{"id":13114,"date":"2026-05-10T07:26:26","date_gmt":"2026-05-10T06:26:26","guid":{"rendered":"https:\/\/sparkle.consulting\/glossary\/"},"modified":"2026-07-22T14:19:31","modified_gmt":"2026-07-22T13:19:31","slug":"glossary","status":"publish","type":"page","link":"https:\/\/sparkle.consulting\/fr\/glossary\/","title":{"rendered":"Glossaire \u00b7 Termes Data, IA &amp; Microsoft"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"13114\" class=\"elementor elementor-13114\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-s3zyp4u elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"s3zyp4u\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container 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.site-content,.elementor-page #primary,.elementor-page .site-main{padding:0!important;margin:0!important;max-width:100%!important}\nbody.elementor-page::before{content:'';position:fixed;top:0;left:0;right:0;height:3px;background:linear-gradient(90deg,#af6d00 0%,#f29100 50%,#af6d00 100%);z-index:99999;pointer-events:none}\nbody.ast-theme-transparent-header:not(.sparkle-scrolled) .main-header-bar,body.ast-theme-transparent-header:not(.sparkle-scrolled) .ast-main-header-wrap{background-color:transparent!important;background-image:none!important;box-shadow:none!important}\nbody.sparkle-scrolled .ast-hfb-header .site-header,body.sparkle-scrolled #masthead,body.sparkle-scrolled .main-header-bar{background:rgba(22,62,82,.78)!important;backdrop-filter:blur(14px) saturate(140%);box-shadow:0 1px 0 rgba(255,255,255,.06),0 8px 24px rgba(0,0,0,.18)}\nbody.sparkle-scrolled .ast-hfb-header .menu-item>a{color:#fff!important}\n<\/style>\n<script id=\"sparkle-scroll-js\">(function(){function check(){document.body.classList.toggle('sparkle-scrolled',window.scrollY>40)}window.addEventListener('scroll',check,{passive:true});check();})();<\/script><style>\n.gl-hero{background:#163e52;color:#fff;padding:104px 0 64px;position:relative;overflow:hidden;font-family:'Inter',sans-serif}\n.gl-hero:before{content:'';position:absolute;inset:0;background:radial-gradient(circle at 22% 28%,rgba(242,145,0,.12) 0%,transparent 55%),radial-gradient(circle at 80% 72%,rgba(86,127,147,.24) 0%,transparent 50%);pointer-events:none}\n.gl-hero .wrap{max-width:880px;margin:0 auto;padding:0 32px;position:relative;z-index:1;text-align:center}\n.gl-hero .crumbs{font-size:13px;color:#93aab7;margin-bottom:32px}\n.gl-hero .crumbs a{color:#93aab7;text-decoration:none}\n.gl-hero .crumbs span{color:#fff;margin:0 10px;opacity:.45}\n.gl-hero .eyebrow{display:inline-flex;gap:12px;font-size:12px;font-weight:600;letter-spacing:.18em;text-transform:uppercase;color:#f29100;margin-bottom:24px}\n.gl-hero .eyebrow:before,.gl-hero .eyebrow:after{content:'';width:36px;height:1px;background:#f29100;display:block;align-self:center}\n.gl-hero h1{color:#fff!important;font-size:clamp(38px,5vw,56px);line-height:1.05;font-weight:700;letter-spacing:-.022em;margin:0 auto 20px;max-width:760px;font-family:'Inter',sans-serif}\n.gl-hero h1 em{font-style:italic;color:#f29100;font-weight:600}\n.gl-hero .lede{font-size:18px;line-height:1.55;color:#cdd9df;max-width:680px;margin:0 auto}\n@media(max-width:720px){.gl-hero{padding:80px 0 48px}.gl-hero h1{font-size:32px}}\n<\/style>\n<section class=\"gl-hero\"><div class=\"wrap\">\n<div class=\"crumbs\"><a href=\"\/\">Home<\/a><span>\/<\/span><a href=\"\/resources\/\">Resources<\/a><span>\/<\/span>Glossary<\/div>\n<div class=\"eyebrow\">Reference<\/div>\n<h1>The data, AI and Microsoft <em>terms that matter<\/em>.<\/h1>\n<p class=\"lede\">A working glossary of the technologies, regulations and patterns we ship every day \u00b7 written for executives, engineers and everyone in between.<\/p>\n<\/div><\/section><script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"DefinedTermSet\",\"name\":\"Sparkle Data & AI Glossary\",\"url\":\"https:\/\/sparkle.consulting\/glossary\/\",\"hasDefinedTerm\":[{\"@type\":\"DefinedTerm\",\"name\":\"RAG (Retrieval-Augmented Generation)\",\"description\":\"Retrieval-Augmented Generation. Grounding an LLM's answers in your own documents by retrieving relevant passages at query time, so responses are accurate, current and citable instead of invented.\"},{\"@type\":\"DefinedTerm\",\"name\":\"LLM (Large Language Model)\",\"description\":\"Large Language Model. A model trained on vast amounts of text to generate and reason over language (GPT, Claude, Gemini, Llama). The engine behind chat, drafting, summarisation and agentic AI.\"},{\"@type\":\"DefinedTerm\",\"name\":\"Embeddings & vector database\",\"description\":\"Numeric representations of text or images that capture meaning, stored in a vector database for similarity search. The retrieval layer behind RAG and semantic search.\"},{\"@type\":\"DefinedTerm\",\"name\":\"MLOps\",\"description\":\"Practices and tooling to deploy, monitor and retrain machine-learning models reliably: versioning, CI\/CD, drift detection and governance across the model lifecycle.\"},{\"@type\":\"DefinedTerm\",\"name\":\"dbt\",\"description\":\"Data build tool. A SQL-first framework for transforming data in the warehouse with version control, testing, documentation and lineage. Runs on Fabric Warehouse via the dbt-fabric adapter.\"},{\"@type\":\"DefinedTerm\",\"name\":\"Databricks\",\"description\":\"Lakehouse platform built on Apache Spark and Delta Lake for large-scale data engineering, ML and analytics. On Azure it integrates with OneLake and Microsoft Fabric.