[{"data":1,"prerenderedAt":355},["ShallowReactive",2],{"navigation":3,"page-\u002Fen\u002Fcreating-datasets\u002Fthe-data-landscape":130,"\u002Fen\u002Fcreating-datasets\u002Fthe-data-landscape-surround":349,"comments-\u002Fen\u002Fcreating-datasets\u002Fthe-data-landscape":354},[4],{"title":5,"path":6,"stem":7,"children":8,"page":129},"En","\u002Fen","en",[9,53,87],{"title":10,"path":11,"stem":12,"children":13,"icon":52},"CRM Analytics Foundations","\u002Fen\u002Ffoundations","en\u002F1.foundations\u002F1.index",[14,16,20,24,28,32,36,40,44,48],{"title":15,"path":11,"stem":12},"Welcome & Dev Org",{"title":17,"path":18,"stem":19},"Graded Quiz","\u002Fen\u002Ffoundations\u002Fquiz","en\u002F1.foundations\u002F10.quiz",{"title":21,"path":22,"stem":23},"What Is CRM 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Steps","\u002Fen\u002Ffoundations\u002Fsix-steps-and-interview-prep","en\u002F1.foundations\u002F8.six-steps-and-interview-prep",{"title":49,"path":50,"stem":51},"Interview Questions","\u002Fen\u002Ffoundations\u002Finterview-questions","en\u002F1.foundations\u002F9.interview-questions","i-lucide-compass",{"title":54,"path":55,"stem":56,"children":57,"icon":86},"Setup & User Provisioning","\u002Fen\u002Fsetup","en\u002F2.setup\u002F1.index",[58,60,64,68,72,76,80,83],{"title":59,"path":55,"stem":56},"Provisioning Users",{"title":61,"path":62,"stem":63},"Licenses & Permissions","\u002Fen\u002Fsetup\u002Flicenses-and-permission-sets","en\u002F2.setup\u002F2.licenses-and-permission-sets",{"title":65,"path":66,"stem":67},"Integration User","\u002Fen\u002Fsetup\u002Fthe-integration-user","en\u002F2.setup\u002F3.the-integration-user",{"title":69,"path":70,"stem":71},"Security 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Dataset?",{"title":17,"path":95,"stem":96},"\u002Fen\u002Fcreating-datasets\u002Fquiz","en\u002F3.creating-datasets\u002F10.quiz",{"title":98,"path":99,"stem":100},"The Data Landscape","\u002Fen\u002Fcreating-datasets\u002Fthe-data-landscape","en\u002F3.creating-datasets\u002F2.the-data-landscape",{"title":102,"path":103,"stem":104},"Upload a CSV","\u002Fen\u002Fcreating-datasets\u002Fload-data-from-csv","en\u002F3.creating-datasets\u002F3.load-data-from-csv",{"title":106,"path":107,"stem":108},"Grain, Lookups & Joins","\u002Fen\u002Fcreating-datasets\u002Fgrain-lookups-and-joins","en\u002F3.creating-datasets\u002F4.grain-lookups-and-joins",{"title":110,"path":111,"stem":112},"Dataset Builder","\u002Fen\u002Fcreating-datasets\u002Fbuild-with-dataset-builder","en\u002F3.creating-datasets\u002F5.build-with-dataset-builder",{"title":114,"path":115,"stem":116},"Run the Dataflow","\u002Fen\u002Fcreating-datasets\u002Frun-and-troubleshoot-the-dataflow","en\u002F3.creating-datasets\u002F6.run-and-troubleshoot-the-dataflow",{"title":118,"path":119,"stem":120},"Build with a Recipe","\u002Fen\u002Fcreating-datasets\u002Fbuild-the-same-dataset-with-a-recipe","en\u002F3.creating-datasets\u002F7.build-the-same-dataset-with-a-recipe",{"title":122,"path":123,"stem":124},"Combine Datasets","\u002Fen\u002Fcreating-datasets\u002Fcombine-datasets","en\u002F3.creating-datasets\u002F8.combine-datasets",{"title":49,"path":126,"stem":127},"\u002Fen\u002Fcreating-datasets\u002Finterview-questions","en\u002F3.creating-datasets\u002F9.interview-questions","i-lucide-database",false,{"id":131,"title":132,"access":133,"body":134,"description":339,"extension":340,"interview":133,"links":133,"meta":341,"navigation":342,"passScore":133,"path":99,"quiz":133,"seo":343,"stem":100,"video":344,"__hash__":348},"docs\u002Fen\u002F3.creating-datasets\u002F2.the-data-landscape.md","The Data Landscape: Connect, Sync, and