A lake house is a relatively new open architecture that combines the best elements of data lakes and

data warehouses.

So to understand a data lake house, let’s first talk about traditional data warehouses and data lakes.

Data warehouses first emerged in the 1990s to give organizations a single source of truth for reporting

and analytics.

They excel at handling structured data.

Everything fits into well-defined tables and schemas, and they power use cases like reporting dashboards

and large scale SQL analytics.

A key feature of data warehouses is that they guarantee Acid transactions.

Acid stands for atomicity, consistency, isolation, and durability.

Atomicity means that each transaction is all or nothing.

Either it fully succeeds or the system rolls back.

Consistency ensures that data always meets defined rules.

Isolation keeps concurrent transactions from interfering with each other, and durability makes committed

changes permanent.

Surviving crashes.

This reliability powers fast, accurate business intelligence.

However, traditional warehouses come with significant drawbacks.

They’re very expensive to scale because they rely on proprietary software and specialized hardware.

And as data volumes balloon, extending the capacity means costly upgrades or complex workarounds,

making it hard to keep pace with today’s diverse, high velocity data sources.

And they can only handle structured tables, so ingesting unstructured data in the form of logs, JSON,

or binary files will require separate systems.

These limitations mean that data warehouses don’t natively support machine learning workflows or real

time streaming data.

Lakes rose to prominence in the late 2000 and early 20 tens as cloud object storage became cheap and

ubiquitous.

They let you dump any data structured, semi-structured and unstructured, be it tables, JSON logs,

images, or even video into a central store without upfront schema design.

This flexibility slashed, storage costs, accelerated data capture, and unlocked new use cases like

exploratory analysis and machine learning.

This makes data lakes ideal for data science and machine learning.

They also power advanced analytics and real time streaming.

Since you can ingest event data directly and run both batch and streaming jobs on the same data store.

However, data lakes weren’t built for traditional BI and reporting.

They lacked Acid compliance and built in governance and security without built in transaction safety

in the form of Acid compliance or schema enforcement.

Data lakes can easily turn into data swamps full of inconsistent files.

So what ended up happening was that a hybrid data lake and data warehouse architecture was adopted.

The data lake side supported the machine learning and data science workloads, and the data warehouse

side supported the BI and reporting workloads.

Enter the Data Lake house.

This combined the best of data warehouses and data lakes in one open architecture, just like a data

lake.

It sits on low cost object storage and ingests every type of data structured, semi-structured and unstructured

at the same time.

It brings warehouse style, reliability and performance.

All rights follow Acid compliance guarantees so you never end up with half completed updates.

Schemas are enforced and evolved automatically, and every change is versioned so you can time travel

to any point in your data’s history.

Because the Lakehouse uses open formats like Parquet and Delta, any engine, be it SQL, Python, R,

or machine learning frameworks can read and write data natively.

You point your BI tools directly at the Lakehouse.

For live dashboards, you can run large scale SQL analytics without extracting data.

You can build and train machine learning models in the same environment, as well as operate batch and

streaming data pipelines.

Storage and compute can scale independently.

You only pay for what you use and built in governance, lineage, and auditing.

Keep your data secure and trustworthy.

With a data lakehouse, you eliminate the trade offs between cost, scale and reliability.

You keep the openness and flexibility of a data lake and gain the safety and speed of a data warehouse.

Data lake houses simplify your architecture by unifying every data workload on a single platform.