\"},{\"@type\":\"DefinedTerm\",\"name\":\"Data lineage\",\"description\":\"The traceable path of data from source system to dashboard cell. Makes impact analysis, auditing and trust possible; automated in Microsoft Purview.\"},{\"@type\":\"DefinedTerm\",\"name\":\"Row-Level Security (RLS)\",\"description\":\"Filtering rows in a semantic model or database by who is querying, so each user sees only the data they are permitted to. Core to governed self-service BI.\"},{\"@type\":\"DefinedTerm\",\"name\":\"Azure Synapse\",\"description\":\"Microsoft's previous-generation analytics service (SQL pools, Spark, pipelines). Microsoft Fabric is its SaaS successor; most new mid-market builds start on Fabric.\"},{\"@type\":\"DefinedTerm\",\"name\":\"Mirroring (Microsoft Fabric)\",\"description\":\"Near real-time replication of an operational database (Azure SQL, Cosmos DB, Snowflake and more) into OneLake at no compute cost, often removing an entire ETL project.\"},{\"@type\":\"DefinedTerm\",\"name\":\"Agentic AI\",\"description\":\"AI systems that plan, call tools, and take actions to achieve goals \u00b7 with optional human-in-the-loop on critical decisions. The next layer beyond chatbots and copilots.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"AI Act\",\"description\":\"EU regulation establishing harmonised rules on artificial intelligence. In force from August 2024 with phased enforcement: prohibited practices Feb 2025, GPAI obligations Aug 2025, high-risk system obligations Aug 2026, full enforcement Aug 2027.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Article 9 (AI Act)\",\"description\":\"Risk management system obligation for high-risk AI \u00b7 the spine of the entire compliance framework. Requires identifying, analysing and mitigating risks throughout the AI system lifecycle.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Azure AI Foundry\",\"description\":\"Microsoft's unified platform for building, deploying and managing AI applications \u00b7 including OpenAI models, Azure ML, fine-tuning, evaluation harnesses and Responsible AI guardrails.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Bronze \/ Silver \/ Gold (medallion)\",\"description\":\"Layered data architecture pattern in lakehouse: Bronze = raw events, Silver = cleansed and conformed, Gold = curated business-ready. Standard pattern in Microsoft Fabric and Databricks.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Capacity (Microsoft Fabric)\",\"description\":\"Compute unit in Microsoft Fabric, sized in F-units (F2, F4, F8 ... F2048). Most mid-market starts with F8 or F16 for the first three months \u00b7 F32+ is rarely needed for organisations under 500 employees.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Centre of Excellence (CoE)\",\"description\":\"Governance structure for citizen-developer programs \u00b7 provides environments, DLP policies, ALM pipelines, naming standards, and a certified template gallery. Essential for safely scaling Power Platform adoption.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Citizen developer\",\"description\":\"Business user who builds applications, automations or reports using low-code tools (typically Power Platform) \u00b7 with IT-defined guardrails. Absorbs the 80\/20 of low-risk work that traditional engineering teams cannot ship fast enough.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Copilot Studio\",\"description\":\"Microsoft platform for building domain-specific AI agents grounded on enterprise data (SharePoint, Dataverse, Microsoft Fabric, custom APIs). Successor to Power Virtual Agents.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Direct Lake\",\"description\":\"Power BI storage mode that queries data directly from OneLake delta tables without import or DirectQuery. Provides sub-second query performance on tables of billions of rows.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"DORA\",\"description\":\"EU Digital Operational Resilience Act \u00b7 regulation for financial sector entities on ICT risk management, third-party risk, incident reporting and cyber resilience. In force from January 2025.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"EU AI Act risk tiers\",\"description\":\"Four-level classification: unacceptable risk (prohibited), high risk (Articles 9-17 obligations), limited risk (transparency obligations Art. 50), minimal risk (no specific obligations). Most enterprise use cases fall in limited or high.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"EventStream (Microsoft Fabric)\",\"description\":\"Real-time data ingestion service in Microsoft Fabric \u00b7 pipes events from Event Hubs, Kafka, IoT Hub or custom sources into KQL databases or lakehouses for sub-minute analytics.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Fabric (Microsoft Fabric)\",\"description\":\"Microsoft's unified SaaS analytics platform combining data engineering, warehousing, real-time intelligence, data science, BI and AI \u00b7 all built on OneLake with separated storage and compute. SaaS evolution of Synapse.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"FAQPage schema\",\"description\":\"Schema.org structured data markup for FAQ content. Enables rich results in Google search and improves LLM citation likelihood for question-pattern content.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Feature store\",\"description\":\"Centralized repository for reusable ML features \u00b7 ensures consistency between training and serving, reduces feature-engineering duplication across use cases, supports lineage and access control.