--- title: "Lakehouse: Unifying the Data Lake and Warehouse | SUMāTO" description: "What a lakehouse is and what it unifies: open formats, transactions, and BI + ML in a single place. By Andrés Lozada, SUMāTO." image: https://sumatogroup.com/hubfs/BRANDING/SUM%C4%81TO%20%7C%20LOGO%201000x500.png --- [Skip to content](https://sumatogroup.com/en/insights/blog/lakehouse-unificar-datos#main-content) - [INSIGHTS](https://sumatogroup.com/en/insights) - [SUPPORT](https://sumatogroup.com/en/support) - [CONTACT](https://sumatogroup.com/en/contact) EN [Español](https://sumatogroup.com/insights/blog/lakehouse-unificar-datos) [English](https://sumatogroup.com/en/insights/blog/lakehouse-unificar-datos) [![SUMāTO Group — home](https://sumatogroup.com/hs-fs/hubfs/BRANDING/SMT%20-%20LOGO.png?width=40&height=40&name=SMT%20-%20LOGO.png)](https://sumatogroup.com/en) - [HOME](https://sumatogroup.com/en/) - About #### SUMāTO - [About us→](https://sumatogroup.com/en/about-us) - [Terms→](https://sumatogroup.com/en/legal) - [Legal→](https://sumatogroup.com/en/legal) - [Cookies→](https://sumatogroup.com/en/legal) - [Data protection→](https://sumatogroup.com/en/legal) #### METHODOLOGIES - [Design Thinking→](https://sumatogroup.com/en/methodologies#design-thinking) - [Lean Startup→](https://sumatogroup.com/en/methodologies#lean-startup) - [PMI→](https://sumatogroup.com/en/methodologies#pmi) - [Scrum→](https://sumatogroup.com/en/methodologies#scrum) #### Vendors - [AWS→](https://sumatogroup.com/en/vendors#aws) - [Cisco→](https://sumatogroup.com/en/vendors#cisco) - [Dahua→](https://sumatogroup.com/en/vendors#dahua) - [Fortinet→](https://sumatogroup.com/en/vendors#fortinet) - [Huawei→](https://sumatogroup.com/en/vendors#huawei) - [Microsoft→](https://sumatogroup.com/en/vendors#microsoft) - [OCI→](https://sumatogroup.com/en/vendors#oci) - [Panduit→](https://sumatogroup.com/en/vendors#panduit) - Capabilities #### TECHNOLOGY - [Artificial Intelligence→](https://sumatogroup.com/en/artificial-intelligence) - [Data Analytics→](https://sumatogroup.com/en/data-analytics) - [Automation→](https://sumatogroup.com/en/automation-rpa) - [Cybersecurity→](https://sumatogroup.com/en/cybersecurity) - [Cloud→](https://sumatogroup.com/en/cloud) #### SEGMENTS - [SMB→](https://sumatogroup.com/en/smb) - [Enterprise→](https://sumatogroup.com/en/enterprise) - [Government→](https://sumatogroup.com/en/government) - Consulting #### Assessments - [AI Readiness→](https://sumatogroup.com/en/ai-readiness-assessment) - [Analytics→](https://sumatogroup.com/en/data-analytics-maturity-assessment) - [Cloud→](https://sumatogroup.com/en/cloud-readiness-assessment) - [Cybersecurity→](https://sumatogroup.com/en/cybersecurity-assessment) - [Enterprise Architecture→](https://sumatogroup.com/en/enterprise-architecture-assessment) - [IT Maturity→](https://sumatogroup.com/en/it-maturity-assessment) - [IT Strategy→](https://sumatogroup.com/en/technology-strategy-assessment) - [Process Automation→](https://sumatogroup.com/en/process-automation-assessment) #### Consulting & Architecture - [AI First→](https://sumatogroup.com/en/ai-first) - [BCP→](https://sumatogroup.com/en/business-continuity-plan) - [DRP→](https://sumatogroup.com/en/disaster-recovery-plan) - [Enterprise Architecture→](https://sumatogroup.com/en/enterprise-architecture-togaf) - [Enterprise Transformation→](https://sumatogroup.com/en/enterprise-transformation) - [IT Strategic Plan→](https://sumatogroup.com/en/it-strategic-plan) - [Strategic Consulting→](https://sumatogroup.com/en/strategic-consulting) - Operations #### INFRASTRUCTURE - [Data Center→](https://sumatogroup.com/en/data-center) - [Managed Services→](https://sumatogroup.com/en/managed-services) - [VDI→](https://sumatogroup.com/en/vdi) - [Intelligent Video Surveillance→](https://sumatogroup.com/en/video-surveillance) #### SECURITY - [NOC→](https://sumatogroup.com/en/noc) - [SOC→](https://sumatogroup.com/en/soc) #### USERS - [Modern Desktop→](https://sumatogroup.com/en/modern-desktop) - [Help Desk→](https://sumatogroup.com/en/help-desk) - Industries Industries - [Banking & Finance→](https://sumatogroup.com/en/banking-finance) - [Insurance→](https://sumatogroup.com/en/insurance) - [Government→](https://sumatogroup.com/en/government) - [Healthcare→](https://sumatogroup.com/en/healthcare) - [Telecommunications→](https://sumatogroup.com/en/telecommunications) - [Retail & Consumer→](https://sumatogroup.com/en/retail) - [Manufacturing→](https://sumatogroup.com/en/manufacturing) - [Energy, Oil & Gas→](https://sumatogroup.com/en/energy-oil-gas) - [Education→](https://sumatogroup.com/en/education) - [Logistics & Transportation→](https://sumatogroup.com/en/logistics-transport) - [Legal Services→](https://sumatogroup.com/en/legal-services) - [Engineering & Construction→](https://sumatogroup.com/en/engineering-construction) - Resources #### CONTENT - [Blog→](https://sumatogroup.com/en/insights) - [Use cases→](https://sumatogroup.com/en/use-cases) #### EVENTS - [Webinars→](https://sumatogroup.com/en/webinars) EN [Español](https://sumatogroup.com/insights/blog/lakehouse-unificar-datos) [English](https://sumatogroup.com/en/insights/blog/lakehouse-unificar-datos) Search - There are no suggestions because the search field is empty. [Inteligencia Artificial](https://sumatogroup.com/en/insights/tag/inteligencia-artificial) # Lakehouse: Unifying the Data Lake and Warehouse [Andrés Lozada](https://sumatogroup.com/en/insights/author/andres-lozada) · Mar 10, 2022, 7:00:00 AM · 7 min read For a decade, we data teams have lived with an architecture split in two: on one side the data lake, cheap and flexible, where everything lands —logs, files, events, semi-structured data—; on the other, the data warehouse, orderly and fast for business dashboards. It works, but at the cost of duplicating data, maintaining two technology stacks, and moving information back and forth endlessly. At SUMāTO we see more and more companies in LATAM asking whether they really need both. The answer maturing in 2022 has a name: lakehouse. **The short version:** The lakehouse is an architecture that puts data warehouse capabilities —transactions, schemas, query performance— directly on top of the cheap, open storage of a data lake. The idea is to have a single place where BI and machine learning coexist, without copying data to a separate system. It is not magic: it is the combination of open table formats and engines that understand them. ## The problem with keeping lake and warehouse separate The two-tier architecture became the de facto standard: you ingest everything into the lake in formats like Parquet and then load a curated subset into the warehouse for the business to query. On paper it is elegant. In practice it generates frictions that pile up. - **Duplication and cost:** the same data lives in two places. You pay for storage twice and, more expensively, you pay for the effort of keeping it in sync. - **Stale data:** between something arriving at the lake and becoming available in the warehouse, hours or days pass. Dashboards show a snapshot that has already aged. - **Fragile pipelines:** each copy process (ETL/ELT) is one more piece that breaks. When it fails, someone gets a call in the middle of the night. - **BI and ML divorced:** analysts live in the warehouse with SQL; data scientists live in the lake with Python and Spark. They work on different versions of the truth. - **Fragmented governance:** applying permissions, lineage, and quality consistently across two systems with different security models is a permanent headache. ## What a lakehouse is A lakehouse is a data architecture that adds a transactional and metadata-management layer on top of the lake's object storage (for example, cloud storage with Parquet files). That layer turns a heap of loose files into something that behaves like database tables: with schema, with version control, and with reliable transactions. Put another way: instead of moving the data to the warehouse to obtain its guarantees, you bring the warehouse's guarantees to where the data already is. A single, open repository serves both the leadership dashboard and the purchase-propensity model. ## What the lakehouse unifies The value of the lakehouse is not a single trick, but the sum of three things that historically lived apart. - **Open formats:** data is stored in open table formats (like those that have gained traction in recent