Where Datasets Fit",null,{"type":135,"value":136,"toc":328},"minimark",[137,141,145,150,166,178,183,194,198,201,239,247,266,270,281,288,292,299,302,313],[138,139,132],"h1",{"id":140},"the-data-landscape-connect-sync-and-where-datasets-fit",[142,143,144],"p",{},"Before you build anything, you need a mental map of how data moves through CRM Analytics. Get this picture right and every tool in the platform suddenly has an obvious place. Get it wrong and you'll spend hours wondering why a dataset is stale or a sync failed.",[146,147,149],"h2",{"id":148},"the-staging-layer-connect-and-sync","The staging layer: Connect and Sync",[142,151,152,153,157,158,161,162,165],{},"Most of your data starts in your ",[154,155,156],"strong",{},"local Salesforce org",", and the platform has a native connection to it. You bring objects in through ",[154,159,160],{},"Connect"," (also called ",[154,163,164],{},"Sync",") exactly as they are — Account, Opportunity, User — each landing as its own synced object.",[142,167,168,169,172,173,177],{},"Think of this as a ",[154,170,171],{},"free staging area",". You're not building finished datasets here; you're pulling raw objects into a holding zone. Later, a dataflow or recipe ",[174,175,176],"em",{},"reads from"," this staging layer to assemble the datasets you'll actually use in dashboards, lenses, and stories.",[179,180],"lesson-cards",{":columns":181,":items":182},"2","[{\"title\":\"Connect \u002F Sync layer\",\"icon\":\"i-lucide-import\",\"description\":\"Raw Salesforce (and external) objects, staged as-is. Supports scheduled and incremental refresh. Nothing here is dashboard-ready yet.\"},{\"title\":\"Dataset layer\",\"icon\":\"i-lucide-database\",\"description\":\"The finished, combined, query-optimized datasets your dashboards read from — built by dataflows or recipes on top of the staging layer.\"}]",[184,185,187],"tip",{"icon":186},"i-lucide-zap",[142,188,189,190,193],{},"The sync layer's superpower is ",[154,191,192],{},"incremental refresh"," — after the first full load, it only pulls changed rows. That's what lets you schedule a sync every 15 minutes, every hour, or every day without hammering your org. We go deep on this in the Data Manager material.",[146,195,197],{"id":196},"where-data-can-come-from","Where data can come from",[142,199,200],{},"Salesforce is the common source, but it's far from the only one. The Connect\u002FSync layer can ingest from:",[202,203,204,211,217,227,233],"ul",{},[205,206,207,210],"li",{},[154,208,209],{},"Your local Salesforce org"," — native, the most common path.",[205,212,213,216],{},[154,214,215],{},"Other Salesforce clouds or connected orgs"," — pull data across environments.",[205,218,219,222,223,226],{},[154,220,221],{},"External sources like Snowflake"," — including a ",[154,224,225],{},"live connector"," that reads at query time.",[205,228,229,232],{},[154,230,231],{},"CSV files"," — a simple manual upload, which we'll do in the next lesson.",[205,234,235,238],{},[154,236,237],{},"Your own ETL tool or API"," — any ETL that has a connector to CRM Analytics can push data in, or you can call the ingestion API directly (documented under the Help link).",[146,240,242,243,246],{"id":241},"data-can-flow-out-too","Data can flow ",[174,244,245],{},"out",", too",[142,248,249,250,253,254,257,258,261,262,265],{},"In recent releases the platform has been extending its ",[154,251,252],{},"output connectors",". Once you've built a dataset — brought it from Salesforce, combined it, enriched it — you can push it back out to ",[154,255,256],{},"Snowflake",", ",[154,259,260],{},"Amazon S3",", or a ",[154,263,264],{},"Tableau"," hyper file to build dashboards