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"GDPR-Health\",\"description\":\"GDPR Article 9 special category data protections applied to health information \u00b7 processing prohibited by default, with limited exceptions (consent, public interest, healthcare provision). Stricter than general personal data.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"GPAI (General-Purpose AI)\",\"description\":\"AI models with broad capabilities (GPT-4, Claude, Gemini, Llama, Mistral). Subject to specific AI Act obligations under Articles 51-55, scaled by computational resources used in training.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"KQL (Kusto Query Language)\",\"description\":\"Query language for time-series and log analytics in Azure Data Explorer and Microsoft Fabric KQL Database \u00b7 optimised for sub-second queries on billions of events.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Lakehouse\",\"description\":\"Architecture combining data lake (low-cost object storage, open formats) with warehouse (ACID transactions, schema, performance). Microsoft Fabric, Databricks Delta Lake, Snowflake Iceberg are lakehouse platforms.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"MDR (Medical Device Regulation)\",\"description\":\"EU regulation governing medical devices including software \u00b7 diagnostic AI often dual-regulated under MDR + AI Act high-risk obligations. Requires CE marking, post-market surveillance, technical documentation.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"NIS2\",\"description\":\"EU directive on cybersecurity for essential and important entities \u00b7 expanded scope (energy, transport, banking, health, public administration, digital infrastructure, manufacturing). National transposition deadline October 2024.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"OneLake\",\"description\":\"Single logical lake underlying all Microsoft Fabric workloads \u00b7 open Delta Parquet format, automatic governance, shortcuts to other storage (Synapse, S3, ADLS, Snowflake) without copying data.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Power Platform\",\"description\":\"Microsoft low-code suite: Power BI (analytics), Power Apps (custom apps), Power Automate (workflow), Copilot Studio (AI agents), AI Builder (ML on tap), Pages (websites). Foundation for citizen-developer programs.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Purview (Microsoft Purview)\",\"description\":\"Microsoft's unified data governance platform \u00b7 classification, sensitivity labels, lineage, data discovery, AI risk register. Replaces Information Protection + Compliance Manager + Data Map under one brand.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Real-Time Intelligence (Fabric)\",\"description\":\"Microsoft Fabric workload for streaming analytics \u00b7 combines EventStream, KQL Database, Activator (alerting). Sub-minute freshness with native integration into Power BI and AI workloads.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Responsible AI\",\"description\":\"Practice of designing, building and deploying AI with fairness, reliability, safety, privacy, security, inclusiveness, transparency and accountability built in. Operationalised through evaluation harnesses, content filters, human-in-the-loop, model cards.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Schema markup\",\"description\":\"Structured data added to web pages using Schema.org vocabulary (JSON-LD format) \u00b7 helps search engines and LLMs understand entity relationships. Common types: Organization, Person, FAQPage, BreadcrumbList, Article.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Semantic model\",\"description\":\"Logical layer over your data with certified definitions, calculated measures, row-level security, calculation groups, hierarchies. The spine of governed self-service BI \u00b7 the reports become commodity, the model is the asset.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Shortcut (Microsoft Fabric)\",\"description\":\"Pointer in OneLake to data physically stored elsewhere (Synapse, ADLS, S3, Snowflake) \u00b7 enables querying without copying. Critical for incremental migration and avoiding data duplication.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Solution Partner (Microsoft)\",\"description\":\"Microsoft Partner Network designation requiring scoring on partner capability score (skills, deployments, customer success, performance). Sparkle holds Solution Partner Data & AI \u00b7 Azure plus Solutions Partner Power Platform \u00b7 Modern Work.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"},{\"@type\":\"DefinedTerm\",\"name\":\"Tier-1 \/ Tier-2 \/ Tier-3 KPI\",\"description\":\"KPI hierarchy framework: Tier-1 (GOLD) moves strategy \u00b7 5-7 metrics for board and exec; Tier-2 (SILVER) moves operations \u00b7 10-15 metrics; Tier-3 (BRONZE) supports specialists \u00b7 30+. Prevents the \\\"55 KPIs all at executive level\\\" trap.