years: Delta Lake, Apache Iceberg, Apache Hudi) on top of standard columnar files. It is not trapped in a warehouse's proprietary engine; any compatible tool can read it. - **ACID transactions:** the table layer brings atomicity and consistency. Multiple concurrent writes and reads do not clash, and a half-finished load does not leave the table corrupted. This is what was missing in the classic lake. - **BI and ML in a single place:** the same set of tables feeds SQL queries for dashboards and, at the same time, model training with Spark or Python. There are no two copies and no two truths. To this are added capabilities once exclusive to the warehouse: *schema enforcement* and its controlled evolution, and so-called **time travel**, which lets you query the state of a table at an earlier moment —pure gold for auditing and for reproducing an ML experiment. ## Concrete benefits When the architecture simplifies, the benefits come down to earth fast. - **A single source of truth:** fewer copies means fewer discrepancies between what the dashboard says and what the model says. - **Fewer pipelines to maintain:** by eliminating the lake-to-warehouse copy stage, there is less fragile code and fewer points of failure. - **Fresher data:** analysis works on data close to the moment it is ingested, not on an overnight load. - **More controlled costs:** object storage is cheap and is decoupled from compute, which spins up only when needed. - **Unified governance:** permissions, quality, and lineage are applied once, over a single set of tables. For an organization building [analytics](https://sumatogroup.com/analitica) capabilities and wanting those same data to support [AI](https://sumatogroup.com/ai-first) initiatives, avoiding the fork between BI and ML is perhaps the strongest argument. ## When to adopt it (and when not to rush) The lakehouse is an emerging architecture, not a silver bullet. It is worth considering when: - You already have a data lake and feel the pain of copying data to the warehouse over and over. - Your BI and machine learning use cases compete for the same data and today live in separate worlds. - The volume and variety of data (semi-structured, events, files) weigh down the traditional warehouse. - You want to avoid vendor lock-in and keep the data in open formats. It is wise to proceed with caution if your workload is modest and a well-sized warehouse already handles it without friction, or if your team does not yet have maturity operating object storage and distributed engines. The migration is not trivial: it means rethinking ingestion, governance, and consumption tools. The sensible move is usually to start with a bounded domain and grow from there. ## Frequently asked questions ### Does the lakehouse completely replace the data warehouse? That is the underlying promise, but in 2022 most organizations coexist with both during the transition. The lakehouse aspires to cover the warehouse's use cases without a separate system; getting there is a journey, not a switch. ### Do I need to throw out my current data lake to have a lakehouse? No. A lakehouse is usually built on the storage you already have, adding an open table layer on top of your files. It is more evolution than replacement. ### What sets the lakehouse apart from simply querying the lake with SQL? Querying the lake directly does not give you reliable transactions, schema control, or consistent performance. The lakehouse's transactional layer is precisely what brings those warehouse guarantees to the files. ### Does it work equally well for BI and for machine learning? That is the point. The same tables feed SQL queries for dashboards and, without copying anything, model training. That convergence is the main reason to look at it. ## The first step Before choosing a table format or an engine, the first step is honest and boring: map where your data architecture hurts today. How many times are the same data copied? How fresh do they reach your dashboards? Do BI and ML work on the same truth? With that diagnosis, deciding whether the lakehouse adds real value stops being a trend and becomes an engineering decision. At SUMāTO we help organizations across LATAM make that