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Domain Modeling: Aggregates. https://lnkd.in/eW2NynhU #ddd #softwareengineering #softwaredevelopment #softwarearchitecture #softwaredeveloper #softwareengineer
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Consultant database, data warehouse, BI, data mart, cube, ETL, SQL, analysis, design, development, documentation, test, management, SQL Server, Access, ADP+, Freelance. JOIN people ON data.
Snowflake a dimension into multiple dimensions and make it look like a single dimension with a view. Read about Subdimensions and Outrigger dimension.
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Data Science and Engineer| Snowflake | Spark| Databricks| SQL | Data Modeling| No-SQL|ETL| Airflow |Supervised & Unsupervised Learning| Deep Networks | Pytorch | Pandas| Numpy| Tableau
The Edge Case(NULL) NULL is used as a default filler in the records when the values of the attributes are unavailable or unknown. Going in details, Nulls can get populated in three places in dimensional modeling, in the first place when the values of attributes in the dimension table are not-available or not-applicable, second when foreign keys in the fact tables referring dimensional tables are null and finally when some of measures in the fact table are null. The Nulls should be handled in the first two cases. 1) Handling NULL at non-prime attributes in dimensional table: The NULL valued attributes should be replaced with either "NA" or a default value associated with the attribute. 2) Handling NULLs at the referencial constraints in fact table: The NULL values in the foreign keys of the fact table should be replaced to refer to dummy record(to manage null references) in the dimensional table. 3) Handling NULLS at measures in the fact table: The NULL values in this case should be handled only if there is business implication.
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Principal Consultant at Agile Analytics, specialising in Power BI with a strong interest in DAX and financial modelling
Alias columns for calculation groups with Selection Expressions https://lnkd.in/gmUbZ_eC This is a follow-up to my earlier post on alias columns in calculation groups. It turns out that Selection Expressions provide an interesting alternative approach!
Alias columns for calculation groups with Selection Expressions - Owen Auger's BI Blog
https://owenaugerbi.com
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#dsaproblem01 🆕 #️⃣tags: #Arrays #sorting 🔴title: Largest Number❗ 🔴Problem Statement❓ Given an array A of non-negative integers, arrange them such that they form the largest number. Note: The result may be very large, so you need to return a string instead of an integer. 🔴Problem Constraints🔒 1 <= len(A) <= 100000 0 <= A[i] <= 2*109 🔴Input Format The first argument is an array of integers. 🔴 Output Format Return a string representing the largest number. 🔴Example Input Input 1: A = [3, 30, 34, 5, 9] Input 2: A = [2, 3, 9, 0] 🔴Example Output Output 1: "9534330" Output 2: "9320" 🔴Example Explanation Explanation 1: Reorder the numbers to [9, 5, 34, 3, 30] to form the largest number. Explanation 2: Reorder the numbers to [9, 3, 2, 0] to form the largest number 9320. ⁉We can discuss the approaches and solution in the comments below ⬇
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checking maintenance PC for DATA logger records #datalogger #relays #fault #codes #simulation #tsts
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Data Science and Engineer| Snowflake | Spark| Databricks| SQL | Data Modeling| No-SQL|ETL| Airflow |Supervised & Unsupervised Learning| Deep Networks | Pytorch | Pandas| Numpy| Tableau
The DE'normalized' Dimensions The two main goals of dimensional modeling are understandability and performance. Denormalized dimensions helps the dimensional model achieve the goal of simplicity and improve performance. In general, the dimensions are wide(contains more attributes) and shallow (less number of rows compared to Facts). One who comes from OLTP systems may have a different opinion that normalizing the dimensions can reduce the space required to accommodate dimensions and reduce redundancy, and by normalizing the cost gets reduced and performance gets increased. In reality the normalized dimensions will decrease the performance and increase the complexity of dimensional model. Going in detailed, in general, fact are deep(contains much much more number of records compared to dimensions). In this case, if we are using normalized dimensions, we need to maintain all the keys of hierarchy of normalized dimension in the fact table. For example, if the dimension table is normalized into 3 tables all the 3 keys should be placed in the fact table, on the other side if we are not normalizing, then the hierarchy of keys can be replaced with one single key to the dimension. Moreover, join is the most expensive operation, and we dont generally join just dimensions but we include facts to get materialized results. This increases the complexity of the query and reduces the performance. Further, the model will be complex to understand. Therefore, normalizing louses the primary goals of the dimensional model and should be avoided.
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Two Major Test Design Techniques- ---------------------------------------- Boundary Value Analysis:- Minimum and Maximum values that use to check for a specific component. Equivalence partitioning:- Divides the input data into partitions.
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Imagine what we could engineer if we were all speaking the same language, instead of arguing over interpretations of a drawing.
🔍 Are you familiar with the role of basic dimensions when using the position control in GD&T drawings? In this question line video, Brandon walks through an example showing how to correctly interpret and apply position tolerance when locating holes. Check it out: https://hubs.la/Q02pv2zv0 #PositionTolerance #BasicDimensions #GDandT
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