\",\"inDefinedTermSet\":\"https:\/\/sparkle.consulting\/glossary\/\"}]}<\/script>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-s0auk1l elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"s0auk1l\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cdz3i6r\" data-id=\"cdz3i6r\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-wx0h96g elementor-widget elementor-widget-html\" data-id=\"wx0h96g\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n.gl-list{background:#fff;padding:64px 0 96px;font-family:'Inter',sans-serif}\n.gl-list .wrap{max-width:1180px;margin:0 auto;padding:0 32px}\n.gl-list .filters{display:flex;flex-wrap:wrap;gap:8px;justify-content:center;margin-bottom:48px;padding-bottom:32px;border-bottom:1px solid #e5e9ed}\n.gl-list .chip{background:#f8f9fb;border:1.5px solid #e5e9ed;border-radius:999px;padding:8px 16px;font-size:13px;font-weight:600;color:#567f93;cursor:pointer;font-family:'Inter',sans-serif;transition:all .2s}\n.gl-list .chip:hover{border-color:#cdd9df;color:#215972}\n.gl-list .chip.active{background:#f29100;border-color:#f29100;color:#fff}\n.gl-list .grid{display:grid;grid-template-columns:repeat(2,1fr);gap:18px}\n.gl-list .item{background:#f8f9fb;border:1px solid #e5e9ed;border-radius:14px;padding:24px 28px;transition:border-color .2s,transform .2s,box-shadow .2s}\n.gl-list .item:hover{border-color:#215972;transform:translateY(-2px);box-shadow:0 6px 20px rgba(22,62,82,.06)}\n.gl-list .item .cat-pill{display:inline-block;font-size:10.5px;font-weight:700;letter-spacing:.14em;text-transform:uppercase;color:#af6d00;background:#fef3e3;padding:4px 10px;border-radius:999px;margin-bottom:10px}\n.gl-list .item dt{font-size:18px;font-weight:700;color:#163e52;margin:0 0 8px;font-family:'Inter',sans-serif;letter-spacing:-.01em}\n.gl-list .item dd{font-size:14.5px;line-height:1.6;color:#567f93;margin:0}\n.gl-list .item.hidden{display:none}\n@media(max-width:780px){.gl-list .grid{grid-template-columns:1fr}.gl-list{padding:48px 0 72px}}\n<\/style>\n<section class=\"gl-list\"><div class=\"wrap\">\n<div class=\"filters\"><button class=\"chip active\" data-cat=\"all\">All<\/button><button class=\"chip\" data-cat=\"AI\">AI<\/button><button class=\"chip\" data-cat=\"Architecture\">Architecture<\/button><button class=\"chip\" data-cat=\"BI\">BI<\/button><button class=\"chip\" data-cat=\"Data engineering\">Data engineering<\/button><button class=\"chip\" data-cat=\"Governance\">Governance<\/button><button class=\"chip\" data-cat=\"Microsoft\">Microsoft<\/button><button class=\"chip\" data-cat=\"Operating model\">Operating model<\/button><button class=\"chip\" data-cat=\"Regulation\">Regulation<\/button><\/div>\n<dl class=\"grid\" id=\"gl-grid\"><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Agentic AI<\/dt><dd>AI systems that plan, call tools, and take actions to achieve goals \u00b7 with optional human-in-the-loop on critical decisions. The next layer beyond chatbots and copilots.<\/dd><\/div><div class=\"item\" data-cat=\"Regulation\"><div class=\"cat-pill\">Regulation<\/div><dt>AI Act<\/dt><dd>EU regulation establishing harmonised rules on artificial intelligence. In force from August 2024 with phased enforcement: prohibited practices Feb 2025, GPAI obligations Aug 2025, high-risk system obligations Aug 2026, full enforcement Aug 2027.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Anthropic<\/dt><dd>AI safety company behind the Claude family of large language models \u00b7 a foundation-model provider used for enterprise assistants, coding and agentic workloads. Claude models are available through Azure AI Foundry and AWS Bedrock.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>Apache Kafka<\/dt><dd>Distributed event-streaming platform where producers publish events and consumers read them \u00b7 moves millions of messages per second between systems and underpins real-time pipelines and event-driven architectures.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>Apache Spark<\/dt><dd>Open-source engine for large-scale data processing \u00b7 handles batch, streaming, SQL and machine learning in memory, far faster than legacy Hadoop MapReduce. The compute engine behind Microsoft Fabric and Databricks.<\/dd><\/div><div class=\"item\" data-cat=\"Regulation\"><div class=\"cat-pill\">Regulation<\/div><dt>Article 9 (AI Act)<\/dt><dd>Risk management system obligation for high-risk AI \u00b7 the spine of the entire compliance framework. Requires identifying, analysing and mitigating risks throughout the AI system lifecycle.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Azure AI Foundry<\/dt><dd>Microsoft's unified platform for building, deploying and managing AI applications \u00b7 including OpenAI models, Azure ML, fine-tuning, evaluation harnesses and Responsible AI guardrails.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Azure Synapse<\/dt><dd>Microsoft's previous-generation analytics service (SQL pools, Spark, pipelines). Microsoft Fabric is its SaaS successor; most new mid-market builds start on Fabric.<\/dd><\/div><div class=\"item\" data-cat=\"Architecture\"><div class=\"cat-pill\">Architecture<\/div><dt>Bronze \/ Silver \/ Gold (medallion)<\/dt><dd>Layered data architecture pattern in lakehouse: Bronze = raw events, Silver = cleansed and conformed, Gold = curated business-ready. Standard pattern in Microsoft Fabric and Databricks.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Capacity (Microsoft Fabric)<\/dt><dd>Compute unit in Microsoft Fabric, sized in F-units (F2, F4, F8 ... F2048). Most mid-market starts with F8 or F16 for the first three months \u00b7 F32+ is rarely needed for organisations under 500 employees.<\/dd><\/div><div class=\"item\" data-cat=\"Operating model\"><div class=\"cat-pill\">Operating model<\/div><dt>Centre of Excellence (CoE)<\/dt><dd>Governance structure for citizen-developer programs \u00b7 provides environments, DLP policies, ALM pipelines, naming standards, and a certified template gallery. Essential for safely scaling Power Platform adoption.