Your company wants AI. Its data lives in eleven systems that don’t talk to each other.
Kavuka Data Lake is your company’s data foundation: it ingests every source in batch and streaming, preserves reprocessable raw data, catalogs with lineage and ownership and governs access by zone and sensitivity — in open formats, ready to evolve into a lakehouse with no migration.
- Batch + streaming
- all sources ingested
- Raw preserved
- the reprocessable source of truth
- Lineage + owner
- living catalog, no swamp
- Open formats
- lakehouse one step away
The offering comes from a team that built it for itself: GUÉP runs its own petabyte-scale data infrastructure — pipelines, catalog and governance in production, not on a slide.
Before any dashboard or AI, someone has to answer: where does the data live?
The data hunt before every analysis
Every new question starts with weeks locating, extracting and understanding data trapped in the source system — and the history the transactional layer has already discarded.
The lake that became a swamp
When someone “dumps everything to the cloud” without catalog, governance and open formats, the repository degenerates into an unusable dump — no owner, no map, no trust.
The lake as a data-protection liability
Personal data replicated without control and access without governance turn the repository into regulatory risk, not a company asset.
Cost Without the foundation, every data initiative pays the hunt tax — weeks to locate, extract and understand what should be one query away; history is lost when transactional systems discard it; and the AI the board asked for is held hostage by the question nobody answered: where does the data live?
The foundation before the building, built once and good for everything.
- 01
Design
The architecture for your case — cloud, on-premises or hybrid — with cost modeled before the decision.
- 02
Ingest
The source pipelines — databases, APIs, events, files — in batch and streaming, with history preserved.
- 03
Catalog and govern
The living map (what exists, where, owned by whom, with what quality and lineage) and access by zone and sensitivity.
- 04
Evolve
The lakehouse path ready: open formats from day one, switched on when analytics demands it — with no migration.
The layers of the foundation
From raw source to governed decision: each layer solves a piece of the “where does the data live?” problem and delivers a repository that is queryable, documented and secure.
Ingestion
Databases, APIs, events and files — batch and streaming
Storage
Low-cost object, raw/curated zones, lifecycle
Catalog
Living inventory: discovery, lineage, owner and quality
Access governance
Permissions by zone, domain and sensitivity — privacy by design
Raw preserved
The reprocessable source of truth, the history that stays
Cost under control
Cheap object + lifecycle — the CFO’s argument
Vendor independence
Cloud, on-premises or hybrid, per your case
Lakehouse evolution
Open formats (Delta, Iceberg) from day one
Who builds the foundation with Kavuka Data Lake
Companies starting their data journey
The right foundation before the first dashboard, without the debt of a rushed choice.
Data trapped in systems
Freeing data for analysis without overloading the source — data in one queryable place.
AI projects
The data prerequisite answered: the base where AI finally has solid ground to stand on.
Those who need the past
Regulatory, audit and models that require preserved, reprocessable history.
Governance and data protection designed into the foundation
In Kavuka Data Lake compliance is not a patch at the end — it is born in the architecture. Personal data is mapped and zoned, access is by sensitivity and formats are open: the repository is a governed asset, not a replicated liability.
- Personal data mapped and zoned from ingestion, with access governed by sensitivity and domain.
- Retention by policy and lifecycle — the history that must stay, what can leave at the right time.
- Trail and lineage per dataset: where it came from, who accessed it, how it was transformed.
- Open formats (Delta Lake, Apache Iceberg): no vendor lock-in, no migration debt.
- The credential of a team that runs its own petabytes: the right choices built into the deployment.
The data hunt is over: the analysis that took weeks just to find the source now starts with a query.
We were born in open formats, so turning on the lakehouse was one step — not the migration project I feared.
The DPO stopped treating the repository as a risk: personal data is zoned and access is governed by sensitivity.
Bring the map of your systems. We return the foundation blueprint.
