{"id":3186,"date":"2026-09-23T10:08:13","date_gmt":"2026-09-23T09:08:13","guid":{"rendered":"https:\/\/todoesdata.com\/?page_id=3186"},"modified":"2026-09-23T10:08:13","modified_gmt":"2026-09-23T09:08:13","slug":"data-platforms","status":"publish","type":"page","link":"https:\/\/todoesdata.com\/en\/products\/data-platforms\/","title":{"rendered":"Data platforms"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":8,"featured_media":2077,"parent":3156,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"tpls\/MarketingNew.php","meta":{"_acf_changed":false,"_yoast_wpseo_focuskw":"data platform","_yoast_wpseo_title":"Data platforms: data warehouse and data lake %%sep%% %%sitename%%","_yoast_wpseo_metadesc":"We design and build data platforms for companies: data warehouse, data lake, pipelines and data governance. The base that makes your BI and AI reliable.","footnotes":""},"class_list":["post-3186","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":{"title":"Data","title2":"platforms","short_desc":"

Data platforms: data warehouse, data lake and data engineering.<\/h1> Your data is spread across the ERP, the CRM, the online shop and twenty spreadsheets. Every report starts by pulling it all together again.\n

\nWe build the data platform that collects it, cleans it and gets it ready<\/strong> for your dashboards and your AI systems.","line_mark":null,"text_block":"","blocks":null,"logo_slider_title":"","logo_slider":null,"3column_title":"","3column_block":null,"secciones":[{"acf_fc_layout":"definicion","fondo":"blanco","kicker":"Data platforms","titulo":"What a data platform is","lead":"The single place where data from across the company arrives on its own, gets cleaned and is ready to query with confidence.","resaltado":"arrives on its own, gets cleaned and is ready","cuerpo":"A data platform brings three things together. A central store, a data warehouse or a data lake, where the data from every system is kept. The data engineering processes (pipelines, ETL) that collect it every night from the ERP, the CRM, the online shop or the spreadsheets. And the quality and governance rules that make sure a customer is one customer and a sale is counted once.\n\nIt is what used to be called Big Data, with one practical difference: in a modern data platform, volume matters less than reliability<\/strong>. A company of 80 people doesn't need to process petabytes. It needs the margin leadership sees to come from the same data finance uses. And it needs an AI assistant to be able to query that data without making anything up.","datos":[{"cifra":"1","etiqueta":"central store for every report and every AI system"},{"cifra":"02:00","etiqueta":"usual time of the automatic load: the data is ready when the working day starts"},{"cifra":"0","etiqueta":"manual exports between the source system and the report"}]},{"acf_fc_layout":"triptico","fondo":"naranja","titulo":"Three quick questions","intro":"The three that come up in every first meeting.","items":[{"etiqueta":"What it is","pregunta":"Isn't this the same as Business Intelligence?","respuesta":"No. BI is what you see: dashboards and metrics. The platform is what sits underneath: where the data lives, how it arrives and who guarantees it is correct. Without it, every dashboard queries the ERP in its own way."},{"etiqueta":"Who it's for","pregunta":"Does it make sense for my company?","respuesta":"Yes, in two cases. If you have more than two systems that don't talk to each other and someone cross-checks their data by hand every week. Or if you are considering AI agents and already know your data needs work."},{"etiqueta":"When","pregunta":"How long until it is up and running?","respuesta":"A first warehouse, with your two or three most important sources loading on their own, in 6-10 weeks. The other sources are added afterwards, one at a time."}]},{"acf_fc_layout":"tarjetas_icono","fondo":"gris","titulo":"How to tell if you need it","intro":"Nobody asks for a data platform for the fun of it. Companies ask for one after hitting one of these four walls.","items":[{"icono":"capas","titulo":"Every new question is a project","texto":"Crossing sales with production costs means requesting three extracts, building a spreadsheet and waiting until someone has time. The answer arrives when it is no longer useful.","enlace":null},{"icono":"alerta","titulo":"The same customer exists three times","texto":"Under different names in the ERP, the CRM and the online shop. Nobody knows how much they buy in total, and the sales rep hears about the unpaid invoice when finance calls.","enlace":null},{"icono":"reloj","titulo":"Reports are slow because the ERP is choking","texto":"Every heavy