning by day made a one-day read 118x smaller but a filter on any other column opened all 365 files (measured in the architecture guide). Pick partitions from the filters people actually use.
6. Access is granted in two places: on the bucket and in the engine.
7. Data quality does not improve by itself. The freshness gap above would survive any migration.

Do you need a lakehouse, or does your warehouse already do this?

This is the decision most pages skip. Three facts change it.

Fact 1: the five warehouses already speak Iceberg

“Lakehouse or warehouse” is no longer a choice of platform. Per their documentation in October 2026, all five warehouses OWOX Data Marts runs on now read and write Apache Iceberg: Google BigQuery through Apache Iceberg managed tables, Snowflake through Apache Iceberg tables for Snowflake, AWS Redshift and AWS Athena through their Iceberg reads and writes, and Databricks through managed Iceberg tables in Unity Catalog (plus UniForm for Delta tables); the architecture guide quotes each vendor.

So when one table really must sit in your own bucket in an open format – for a data science team’s Spark jobs, for a partner, to avoid lock-in on one dataset – you can put that table in Iceberg from the warehouse you already have. The migration question shrinks to a per-table question.

Fact 2: two estates we measured are small and tabular

We sized two BigQuery projects on 15 October 2026. One is synthetic: owox-demo, the data behind our demo businesses (a retail chain, a trading business, a subscription store, a clinic network and others). The other is real: a working OWOX project used for content analytics, connector tests and demo staging. The real project was sized from table metadata, one dataset at a time, which bills 0 bytes:

SELECT table_id, size_bytes, row_count, type
FROM `project.dataset.__TABLES__`
ORDER BY size_bytes DESC

The demo project was sized with INFORMATION_SCHEMA.TABLE_STORAGE, grouped by dataset (that scan billed 1.56 GB, about half of the estate it measured).

Both estates sit far from every row of the “you need a lakehouse” column below. All data is tables; one SQL engine answers the questions; the one large dataset is two tables of a single export (11.18 GB and 4.38 GB), and everything else is small. Two estates are not a survey, and yours may differ; checking takes one metadata query per dataset. The problems these estates do have (seven ad schemas to map, a feed that stops before the orders do) are modelling and quality problems.

Fact 3: on a lakehouse, layout and housekeeping become your job

A warehouse already rewards layout: on our demo sales table, a one-day filter on an unpartitioned table read 35% more than the whole year, as the implementation guide measures step by step. An Iceberg table behaves the same way, and adds its own work. Partitioning by day made a one-day read 118x smaller in our local Iceberg build, at the price of a table 3x larger and 365 files for any other filter (architecture guide). Frequent loads left 500 files and more metadata than data until compaction (the test above). Moving to a lakehouse does not fix layout; it makes layout and its upkeep your job. On a warehouse, partitioned tables are most of the fix.

The rule

If the trouble is numbers nobody can defend, neither column applies: that is a modelling and checking problem, and it moves with you to any platform. How to choose a data warehouse covers the platform choice itself; the Databricks and Athena guides cover the two of the five built closest to the lake.

Lakehouse for marketing: where it fits and where it does not

It fits when marketing data sits next to much larger data that another team works on: raw clickstream or app events at a volume where storage cost matters, shared with a data science team training propensity or media-mix models in Spark on the same tables. It also fits when raw API payloads must be kept for years so they can be reprocessed when a definition changes.

It does not fit the usual marketing reporting job: ad spend from a handful of data connectors, orders, sessions, maybe CRM stages. In our demo, seven ad platforms over almost three years are 33 MB. The hard parts are a date stored as STRING in two of seven platforms, seven column layouts mapped to one, a spend feed that stops five days before orders, and agreeing what “revenue” means. None of that is storage. A marketing analytics data model solves it on whichever of the five warehouses you use, and a unified ad report is the output a reporting team usually needs.

Where OWOX Data Marts fits

OWOX Data Marts works on top of your data storage – Google BigQuery, Snowflake, AWS Redshift, AWS Athena or Databricks – rather than instead of it. A Data Mart is SQL, a table or a view in that storage, with a description, an owner and relationships to other Data Marts; together they form the data model that reports in spreadsheets, Data Studio and AI assistants are built from. Whether the tables underneath are native or open-format, the modelled layer and its checks are the part the business reads.

Data QualityWhat is Data Freshness? How to Measure It and How Fresh a Decision NeedsOctober 14, 2026→Data AnalyticsWhat is Data Analytics? How Data Becomes an Insight You Can TrustOctober 14, 2026→Data AnalyticsWhat is Raw Data? Definition, Examples and What Aggregation LosesOctober 14, 2026→

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