there. Analytics isn't a one-way street anymore.",[146,267,269],{"id":268},"one-feature-to-know-about-trend-report-data","One feature to know about: Trend Report Data",[142,271,272,273,276,277,280],{},"If you have the CRM Analytics Plus license plus the ",[154,274,275],{},"Trend Report Data in Analytics"," permission, every operational report gets a small ",[154,278,279],{},"\"Trend in Analytics\""," button. Click it and you can schedule that report — daily, weekly, or monthly — to store its results directly in a dataset.",[142,282,283,284,287],{},"It's handy if you're a heavy Salesforce report user who just wants historical snapshots of counts, activities, or cases. Be aware, though, that it's ",[154,285,286],{},"limited",": it goes straight to a dataset with little control over the transformation. In practice you'll more often snapshot data through an app or a recipe.",[146,289,291],{"id":290},"the-engines-dataflows-and-recipes","The engines: dataflows and recipes",[142,293,294,295,298],{},"Between the staging layer and your finished datasets sit the two ",[154,296,297],{},"engines that execute your instructions",":",[179,300],{":columns":181,":items":301},"[{\"title\":\"Dataflow\",\"icon\":\"i-lucide-workflow\",\"description\":\"The original engine. Dataset Builder writes its instructions into a dataflow, which you schedule to build and refresh datasets.\"},{\"title\":\"Recipe\",\"icon\":\"i-lucide-blend\",\"description\":\"The modern, visual data-prep engine. Drag-and-drop joins, transforms, and outputs — more flexible, and where Salesforce is investing.\"}]",[142,303,304,305,308,309,312],{},"The key insight that trips people up: these run in an ",[154,306,307],{},"order",". Sync refreshes the base objects first, ",[174,310,311],{},"then"," a dataflow or recipe runs to update the finished datasets. If your dashboard shows stale numbers, the culprit is almost always something out of order in that chain — a sync that hasn't run, or a recipe scheduled before its source finished.",[142,314,315,316,319,320,323,324,327],{},"Throughout this section you'll hear me distinguish ",[154,317,318],{},"baseline datasets"," (the raw building blocks) from ",[154,321,322],{},"final resulting datasets"," (what dashboards consume). Hold onto that distinction — it's the difference between a maintainable analytics app and a tangle nobody can debug. Next, the simplest way to make a dataset: a ",[154,325,326],{},"CSV upload",".",{"title":329,"searchDepth":330,"depth":331,"links":332},"",1,2,[333,334,335,337,338],{"id":148,"depth":331,"text":149},{"id":196,"depth":331,"text":197},{"id":241,"depth":331,"text":336},"Data can flow out, too",{"id":268,"depth":331,"text":269},{"id":290,"depth":331,"text":291},"Before you build a dataset, understand the CRM Analytics data landscape: the Connect\u002FSync staging layer, how data comes in from Salesforce, Snowflake, CSVs, and ETL tools, and where dataflows and recipes fit.","md",{},{"title":98},{"title":132,"description":339},{"id":345,"start":346,"end":347},"-0mp2mUjyVI",114,369,"kFwXnZAuy9rhbmunrRjNp1cYTEqrsGLpv54objZbIH8",[350,352],{"title":17,"path":95,"stem":96,"description":351,"children":-1},"Take the graded CRM Analytics exam on creating datasets — datasets and grain, CSV uploads, Dataset Builder, dataflows, recipes, and combining datasets. Score 85% to pass; questions rotate.",{"title":102,"path":103,"stem":104,"description":353,"children":-1},"Create a CRM Analytics dataset by uploading a CSV file. Walk through the upload flow, review dimensions, measures, and dates, control field types with the metadata schema, and automate uploads with Dataset Utils.",[],1783475158255]