query slows down the people doing the invoicing. So reports are run at night, or not run at all.","enlace":null},{"icono":"cerebro","titulo":"You want AI and there is nothing to build it on","texto":"Someone tries an assistant over the data and it answers with figures that don't match the official report. The problem isn't the model: there is no single source to query.","enlace":null}]},{"acf_fc_layout":"tarjetas_imagen","fondo":"blanco","titulo":"What we build","filtro":false,"intro":"Six separate pieces, from data architecture consulting to the data engineering that keeps every load running. You can hire them together or one at a time, depending on what your company actually needs.","items":[{"imagen":3160,"etapa":"","titulo":"Architecture and source integration","texto":"ERP, CRM, production, e-commerce, banks and spreadsheets, loaded every night into a central store. We decide what goes into the data warehouse and what into the data lake, sized to your company.","enlace":null},{"imagen":3187,"etapa":"","titulo":"Data quality measured on every load","texto":"Automatic rules: duplicate customers, amounts with and without VAT mixed together, orders with no date. A dashboard shows what percentage of the data is valid and who should correct it.","enlace":null},{"imagen":3189,"etapa":"","titulo":"Data catalogue and governance","texto":"Which tables exist, what each field means, who owns it and who can see it. That way a new employee, or an AI agent, finds the right data without having to ask.","enlace":null},{"imagen":3191,"etapa":"","titulo":"Pipelines that watch themselves","texto":"The processes that move and transform the data, under monitoring. If a load fails at 03:00, someone knows by 03:05, and nobody opens a report with data from the day before yesterday.","enlace":null},{"imagen":3193,"etapa":"","titulo":"Data model by business domain","texto":"Sales, customers, product, finance and operations, modelled once and with the same keys. Crossing anything with anything stops being a project.","enlace":null},{"imagen":3195,"etapa":"","titulo":"Semantic layer for BI and AI","texto":"Metrics defined once and exposed in the same way to the dashboard and to the AI agent. This is where the platform becomes the foundation for the rest of the catalogue.","enlace":{"title":"Business Intelligence","url":"https:\/\/todoesdata.com\/en\/products\/business-intelligence\/","target":""}}]},{"acf_fc_layout":"pasos","fondo":"gris","titulo":"How we work","intro":"Five steps. The first one isn't choosing a technology.","items":[{"titulo":"An honest inventory","texto":"Which systems there are, what data they hold, how they are accessed, who they depend on and what state they are in. This is where the timeline and the budget come from."},{"titulo":"The questions to answer first","texto":"We agree with leadership on what the platform really needs to do. Answering the key questions is always cheaper than answering everything \u2018just in case\u2019."},{"titulo":"A small warehouse that works","texto":"With quality measured from day one, so it can be used, criticised and adjusted with the team in the room. Whatever isn't used in the first few weeks never gets used."},{"titulo":"Grow source by source","texto":"Every new system comes in with its owner, its quality rules and its documentation. No exceptions: whatever comes in without an owner ends up as noise."},{"titulo":"Hand over and step back","texto":"The code, the model and the documentation are yours. We train whoever will maintain it; if you would rather we did it, that can be agreed. We don't work to make you dependent on us."}]},{"acf_fc_layout":"logos","fondo":"blanco","titulo":"We work with the technology you already have","muro":false,"items":[{"nombre":"Microsoft Azure","logo":null,"enlace":null},{"nombre":"Azure Data Factory","logo":null,"enlace":null},{"nombre":"Microsoft Fabric","logo":null,"enlace":null},{"nombre":"Google Cloud","logo":null,"enlace":null},{"nombre":"AWS","logo":null,"enlace":null},{"nombre":"SQL Server","logo":null,"enlace":null},{"nombre":"Python","logo":null,"enlace":null},{"nombre":"Snowflake","logo":null,"enlace":null},{"nombre":"BigQuery","logo":null,"enlace":null},{"nombre":"Databricks","logo":null,"enlace":null},{"nombre":"dbt","logo":null,"enlace":null},{"nombre":"Airflow","logo":null,"enlace":null}]},{"acf_fc_layout":"diagrama","fondo":"naranja","titulo":"Without a platform, AI sounds confident and gets it wrong","texto":"The platform stores and organises; AI queries and acts. The platform makes sure the data exists, is clean and means the same thing across the company. An AI agent reads it, reasons over it and carries out a task.