Dimensional modeling principles

Dimensional modeling principles

Dimensional modeling is the name of a logical design technique often used for data warehouses. DM is the only viable technique for databases that are designed to support end-user queries in a data warehouse. It is different from, and contrasts with, entity-relation modeling. ER is very useful for the transaction capture and the data administration phases of constructing a data warehouse, but it Aug 13,  · The model should allow drilling down or rolling up along dimension hierarchies. With these requirements, we find that a dimensional model with the fact table in the middle and the dimension tables arranged around the fact table satisfies the blogger.comted Reading Time: 3 mins Sep 17,  · Once you reach that point and you need to bring order to your Montana ranch, dimensional modeling will help break the work ahead of you into manageable chunks, with the star schema as its well-defined unit of delivery. Principles to follow: Define your metrics (and commit them to

How I set up my conformed dimensional models

You just started your dream job — you are the first data scientist at a meal kit startup. Quicker than you can say triple exponential smoothing, network optimization, and multi-arm bandit you find yourself thinking about dimensional modeling principles to clean and model the data so you can build those dashboards. Where should you begin? These are highly personal preferences—I like the old-school name "data mart" because it brings up images of happy slushie customers at a Kwik-e-Mart, dimensional modeling principles.

But Kimball is not the only dimensional modeling principles approach—the dbt community has compiled a good list of the other notable data modeling schools out there.

What matters is that you have a repeatable approach for how you build your data models. Here's mine I start with mapping out the customer lifecycle on a swimlane diagram because, in the words of Lean Analytics. What is a swimlane? It has vertical lanes representing each discipline and customers move from one end to another horizontally across those vertical lanes.

In the case dimensional modeling principles our imaginary meal kit startup, we have a marketing website where users can subscribe. Once users become subscribers, finance wants to calculate monthly recurring revenue MRR and other SaaS metrics.

Each week, as orders ship out, the operations team monitors things like the ratio of on-time shipments to all shipments and average transit times, dimensional modeling principles.

Why a swimlane? Clarifying each discrete step in the process helps me familiarize myself with our product offerings and other relevant elements of the business plan canvas. It also helps me find the various stakeholders — my internal customers — and involve them in the process of dimensional modeling, dimensional modeling principles. The diagram can help break down an otherwise overwhelming process into discrete steps and serve as my roadmap.

And finally, it helps me work towards a conformed dimensional model more on this below. Once I have a swimlane draft with the customer moving left to right and crossing each vertical department, dimensional modeling principles, I add some horizontal lanes, dimensional modeling principles.

For each discipline, I go through the following steps:. Now that I know what metrics are used by what departments and I have a list of fact tables, dimensional modeling principles, data sources, dimensional modeling principles, and acceptance dimensional modeling principles, I can identify the dimensions — what else do we care about? How do we want to slice and dice the data?

I like to work with conformed dimensions, meaning dimension tables that are reused across all fact tables and departments. It helps answer questions that cross disciplines, dimensional modeling principles. Dimensional modeling principles dimensions help align disciplines and will prevent the kind of metric knife fights described and illustrated!

so aptly in Chapter 4 of The Analytics Setup Guidebook, dimensional modeling principles. But perhaps most importantly, it helps you write and maintain fewer tables. As many people pointed out, storing data is cheaper now than humans spending hours thinking about data models. I still like to think of this problem as having a New York apartment vs. a Montana ranch. Once you reach that point and you need to bring order to your Montana ranch, dimensional modeling will help break the work ahead of you into manageable chunks, with the star schema as dimensional modeling principles well-defined unit of delivery.

Finance wants to know monthly recurring revenue before they need your forecasts. Operations wants to report on average transit time. Marketing wants to establish a baseline for conversion rate. metrics follow a dimensional modeling principles, logical order. Hopefully, that user continues to use the service, and eventually pays for a subscription.

The user invites others and, and perhaps upgrades to a higher tier. As a customer, she may have issues. In the end, dimensional modeling principles, she stops using the service — at which point, we know how much revenue she contributed to the business.

Describing the customer lifecycle in this way is a good method for understanding the key metrics that drive your business. For each discipline, dimensional modeling principles, I go through the following steps: List the key metrics — what KPIs are in use? You can flesh them out in a neat spreadsheet following this Data School tutorial or you can use the BEAM example data table to capture similar information. Identify the base units — what are we measuring with these metrics?

What is an observable measurable interaction event in this lane of the customer lifecycle? Find the data sources — where do these interactions between your customers and the company happen? Where does the data come from? This step may ferret out many gotchas involved in the data cleaning process. Add any dimensional modeling principles tables you may need beyond the base units — how do we aggregate these subscriptions? If our KPI is monthly recurring revenue MRRdo we want to compare this number on a day-to-day or an hourly basis?

Fact tables should be aggregated at their most granular form to give us flexibility creating derived tables downstream. Ask your stakeholders for acceptance criteria — when I build this table, how will I know that it is correct?

What is the North Star you navigate by? Do you currently look at recurring revenue reports in the datasource e. Stripea third-party tool e. ChartMogulor in an internal spreadsheet? What is the current solution this dashboard is going to replace? If operations could see on-time shipment rates for different MRR tiers?

Principles to follow: Define your metrics and commit them to writing. Work with your stakeholders to find the base units of their metrics and primary data sources. Group your fact tables by functional areas, name them, and use the same words in your transformation layer and Looker models.

Use conformed dimensions to align metrics and definitions. Define the acceptance criteria. Get started with dbt today.

What is Dimensional Modeling

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Dimensional modeling - Wikipedia

Dimensional Modeling Basics • Dimensional modeling gets its name from the business dimensions we need to incorporate into the logical data model. It is a logical design technique to structure the business dimensions and the metrics that are analyzed along these dimensions. This modeling technique is intuitive for that purpose Sep 17,  · Once you reach that point and you need to bring order to your Montana ranch, dimensional modeling will help break the work ahead of you into manageable chunks, with the star schema as its well-defined unit of delivery. Principles to follow: Define your metrics (and commit them to Aug 13,  · The model should allow drilling down or rolling up along dimension hierarchies. With these requirements, we find that a dimensional model with the fact table in the middle and the dimension tables arranged around the fact table satisfies the blogger.comted Reading Time: 3 mins