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>Change Data Capture (CDC)<\/dt><dd>Integration technique that captures only the row-level changes (inserts, updates, deletes) in a source system and streams them downstream \u00b7 keeps lakehouses and analytics in near real time without full reloads.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>ChatGPT<\/dt><dd>OpenAI's conversational assistant built on its GPT and o-series models \u00b7 the most widely used generative-AI product and, for many organisations, the first exposure to LLMs at work.<\/dd><\/div><div class=\"item\" data-cat=\"Operating model\"><div class=\"cat-pill\">Operating model<\/div><dt>Citizen developer<\/dt><dd>Business user who builds applications, automations or reports using low-code tools (typically Power Platform) \u00b7 with IT-defined guardrails. Absorbs the 80\/20 of low-risk work that traditional engineering teams cannot ship fast enough.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Copilot Studio<\/dt><dd>Microsoft platform for building domain-specific AI agents grounded on enterprise data (SharePoint, Dataverse, Microsoft Fabric, custom APIs). Successor to Power Virtual Agents.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Data catalogue<\/dt><dd>Searchable inventory of an organisation's data assets with descriptions, ownership, classification and lineage \u00b7 helps people find, trust and correctly use data. Delivered in the Microsoft stack through Purview.<\/dd><\/div><div class=\"item\" data-cat=\"Operating model\"><div class=\"cat-pill\">Operating model<\/div><dt>Data citizenship<\/dt><dd>Culture in which every employee handles data responsibly, ethically and competently \u00b7 the behavioural counterpart to data governance, turning policy into everyday practice. Pairs with data literacy.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Data custodian<\/dt><dd>Technical role responsible for the safe transport, storage and operation of data on behalf of the data owner \u00b7 typically IT or platform engineers who implement access controls, backups and security.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Data governance<\/dt><dd>Framework of policies, roles and processes that defines how data is managed, secured and used across an organisation \u00b7 sets the rules that data owners approve and data stewards enforce.<\/dd><\/div><div class=\"item\" data-cat=\"Architecture\"><div class=\"cat-pill\">Architecture<\/div><dt>Data lake<\/dt><dd>Low-cost store for raw, structured and unstructured data in open formats \u00b7 flexible and scalable, but needs governance and structure (see lakehouse) to become reliable for analytics.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Data lineage<\/dt><dd>The traceable path of data from source system to dashboard cell. Makes impact analysis, auditing and trust possible; automated in Microsoft Purview.<\/dd><\/div><div class=\"item\" data-cat=\"Operating model\"><div class=\"cat-pill\">Operating model<\/div><dt>Data maturity<\/dt><dd>Measure of how effectively an organisation turns data into value \u00b7 spanning strategy, platform, governance, skills and culture. Sparkle's Data Maturity Scan benchmarks where you stand and what to fix first.<\/dd><\/div><div class=\"item\" data-cat=\"Architecture\"><div class=\"cat-pill\">Architecture<\/div><dt>Data mesh<\/dt><dd>Decentralised approach that treats data as a product owned by domain teams, on shared self-service infrastructure with federated governance \u00b7 an operating-model shift as much as an architecture.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Data owner<\/dt><dd>Senior business leader accountable for a data domain \u00b7 approves policy, sets access rules and owns the budget. Accountable for the data, not hands-on with it day to day.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>Data pipeline<\/dt><dd>Automated sequence that moves and transforms data from source to destination \u00b7 the plumbing behind every report, model and dashboard. Built and scheduled with tools such as Data Factory, Airflow or dbt.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Data quality<\/dt><dd>Degree to which data is accurate, complete, consistent, timely and fit for purpose \u00b7 measured, monitored and improved as a discipline, because decisions are only as good as the data behind them.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Data steward<\/dt><dd>Business practitioner responsible for day-to-day data quality, definitions and compliance within a domain \u00b7 the guardian who enforces the rules the data owner approves.<\/dd><\/div><div class=\"item\" data-cat=\"Architecture\"><div class=\"cat-pill\">Architecture<\/div><dt>Data warehouse<\/dt><dd>Central repository of structured, modelled data optimised for reporting and analytics \u00b7 the traditional home of BI, now increasingly delivered as part of a lakehouse.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>Databricks<\/dt><dd>Lakehouse platform built on Apache Spark and Delta Lake for large-scale data engineering, ML and analytics. On Azure it integrates with OneLake and Microsoft Fabric.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>dbt<\/dt><dd>Data build tool. A SQL-first framework for transforming data in the warehouse with version control, testing, documentation and lineage. Runs on Fabric Warehouse via the dbt-fabric adapter.<\/dd><\/div><div class=\"item\" data-cat=\"Architecture\"><div class=\"cat-pill\">Architecture<\/div><dt>Delta Lake<\/dt><dd>Open storage format that adds ACID transactions, schema and time travel to data-lake files \u00b7 the reliability layer that makes lakehouses possible. OneLake stores data in Delta Parquet.