The Error: A lot of users import the data in a single flat table without applying the best practices of relational modeling.
The Problem with It:
Flat tables are inflexible, and they may cause redundant data.
DAX constructs turn out to be cumbersome and wasteful.
Graphs are not effective when dealing with big data.
The Fix:
Use a star schema and have fact tables (numerical data) and dimension tables (descriptive data). Such structure improves performance and eases DAX formulas.
The Error: Left over of wrong data types (I.e. considering a date column as text or numbers as string).
The Reason It Is a Problem:
Brings problems with the aggregations, filtering, and sorting.
Cuts relationships and retards visuals.
Makes it difficult to conduct time intelligence functions.
The Fix:
Before loading data, it is always advisable to read data types in Power query editor and make corrections. Format text fields such as dates, numbers, currency and percent appropriately.
The Blunder: Having excessively many calculated columns in lieu of DAX measures.
The Problem with It:
Causes files to be larger and increases the refresh time.
Causes inflated models and duplicate logic.
Lessens performance, particularly with large datasets.
The Fix:
Apply metrics in computations that relies on report filters and interactions. Make reservations with respect to calculating columns and use them only when you have to.
The Misconception: Wrong table relationships Table relationships could be wrong, and more so when both directions are allowed.
The Problem with It:
Causes circular relationships and inappropriate outcomes.
May make debugging of DAX extremely hard.
Lowers visibility of models.
The Fix:
Employ single direction relationships in default. Use bi-directional filters only when it makes sense like many to many relationship with caution.
The Error: Bringing in columns, tables or rows that are not needed.
The Problem with It:
Enlarges size of memory and file.
Delays refreshes and responsiveness of interaction.
Makes model maintenance difficult.
Power BI reports have to be fast, reliable, and scalable, and avoiding these data modeling issues is important to achieve that. It does not matter whether you are doing enterprise preparation or working on Power BI training; these basic practices will bring your analytics skills to the next level.
Being an Indian and aiming to master your craft, you may want to attend the Power BI training in Hyderabad, a city with a well-developed environment for learning and training data analytics skills. Good data models are not incidental, make yours count.
Analytics Benchmark
Analytics benchmark is the Best Microsoft Technologies Training Institute In Hyderabad Provids Online & Classroom Training In Hyderabad and also Across the World by 15+ years of Industry experts.
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