Star Power

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Star Power

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Star Power Star schema overview Video

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Under their order, Mario is to go around and collect all the power stars and to destroy all who stands in his way. This is my best work yet and I am sure that you will love it.

Please vote fairly. If you have an old computer, then set the quality to low. This is one of my favorite flash animations of all time. It's just so rewatchable and so memorable and it was the first time I've seen a Mario video on the internet that takes the Mario franchise and makes it something completely different from what they officially are.

Also the music used throughout this series is badass I love Tekken 5 so that's probably another reason why I love this so much.

I have always been a big fan of sprite movies, and this one is no exception. I used to watch this a lot when I was younger! Seeing Mario go on a rampage while being controlled by Boos always filled me with dread!

I remember watching these in like and was still late to the party. I was simply too young to discover and enjoy NG that far back. Information about the new graphic novel and details for commissioning him can be found there.

No matter what you choose to do now that Star Power has ended, Garth and Michael will always be grateful for your love and support. By becoming a patron, you'll instantly unlock access to exclusive posts.

Recent posts by Star Power. How it works. Add a payment method. The "one" side is always a dimension-type table while the "many" side is always a fact-type table.

A well-structured model design should include tables that are either dimension-type tables or fact-type tables. Avoid mixing the two types together for a single table.

We also recommend that you should strive to deliver the right number of tables with the right relationships in place. It's also important that fact-type tables always load data at a consistent grain.

Lastly, it's important to understand that optimal model design is part science and part art. Sometimes you can break with good guidance when it makes sense to do so.

There are many additional concepts related to star schema design that can be applied to a Power BI model.

These concepts include:. In star schema design, a measure is a fact table column that stores values to be summarized.

In a Power BI model, a measure has a different—but similar—definition. It's important to understand that Power BI models support a second method for achieving summarization.

These columns are referred to as implicit measures. They offer a convenience for you as a model developer, as in many instances you do not need to create measures.

For example, the Adventure Works reseller sales Sales Amount column could be summarized in numerous ways sum, count, average, median, min, max, etc.

However, there are three compelling reasons for you to create measures, even for simple column-level summarizations:. However, Power BI Desktop live connections allow report authors to show hidden fields in the Fields pane, which can result in circumventing this design approach.

A surrogate key is a unique identifier that you add to a table to support star schema modeling. By definition, it's not defined or stored in the source data.

Commonly, surrogate keys are added to relational data warehouse dimension tables to provide a unique identifier for each dimension table row.

Power BI model relationships are based on a single unique column in one table, which propagates filters to a single column in a different table. When a dimension-type table in your model doesn't include a single unique column, you must add a unique identifier to become the "one" side of a relationship.

You must merge this query with the "many"-side query so that you can add the index column to it also.

When you load these queries to the model, you can then create a one-to-many relationship between the model tables.

A snowflake dimension is a set of normalized tables for a single business entity. For example, Adventure Works classifies products by category and subcategory.

Categories are assigned to subcategories, and products are in turn assigned to subcategories. If you use your imagination, you can picture the normalized tables positioned outwards from the fact table, forming a snowflake design.

In Power BI Desktop, you can choose to mimic a snowflake dimension design perhaps because your source data does or integrate denormalize the source tables into a single model table.

Generally, the benefits of a single model table outweigh the benefits of multiple model tables. The most optimal decision can depend on the volumes of data and the usability requirements for the model.

When you choose to integrate into a single model table, you can also define a hierarchy that encompasses the highest and lowest grain of the dimension.

Possibly, the storage of redundant denormalized data can result in increased model storage size, particularly for very large dimension tables.

A slowly changing dimension SCD is one that appropriately manages change of dimension members over time. It applies when business entity values change over time, and in an ad hoc manner.

A good example of a slowly changing dimension is a customer dimension, specifically its contact detail columns like email address and phone number.

In contrast, some dimensions are considered to be rapidly changing when a dimension attribute changes often, like a stock's market price.

The common design approach in these instances is to store rapidly changing attribute values in a fact table measure. A dimension-type table could be Type 1 or Type 2, or support both types simultaneously for different columns.

A Type 1 SCD always reflects the latest values, and when changes in source data are detected, the dimension table data is overwritten. This design approach is common for columns that store supplementary values, like the email address or phone number of a customer.

When a customer email address or phone number changes, the dimension table updates the customer row with the new values.

It's as if the customer always had this contact information. It refreshes the table data to ensure the latest values are loaded.

A Type 2 SCD supports versioning of dimension members. If the source system doesn't store versions, then it's usually the data warehouse load process that detects changes, and appropriately manages the change in a dimension table.

In this case, the dimension table must use a surrogate key to provide a unique reference to a version of the dimension member.

It also includes columns that define the date range validity of the version for example, StartDate and EndDate and possibly a flag column for example, IsCurrent to easily filter by current dimension members.

For example, Adventure Works assigns salespeople to a sales region. When a salesperson relocates region, a new version of the salesperson must be created to ensure that historical facts remain associated with the former region.