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Direct Lake<\/dt><dd>Power BI storage mode that queries data directly from OneLake delta tables without import or DirectQuery. Provides sub-second query performance on tables of billions of rows.<\/dd><\/div><div class=\"item\" data-cat=\"Regulation\"><div class=\"cat-pill\">Regulation<\/div><dt>DORA<\/dt><dd>EU Digital Operational Resilience Act \u00b7 regulation for financial sector entities on ICT risk management, third-party risk, incident reporting and cyber resilience. In force from January 2025.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Embeddings &amp; vector database<\/dt><dd>Numeric representations of text or images that capture meaning, stored in a vector database for similarity search. The retrieval layer behind RAG and semantic search.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>ETL \/ ELT<\/dt><dd>Two patterns for moving data \u00b7 ETL transforms before loading, ELT loads raw then transforms in the target platform. Modern cloud platforms favour ELT because storage is cheap and compute is elastic.<\/dd><\/div><div class=\"item\" data-cat=\"Regulation\"><div class=\"cat-pill\">Regulation<\/div><dt>EU AI Act risk tiers<\/dt><dd>Four-level classification: unacceptable risk (prohibited), high risk (Articles 9-17 obligations), limited risk (transparency obligations Art. 50), minimal risk (no specific obligations). Most enterprise use cases fall in limited or high.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>EventStream (Microsoft Fabric)<\/dt><dd>Real-time data ingestion service in Microsoft Fabric \u00b7 pipes events from Event Hubs, Kafka, IoT Hub or custom sources into KQL databases or lakehouses for sub-minute analytics.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Fabric (Microsoft Fabric)<\/dt><dd>Microsoft's unified SaaS analytics platform combining data engineering, warehousing, real-time intelligence, data science, BI and AI \u00b7 all built on OneLake with separated storage and compute. SaaS evolution of Synapse.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Feature store<\/dt><dd>Centralised repository for reusable ML features \u00b7 ensures consistency between training and serving, reduces feature-engineering duplication across use cases, supports lineage and access control.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Fine-tuning<\/dt><dd>Further training of a foundation model on your own examples to specialise its behaviour \u00b7 an alternative or complement to prompting and RAG when you need consistent, domain-specific output.<\/dd><\/div><div class=\"item\" data-cat=\"Regulation\"><div class=\"cat-pill\">Regulation<\/div><dt>GDPR-Health<\/dt><dd>GDPR Article 9 special category data protections applied to health information \u00b7 processing prohibited by default, with limited exceptions (consent, public interest, healthcare provision). Stricter than general personal data.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Generative AI (GenAI)<\/dt><dd>AI that creates new content \u00b7 text, code, images, audio or video \u00b7 from patterns learned in training data. The category that includes LLMs and the tools built on them.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Google Gemini<\/dt><dd>Google's family of multimodal foundation models and the assistant built on them \u00b7 a leading alternative to OpenAI, integrated across Google Workspace and Google Cloud.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>GPAI (General-Purpose AI)<\/dt><dd>AI models with broad capabilities (GPT-4, Claude, Gemini, Llama, Mistral). Subject to specific AI Act obligations under Articles 51-55, scaled by computational resources used in training.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Hallucination<\/dt><dd>When a generative model produces confident but false or fabricated output \u00b7 the core reason enterprise AI needs grounding (RAG), guardrails and human review.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>KQL (Kusto Query Language)<\/dt><dd>Query language for time-series and log analytics in Azure Data Explorer and Microsoft Fabric KQL Database \u00b7 optimised for sub-second queries on billions of events.<\/dd><\/div><div class=\"item\" data-cat=\"Architecture\"><div class=\"cat-pill\">Architecture<\/div><dt>Lakehouse<\/dt><dd>Architecture combining data lake (low-cost object storage, open formats) with warehouse (ACID transactions, schema, performance). Microsoft Fabric, Databricks Delta Lake, Snowflake Iceberg are lakehouse platforms.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>LLM (Large Language Model)<\/dt><dd>Large Language Model. A model trained on vast amounts of text to generate and reason over language (GPT, Claude, Gemini, Llama). The engine behind chat, drafting, summarisation and agentic AI.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Machine learning<\/dt><dd>Field of AI where systems learn patterns from data to make predictions or decisions, rather than following hand-written rules \u00b7 underpins forecasting, classification, recommendation and anomaly detection.<\/dd><\/div><div class=\"item\" data-cat=\"Governance\"><div class=\"cat-pill\">Governance<\/div><dt>Master data management (MDM)<\/dt><dd>Discipline of creating one trusted, consistent version of core business entities \u00b7 customers, products, suppliers \u00b7 shared across systems. Ends the \"which customer list is right?\" problem.