diagnosis and design the path —without leaps into the void. If you want to talk about your case, write to us at [sumatogroup.com/contacto](https://sumatogroup.com/contacto). Next step Can your data support the decisions you already make with it? [Data & Analytics Maturity Assessment →](https://sumatogroup.com/en/data-analytics-maturity-assessment) [Inteligencia Artificial](https://sumatogroup.com/en/insights/tag/inteligencia-artificial), [Datos y Analítica](https://sumatogroup.com/en/insights/tag/datos-y-analítica) ![Andrés Lozada](https://sumatogroup.com/hs-fs/hubfs/SPEAKERS/AL.jpeg?width=56&height=56&name=AL.jpeg) Andrés Lozada Mar 10, 2022, 7:00:00 AM [LinkedIn](https://www.linkedin.com/in/andreslozada/) ### Explore more from SUMāTO [Enterprise AI](https://sumatogroup.com/en/artificial-intelligence) [Enterprise Transformation](https://sumatogroup.com/en/enterprise-transformation) [Strategic Consulting](https://sumatogroup.com/en/strategic-consulting) [AI Agent](https://sumatogroup.com/en/artificial-intelligence) [AI Contact Center](https://sumatogroup.com/en/artificial-intelligence) [Cybersecurity](https://sumatogroup.com/en/cybersecurity) ### Related Posts #### [Data Mesh and Lakehouse: Decentralized Data](https://sumatogroup.com/en/insights/blog/data-mesh-lakehouse) For a decade, the promise was seductive: concentrate all of the organization's data in a single central repository, governed by a specialized team,... #### [Copilots: AI That Assists Every Team](https://sumatogroup.com/en/insights/blog/copilotos-ia) Over the past few months, one word has crept into every conversation about enterprise technology: copilot. The metaphor is deliberate. No one is... #### [RAG and Vector Databases: AI Grounded in Your Knowledge](https://sumatogroup.com/en/insights/blog/rag-bases-vectoriales) You try one of these new language models, it leaves you breathless with the fluency of its answers, and then you ask it about your company's return... ![SUMāTO](https://sumatogroup.com/hs-fs/hubfs/BRANDING/SUM%C4%81TO%20%7C%20LOGO%201000x500.png?width=200&height=100&name=SUM%C4%81TO%20%7C%20LOGO%201000x500.png) Strategic technology planning consultants. AI, Analytics, Cloud and Cybersecurity ## Navigation [Home](https://sumatogroup.com/en) [Capabilities](https://sumatogroup.com/en/artificial-intelligence) [Consulting](https://sumatogroup.com/en/strategic-consulting) [Operations](https://sumatogroup.com/en/managed-services) [Industries](https://sumatogroup.com/en/banking-finance) [Resources](https://sumatogroup.com/en/insights) ## SUMāTO [About](https://sumatogroup.com/en/about-us) [Terms](https://sumatogroup.com/en/legal#terminos) [Legal & Privacy](https://sumatogroup.com/en/legal) [Data protection](https://sumatogroup.com/en/legal) Cookies ## [Contact](https://sumatogroup.com/en/contact) [sales@sumatogroup.com](mailto:sales@sumatogroup.com) Mexico HQ Mexico City, Mexico [+52 55 8897 5791](tel:+525588975791) Bogotá Bogotá, Colombia [+57 601 724 5059](tel:+576017245059) © 2026 SUMāTO Group. All rights reserved. ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Andrés Lozada", "url" : "https://sumatogroup.com/en/insights/author/andres-lozada" }, "dateModified" : "2026-07-09T19:18:48.467Z", "datePublished" : "2022-03-10T13:00:00.000Z", "headline" : "Lakehouse: Unifying the Data Lake and Warehouse | SUMāTO", "mainEntityOfPage" : { "@id" : "https://sumatogroup.com/en/insights/blog/lakehouse-unificar-datos", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://sumatogroup.com/hubfs/BRANDING/Logo_SUMATO_Original%20-%201000x500.png" }, "name" : "SUMāTO Group" } } ``` ```json { "@context" : "https://schema.org", "@type" : "FAQPage", "mainEntity" : [ { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "That is the underlying promise, but in 2022 most organizations coexist with both during the transition. The lakehouse aspires to cover the warehouse's use cases without a separate system; getting there is a journey, not a switch." }, "name" : "Does the lakehouse completely replace the data warehouse?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "No. A lakehouse is usually built on the storage you already have, adding an open table layer on top of your files. It is more evolution than replacement." }, "name" : "Do I need to throw out my current data lake to have a lakehouse?