\n\nNeither replaces the other. The platform goes underneath, and you notice it with the very first question. \u2018How much have we sold to this customer?\u2019 only has a reliable answer if there is a single source where that customer is one customer and their sales are complete. Without it, the most expensive model on the market answers confidently and gets it wrong.\n\nIn practice, the AI projects that go well start here. First the warehouse with sales and payments. Then the dashboard that shows them. And finally the agent that warns the sales rep before the unpaid invoice reaches finance.","diagrama":"plataformas","imagen":2798},{"acf_fc_layout":"slider","titulo":"Where it applies","items":[{"titulo":"Month-end close","texto":"Accounting, banks and invoicing in the same store, with the reconciliation done before the close arrives. The finance team stops chasing extracts.","imagen":2883,"enlace":null},{"titulo":"Single customer view","texto":"ERP, CRM and online shop reconciled, so you know who each customer is, how much they buy in total and how much they owe. It often reveals a customer base more concentrated than anyone thought, or customers who are chronically late to pay.","imagen":2885,"enlace":null},{"titulo":"Operations and the plant floor","texto":"Production, maintenance and quality data collected from machines and plant systems, and set against what they cost. This is where technical data turns into an economic decision.","imagen":2907,"enlace":null},{"titulo":"Stock and purchasing","texto":"Inventory, orders and suppliers in a shared model, so requirements are calculated every night without depending on each warehouse manager's judgement.","imagen":2887,"enlace":null},{"titulo":"A base for AI agents","texto":"A warehouse with defined metrics and a documented catalogue, on which an agent can answer questions or carry out tasks without inventing figures.","imagen":2881,"enlace":null}]},{"acf_fc_layout":"faq","fondo":"gris","titulo":"Frequently asked questions","intro":"The ones asked before the budget is approved. If yours is missing, write to us.","enlace":{"title":"Let's talk about your project","url":"https:\/\/todoesdata.com\/en\/contact\/","target":""},"items":[{"pregunta":"How much does a data platform cost?","respuesta":"As with any data engineering consulting, it depends on the number of sources and the state they are in, not on the size of the company. A one-hour session looking at your systems is enough to fix a price. Before that, any figure would be a guess."},{"pregunta":"In the cloud or on our own servers?","respuesta":"Almost always in the cloud: you pay for what you use, there is no server to maintain and scaling up takes a click. On-premise only when a legal or contractual requirement means the data cannot leave your premises."},{"pregunta":"How long does it take?","respuesta":"The first useful warehouse, 6-10 weeks. The full platform, 4-8 months depending on the number of sources. Each source that comes in is useful straight away: you don't have to wait until the end."},{"pregunta":"Data warehouse or data lake?","respuesta":"It is the first question in any data warehouse consulting project. A warehouse if your data is tables (ERP, CRM, accounting) and the question is a business one. A lake if you store documents, images or machine data, or you want to feed AI models. Many mid-sized companies end up with both."},{"pregunta":"Can we migrate from Excel and the ERP without stopping the business?","respuesta":"Yes, that is the usual way. The ERP isn't touched: the platform reads from it. Spreadsheets are loaded as they are and replaced by their real source as they get documented. Nobody stops working."},{"pregunta":"And what does this have to do with AI?","respuesta":"It is what makes AI work. An agent that queries company data needs a single, clean, documented source; otherwise it answers with figures nobody can defend. That is why AI agent projects usually start here."},{"pregunta":"In which industries is this the first thing to do?","respuesta":"In healthcare<\/a> it always comes first, and it is the only industry where that happens. Until it is written down which data leaves, where it is processed and who can see what, the project doesn't get past the approval meeting. It comes third in manufacturing and food<\/a>, to pull up the traceability of a batch instantly instead of rebuilding it by hand."}]}]},"yoast_head":"\nData platforms: data warehouse and data lake | Todoesdata<\/title>\n<meta name=\"description\" content=\"We design and build data platforms for companies: data warehouse, data lake, pipelines and data governance. 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