<\/dd><\/div><div class=\"item\" data-cat=\"Regulation\"><div class=\"cat-pill\">Regulation<\/div><dt>MDR (Medical Device Regulation)<\/dt><dd>EU regulation governing medical devices including software \u00b7 diagnostic AI often dual-regulated under MDR + AI Act high-risk obligations. Requires CE marking, post-market surveillance, technical documentation.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Microsoft Copilot<\/dt><dd>Microsoft's AI assistant embedded across Microsoft 365, Windows and the Power Platform \u00b7 grounded on your organisation's data through the Microsoft Graph, with enterprise security and compliance.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Mirroring (Microsoft Fabric)<\/dt><dd>Near real-time replication of an operational database (Azure SQL, Cosmos DB, Snowflake and more) into OneLake at no compute cost, often removing an entire ETL project.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>MLOps<\/dt><dd>Practices and tooling to deploy, monitor and retrain machine-learning models reliably: versioning, CI\/CD, drift detection and governance across the model lifecycle.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Model Context Protocol (MCP)<\/dt><dd>Open standard for connecting AI assistants to tools, data and systems through a common interface \u00b7 the emerging \"USB-C for AI\", making agents easier to integrate with enterprise software.<\/dd><\/div><div class=\"item\" data-cat=\"Regulation\"><div class=\"cat-pill\">Regulation<\/div><dt>NIS2<\/dt><dd>EU directive on cybersecurity for essential and important entities \u00b7 expanded scope (energy, transport, banking, health, public administration, digital infrastructure, manufacturing). National transposition deadline October 2024.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>OneLake<\/dt><dd>Single logical lake underlying all Microsoft Fabric workloads \u00b7 open Delta Parquet format, automatic governance, shortcuts to other storage (Synapse, S3, ADLS, Snowflake) without copying data.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>OpenAI<\/dt><dd>AI research company behind the GPT and o-series models and ChatGPT \u00b7 closely tied to Microsoft, with its models available through Azure OpenAI in Azure AI Foundry.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>Orchestration<\/dt><dd>Coordinating the order, scheduling, dependencies and monitoring of data pipeline steps \u00b7 handled by tools such as Azure Data Factory, Apache Airflow or Dagster.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Perplexity<\/dt><dd>AI-powered answer engine that responds to questions with synthesised, cited answers drawn from live web sources \u00b7 a conversational alternative to traditional search.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Power BI<\/dt><dd>Microsoft's analytics and data-visualisation platform \u00b7 builds interactive reports and dashboards on governed semantic models, with Direct Lake connectivity to Fabric. The most widely adopted enterprise BI tool.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Power Platform<\/dt><dd>Microsoft low-code suite: Power BI (analytics), Power Apps (custom apps), Power Automate (workflow), Copilot Studio (AI agents), AI Builder (ML on tap), Pages (websites). Foundation for citizen-developer programs.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Prompt engineering<\/dt><dd>Practice of designing the instructions and context given to a generative model to get reliable, useful output \u00b7 the cheapest lever for quality before fine-tuning or extra tooling.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Purview (Microsoft Purview)<\/dt><dd>Microsoft's unified data governance platform \u00b7 classification, sensitivity labels, lineage, data discovery, AI risk register. Replaces Information Protection + Compliance Manager + Data Map under one brand.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>PySpark<\/dt><dd>Python interface to Apache Spark \u00b7 lets data engineers write large-scale, distributed data processing in Python. The default way to work with Spark in Fabric and Databricks notebooks.<\/dd><\/div><div class=\"item\" data-cat=\"Data engineering\"><div class=\"cat-pill\">Data engineering<\/div><dt>Python<\/dt><dd>The dominant programming language for data engineering, analytics and AI \u00b7 readable, versatile and backed by a vast ecosystem of data libraries. The common language across the modern data stack.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>RAG (Retrieval-Augmented Generation)<\/dt><dd>Retrieval-Augmented Generation. Grounding an LLM's answers in your own documents by retrieving relevant passages at query time, so responses are accurate, current and citable instead of invented.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Real-Time Intelligence (Fabric)<\/dt><dd>Microsoft Fabric workload for streaming analytics \u00b7 combines EventStream, KQL Database, Activator (alerting). Sub-minute freshness with native integration into Power BI and AI workloads.<\/dd><\/div><div class=\"item\" data-cat=\"AI\"><div class=\"cat-pill\">AI<\/div><dt>Responsible AI<\/dt><dd>Practice of designing, building and deploying AI with fairness, reliability, safety, privacy, security, inclusiveness, transparency and accountability built in. Operationalised through evaluation harnesses, content filters, human-in-the-loop, model cards.<\/dd><\/div><div class=\"item\" data-cat=\"BI\"><div class=\"cat-pill\">BI<\/div><dt>Row-Level Security (RLS)<\/dt><dd>Filtering rows in a semantic model or database by who is querying, so each user sees only the data they are permitted to. Core to governed self-service BI.