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Querying the lake directly does not give you reliable transactions, schema control, or consistent performance. The lakehouse's transactional layer is precisely what brings those warehouse guarantees to the files." }, "name" : "What sets the lakehouse apart from simply querying the lake with SQL?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "That is the point. The same tables feed SQL queries for dashboards and, without copying anything, model training. That convergence is the main reason to look at it." }, "name" : "Does it work equally well for BI and for machine learning?" } ] } ``` ```json { "@context" : "https://schema.org", "@id" : "https://sumatogroup.com/#organization", "@type" : "Organization", "address" : { "@type" : "PostalAddress", "addressCountry" : "MX", "addressLocality" : "Huixquilucan", "addressRegion" : "Estado de México", "postalCode" : "52787", "streetAddress" : "Av. Vialidad de la Barranca No. 6, Torre 1, Suite 400, Piso 4, Col. Bosques de las Palmas" }, "alternateName" : [ "SUMāTO Group", "SUMATO Group", "Sumato Group", "SUMATO", "SUMaTO", "SUMaTO Group", "SUMTO", "SUMTO Group" ], "areaServed" : [ { "@type" : "Country", "name" : "México" }, { "@type" : "Country", "name" : "Colombia" }, { "@type" : "Place", "name" : "Latinoamérica" } ], "contactPoint" : { "@type" : "ContactPoint", "areaServed" : "Latinoamérica", "availableLanguage" : [ "es", "en" ], "contactType" : "sales", "email" : "sales@sumatogroup.com" }, "description" : "SUMāTO is a Latin American technology consulting and integration firm founded in 2016, with a presence in Mexico and Colombia. It designs, implements and operates artificial intelligence, data analytics, automation, cybersecurity and cloud on the systems a client already runs, under governance frameworks such as NIST AI RMF and ISO/IEC 42001.", "foundingDate" : "2016", "knowsAbout" : [ "Inteligencia Artificial", "IA Generativa", "Agentes de IA", "Analítica de Datos", "Big Data", "Automatización de Procesos (RPA)", "Ciberseguridad", "Computación en la Nube", "Continuidad del Negocio y Recuperación ante Desastres", "Arquitectura Empresarial", "Transformación Digital" ], "legalName" : "SUMāTO Group", "location" : [ { "@type" : "Place", "address" : { "@type" : "PostalAddress", "addressCountry" : "MX", "addressLocality" : "Huixquilucan", "addressRegion" : "Estado de México", "postalCode" : "52787", "streetAddress" : "Av. Vialidad de la Barranca No. 6, Torre 1, Suite 400, Piso 4, Col. Bosques de las Palmas" }, "name" : "SUMāTO MX", "telephone" : "+52 55 8897 5791" }, { "@type" : "Place", "address" : { "@type" : "PostalAddress", "addressCountry" : "CO", "addressLocality" : "Bogotá", "streetAddress" : "Cra. 45 # 103-34, Of. 202" }, "name" : "SUMāTO CO", "telephone" : "+57 601 724 5059" } ], "logo" : { "@type" : "ImageObject", "height" : 500, "url" : "https://sumatogroup.com/hubfs/BRANDING/SUM%C4%81TO%20%7C%20LOGO%201000x500.png", "width" : 1000 }, "name" : "SUMāTO", "sameAs" : [ "https://www.linkedin.com/company/sumatogroup", "https://www.youtube.com/@sumatogroup", "https://torre.ai/teams/SUMaTOGroup", "https://www.cbinsights.com/company/sumto-group", "https://elioplus.com/profiles/channel-partners/57295/sumato-group" ], "telephone" : "+52 55 8897 5791", "url" : "https://sumatogroup.com" } ``` ```json { "@context" : "https://schema.org", "@id" : "https://sumatogroup.com/#website", "@type" : "WebSite", "description" : "Technology consulting in AI, data, automation, cybersecurity and cloud across Latin America.", "inLanguage" : "en", "name" : "SUMāTO", "publisher" : { "@id" : "https://sumatogroup.com/#organization" }, "url" : "https://sumatogroup.com" } ``` ```json { "@context" : "https://schema.org", "@type" : "BreadcrumbList", "itemListElement" : [ { "@type" : "ListItem", "item" : "https://sumatogroup.com/en", "name" : "Home", "position" : 1 }, { "@type" : "ListItem", "item" : "https://sumatogroup.com/en/insights/blog/lakehouse-unificar-datos", "name" : "Lakehouse: Unifying the Data Lake and Warehouse", "position" : 2 } ] } ```