Alteryx Transformation:

1. Data Acquisition:

   • Retrieve data from Open Data sources provided in the assignment, ensuring data is obtained for all specified cities: Chicago, Dallas, etc.
   • Format data into delimited files, preferably TSV as recommended.
   • Follow guidelines for data staging and adhere to staging table naming conventions (prefix with "stg_").

2. Data Profiling:

   • Utilize Alteryx for data profiling purposes to gain insights into the data characteristics.
   • Document the analysis findings in detail, highlighting any discrepancies or notable patterns observed in the data.

3. Data Preparation:

   • Implement necessary data transformations within Alteryx workflows to address multi-valued attributes and data type conversions for each column.
   • Ensure proper handling of dates, including conversion of any text-based dates to date data types.
   • Document the transformation logic applied to each column for reference.

4. Data Loading:

   • Execute the Alteryx workflows to load data into the designated stage tables.
   • Include screenshots of the completed workflows as part of the submission.
   • Capture runtime information for each workflow and list the total time taken for job completion.

5. Stage Table Creation:

   • Provide DDL scripts for creating stage tables, incorporating additional columns like DI_CreateDate and DI_WorkflowFileName as specified in the guidelines.
   • Ensure consistency in data types and integrity constraints in the stage tables.

Dimensional Model Design:

1. Identification of Dimensions & Facts:

   • Define the dimensions and facts based on the requirements outlined in the assignment.
   • Utilize tools like ER/Studio or Navicat to create a dimensional data model.
   • Include screenshots or diagrams illustrating the model structure.

2. DDL Script Generation:

   • Generate DDL scripts for creating the dimensional schema in the chosen database platform (MySQL, SQL Server, Azure SQL database, etc.).
   • Ensure the schema aligns with the defined dimensional model.

Talend Integration:

1. Integration Workflow Creation:

   • Develop Talend jobs to load data from stage tables into the dimensional model.
   • Include transformations and mappings necessary for data integration.
   • Ensure proper handling of surrogate keys and date/datetime columns.

2. Data Loading Validation:

   • Verify data integrity by comparing row counts before and after loading into the target tables.
   • Provide SQL scripts and screenshots to demonstrate the validation process.

Dashboard Creation:

1. BI Dashboard Development:

   • Utilize Power BI Desktop and Tableau Desktop to create interactive dashboards.
   • Tableau Link: https://public.tableau.com/app/profile/yuga.santosh.kanse/viz/FoodInspections_17152070379370/Sheet1
   • Incorporate visualizations representing food inspection results based on specified criteria such as inspection result, type, risk category, etc.
   • Include elements like inspection numbers, license numbers, violations, and inspector comments for comprehensive analysis.
   • Provide screenshots or links to the published dashboards on Power BI Service and Tableau Online.

2. Dashboard Contents:

   • Detail the components included in the dashboards, highlighting key metrics and insights derived from the data.
   • Emphasize the relevance of each visualization in addressing the objectives of the assignment.
   • Include any additional contextual information or reference materials related to food inspection regulations or data sources used.

By following these detailed steps and documentation guidelines, you'll ensure clarity and transparency in your project workflow and outcomes, facilitating understanding and evaluation by stakeholders and collaborators.