<\/dd><\/div><div class=\"item\" data-cat=\"BI\"><div class=\"cat-pill\">BI<\/div><dt>Self-service BI<\/dt><dd>Model in which business users build their own reports and analyses on governed, certified data \u00b7 fast for users, safe for IT, because the semantic model holds the shared definitions.<\/dd><\/div><div class=\"item\" data-cat=\"BI\"><div class=\"cat-pill\">BI<\/div><dt>Semantic model<\/dt><dd>Logical layer over your data with certified definitions, calculated measures, row-level security, calculation groups, hierarchies. The spine of governed self-service BI \u00b7 the reports become commodity, the model is the asset.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Shortcut (Microsoft Fabric)<\/dt><dd>Pointer in OneLake to data physically stored elsewhere (Synapse, ADLS, S3, Snowflake) \u00b7 enables querying without copying. Critical for incremental migration and avoiding data duplication.<\/dd><\/div><div class=\"item\" data-cat=\"Microsoft\"><div class=\"cat-pill\">Microsoft<\/div><dt>Solution Partner (Microsoft)<\/dt><dd>Microsoft Partner Network designation requiring scoring on partner capability score (skills, deployments, customer success, performance). Sparkle holds Solution Partner Data &amp; AI \u00b7 Azure plus Solutions Partner Power Platform \u00b7 Modern Work.<\/dd><\/div><div class=\"item\" data-cat=\"BI\"><div class=\"cat-pill\">BI<\/div><dt>Tier-1 \/ Tier-2 \/ Tier-3 KPI<\/dt><dd>KPI hierarchy framework: Tier-1 (GOLD) moves strategy \u00b7 5-7 metrics for board and exec; Tier-2 (SILVER) moves operations \u00b7 10-15 metrics; Tier-3 (BRONZE) supports specialists \u00b7 30+. Prevents the \"55 KPIs all at executive level\" trap.<\/dd><\/div><\/dl>\n<\/div><\/section>\n<script id=\"gl-filter\">\n(function(){\n  var chips = document.querySelectorAll('.gl-list .chip');\n  var items = document.querySelectorAll('.gl-list .item');\n  chips.forEach(function(c){\n    c.addEventListener('click', function(){\n      chips.forEach(function(x){ x.classList.remove('active'); });\n      c.classList.add('active');\n      var cat = c.dataset.cat;\n      items.forEach(function(it){\n        it.classList.toggle('hidden', cat !== 'all' && it.dataset.cat !== cat);\n      });\n    });\n  });\n})();\n<\/script>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-sc0dqoo elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"sc0dqoo\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c6ynwoi\" data-id=\"c6ynwoi\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-wtzrv4n elementor-widget elementor-widget-html\" data-id=\"wtzrv4n\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n.gl-cta{background:#f8f9fb;padding:72px 0;text-align:center;font-family:'Inter',sans-serif}\n.gl-cta .wrap{max-width:780px;margin:0 auto;padding:0 32px}\n.gl-cta h2{color:#163e52!important;font-size:28px;line-height:1.2;margin:0 0 14px;font-weight:700;letter-spacing:-.02em}\n.gl-cta h2 em{font-style:italic;color:#f29100;font-weight:600}\n.gl-cta p{font-size:16px;color:#567f93;margin:0 0 24px;line-height:1.6}\n.gl-cta .btn{padding:13px 26px;border-radius:9px;background:#f29100;color:#fff;font-size:14px;font-weight:600;text-decoration:none;display:inline-block;box-shadow:0 4px 14px rgba(242,145,0,.25)}\n.gl-cta .btn:hover{background:#af6d00}\n<\/style>\n<section class=\"gl-cta\"><div class=\"wrap\">\n<h2>Term <em>missing<\/em>?<\/h2>\n<p>We add terms as we build with customers. Suggest one and we'll consider it for the next quarterly update.<\/p>\n<a class=\"btn\" href=\"mailto:hello@sparkle.consulting?subject=Glossary%20suggestion\">Suggest a term<\/a>\n<\/div><\/section>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Home\/Resources\/Glossary Reference The data, AI and Microsoft terms that matter. A working glossary of the technologies, regulations and patterns we ship every day \u00b7 written for executives, engineers and everyone &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/sparkle.consulting\/fr\/glossary\/\"> <span class=\"screen-reader-text\">Glossaire \u00b7 Termes Data, IA &amp; Microsoft<\/span> Lire la suite \u00bb<\/a><\/p>","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"enabled","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"class_list":["post-13114","page","type-page","status-publish","hentry"],"acf":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"trp-custom-language-flag":false},"uagb_author_info":{"display_name":"Kristof Van de Loock","author_link":"https:\/\/sparkle.consulting\/fr\/author\/kristof-vandeloocksparkle-consulting\/"},"uagb_comment_info":0,"uagb_excerpt":"Home\/Resources\/Glossary Reference The data, AI and Microsoft terms that matter. A working glossary of the technologies, regulations and patterns we ship every day \u00b7 written for executives, engineers and everyone &hellip; Glossary \u00b7 Data, AI &#038; Microsoft Terms Read More \u00bb","_links":{"self":[{"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/pages\/13114","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/comments?post=13114"}],"version-history":[{"count":9,"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/pages\/13114\/revisions"}],"predecessor-version":[{"id":14081,"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/pages\/13114\/revisions\/14081"}],"wp:attachment":[{"href":"https:\/\/sparkle.consulting\/fr\/wp-json\/wp\/v2\/media?parent=13114"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}