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Vibedata

Collections

A Collection is a way of finding Recipes, not a thing you run. It holds no Recipe of its own: the title, the acceptance conditions and the readiness you see inside one are the Recipe’s own, which is why the same Recipe can sit in several Collections without any of them disagreeing about what it is.

By domain

The domain whose data you are working on today. You are still the data engineer in every one of these.

Larger outcomes

Recognisable pieces of work that take more than one Recipe, assembled rather than merged.

  • Customer and product analytics

    Recipes for building customer value, retention, segmentation and product behaviour models that business teams can reproduce.

    7 Recipes

  • Legacy and platform re-engineering

    Recipes for moving existing SQL, notebooks, connectors and estates onto dbt, dlt and Fabric, from assessing what a move would take to converting each piece.

    12 Recipes

  • Logistics and fleet analytics

    Logistics and fleet analytics Recipes, including reusable modelling patterns with relevant operational triggers and domain-specific fleet and shift outcomes.

    9 Recipes

  • Marketing data engineering

    Recipes for attribution, acquisition economics, campaign spend pacing and contact-pressure eligibility under approved business rules.

    4 Recipes

  • Procurement data engineering

    Recipes for procurement spend and contract compliance, purchase commitments, invoice matching and payment checks, supplier rebates, realized savings and SaaS renewal evidence.

    9 Recipes

By problem area

The kind of problem the Recipes solve.

  • Ingest and reconcile source data

    Recipes for landing source data in owned pipelines and governed tables, with explicit record disposition, safe reruns and reconciliation.

    11 Recipes

  • Orchestration and reliability

    Recipes for scheduling, gating and monitoring pipelines so runs start when their inputs are ready and failures are visible.

    5 Recipes

  • Semantic layers

    Recipes for defining governed metrics and semantic models that downstream tools query.

    2 Recipes

By industry

Vertical Recipes together with the horizontal ones that vertical reaches for most.

  • CPG data engineering

    Recipes for consumer packaged goods data engineering, including trade-promotion lift, gross-to-net revenue and distributor sell-in, sell-out and channel stock.

    4 Recipes

  • Healthcare and life sciences

    Recipes for data engineers working with healthcare and life-sciences data, such as claims, admissions and clinical-trial enrollment.

    4 Recipes

  • Manufacturing data engineering

    Recipes for data engineers working with manufacturing data, such as production runs, bills of materials, material lots and equipment downtime.

    6 Recipes

  • Retail data engineering

    Recipes for retail data engineering, including stock availability and ownership, shelf-life allocation, sales margins, and marketplace and till settlement.

    7 Recipes

By platform

Where the work can actually run. Each Recipe names its exact execution targets.

  • DuckDB

    Recipes that can execute against local DuckDB.

    112 Recipes

  • Microsoft Fabric

    Recipes that can execute against Microsoft Fabric Lakehouse, Fabric Warehouse, or both.

    97 Recipes

  • MotherDuck

    Recipes that can execute against MotherDuck.

    95 Recipes