HareHare Data Platform

The flexibility of a data lake.
The reliability of a warehouse.

Lakehouse is not a buzzword — it is where storage, table formats and compute naturally converged as each evolved. Hare turns it into a platform you can deploy today.

Evolution

How data platform architecture evolved.

Each generation emerged to fix the structural limits of the one before. Scroll to see how it all led to the lakehouse.

2000s

Data warehouse

  • Fast queries, strict schemas
  • Consistent transactions, mature governance
  • Capacity and cost ceilings
  • Structured data only
2010s

Data lake

  • Scale out on commodity hardware
  • Store any format
  • Storage and compute tied together
  • No transactions — lakes turn into swamps
2020s

Lakehouse · Hare

  • Object storage + open table format
  • Decoupled, independent scaling
  • ACID, versioning, schema evolution
  • Many engines on the same data
Why now

What drove the shift.

Industry-level changes in hardware and standards — owned by no single vendor. That is why every platform is heading the same way.

2006
2012
2018
Today

Networks and storage got fast

1 GbE → 25 / 100 GbE, hard disks → NVMe. Reading data remotely is no longer the bottleneck, so compute no longer has to sit next to the data — storage and compute can finally separate.

On-premCloud A
S3 API
SQL enginesAI frameworks

Object storage became a standard

The S3 API is now the common language of storage — the same on-prem and in the cloud, supported by every tool. Wherever the data lives, applications don't change.

ACID transactions
Versions / Time Travel
Schema evolution
Open data files

Open table formats matured

A reliable metadata layer on top of open data files gives the data lake warehouse-grade transactions and governance for the first time.

Comparison

Warehouse, lake, lakehouse — at a glance.

CapabilityData warehouseData lakeHare Lakehouse
Structured + semi-structured + unstructured✕✓✓
ACID transactions and consistency✓✕✓
Time Travel (version history)△✕✓
Independent scaling of storage and compute△✕✓
Many engines on the same data✕△✓
Open formats, no lock-in✕✓✓
Column / row-level access and audit✓△✓
Direct access for AI / ML△✓✓
Try it

How does Hare make a table reliable?

Every write creates a new snapshot while older snapshots are kept. Use the buttons on the left to change the data, then click any snapshot above to go back in time.

Adding a column rewrites no data files — existing rows simply show null.

ACID transactionsSnapshots switch only when a write completes — readers always see complete data
Time TravelQuery any past version; recover from mistaken deletes or updates
Schema evolutionColumns tracked by ID — change tables without rewriting data
Open data filesStill Parquet underneath, readable by any engine

Hare's table format layer is built on Apache Iceberg.

The secret to speed

Not scanning faster — scanning less.

Hare keeps min / max statistics for every data file. At query time, files that can't match the filter are skipped entirely. Try different filters below.

Files scanned 24 / 24Skipped 0Data read 0 MB
Looking ahead

Where things are clearly heading.

Hare sits on the path of each of these trends — new capabilities plug in rather than requiring a rebuild.

🧭

Catalog standards

Table catalog interfaces are converging on a common standard, making it ever cheaper to switch or add engines.

⚡

Streaming into the lake

Events are written straight into open tables, skipping intermediate batch ETL for fresher data.

🤖

AI workloads converge

Object storage feeds model training directly, and vector search shares the same platform as analytics.

🔗

Many engines, one copy

SQL, Spark, streaming and Python share the same tables — no more copying data for every tool.

Wondering what a lakehouse would look like in your company?

Start with an assessment and get a concrete target architecture.

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