With estimated cost — the consultation that becomes a project. The foundation is built once and serves everything.
- For businesses only. No purchase commitment.
- Data used solely for commercial contact.
- Enterprise leads answered within 1 business day.
What a data lake is and how to build the right foundation
A data lake is the company’s data foundation: the central repository that receives all data, in any format — transactional, logs, events, files, images, APIs — stored on low-cost object, preserved in raw state (the reprocessable source of truth) and cataloged, meaning discovered, documented, with an owner and lineage. It answers the question that precedes any analytics or AI project: where does the data live? It is not the dashboard or the model — it is the ground both stand on.
Cloud object storage (S3, ADLS, GCS) became the universal substrate — cheap, durable and practically unlimited. But the lesson of the decade was the swamp: a lake without catalog, governance and transactional formats degenerates into an unusable dump. The industry responded with the lakehouse evolution — the open-table layer (Delta Lake, Apache Iceberg) on top of object storage, bringing data-warehouse guarantees to the lake at the lake’s cost. The practical consequence for those starting today is direct: the lake is born ready to become a lakehouse — open formats, catalog and governance from day one, with no migration debt.
It is worth understanding the differences. A data lake stores everything, raw and cheap, in any format; a data warehouse structures data for analysis with transactional guarantees, but is expensive and rigid; the lakehouse unites the two — the open-table layer on object storage, with warehouse guarantees at the lake’s cost. The lake is the foundation; the lakehouse, the evolution. What separates a foundation from a dump is not how much you store, but what you build around it: a living catalog (discovery, lineage, owner), organized zones (raw and curated), access governance and monitored quality — designed at deployment, not patched in later.
The Kavuka offering covers the whole cycle: lake architecture and deployment (cloud, on-premises or hybrid), source ingestion pipelines, the catalog and access governance, and the natural evolution path — the Lakehouse, when the foundation needs to become a transactional analytics platform. The topology follows your case — volume, cost, latency, sovereignty and regulation —, with cost modeled before the decision and vendor independence as part of the offering. And governance is born at the base: personal data mapped and zoned, access by sensitivity, retention by policy and the trail — the lake as a governed asset, not a replicated liability. The credential: GUÉP runs its own petabyte-scale data infrastructure. The foundation comes from a team that built it for itself.
What is the difference between data lake, data warehouse and lakehouse?
The lake stores everything, raw and cheap, in any format; the warehouse structures data for analysis with transactional guarantees, but is expensive and rigid; the lakehouse unites the two — the open-table layer over object storage, with warehouse guarantees at the lake’s cost. The lake is the foundation; the lakehouse, the evolution (it has its own page).
How do I keep the lake from becoming a swamp?
With what separates a foundation from a dump: a living catalog (discovery, lineage, owner), organized zones (raw/curated), access governance and monitored quality — designed at deployment, not patched in later.
Cloud or on-premises?
It depends on the case: volume, cost, latency, sovereignty and regulation. We work all three topologies — cloud, on-premises and hybrid — with cost modeled before the decision; vendor independence is part of the offering.
What about data protection in a repository that holds everything?
Governance is born in the foundation: personal data mapped and zoned, access by sensitivity, retention by policy and the trail — the lake as a governed asset, not a replicated liability.
When should I evolve to a lakehouse?
When analytics demands transactional guarantees, BI directly on the lake or the AI base — and because we are born in open formats, the evolution is incremental: the same tables, new capabilities. No migration, no debt to repay.
How long does deployment take?
We start with the design: you bring the map of your systems and we return the foundation architecture with estimated cost. From there deployment is incremental — the first sources ingested and cataloged before covering the rest, with the right choices already built in by a team that runs its own petabytes.
Does the Data Lake connect with the other Kavuka solutions?
Yes. The foundation feeds the platform: the Lakehouse is its analytical evolution, Entity Resolution resolves the identity of records and Data Enrichment adds external content. The lake is the ground; the other solutions build on top.
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