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icassiem's avatar
icassiem
Post Prodigy
2 months ago
Solved

Data Environment Options Follow-Up

Good day, I previously requested assistance on designing a small data platform for Portfolio reporting Solved: Data Environment Options - Microsoft Fabric Community   My source enviornment is acr...
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  • v-dineshya's avatar
    v-dineshya
    2 months ago

    Hi icassiem ,

    1. ERD / Architecture Example.

    AWS (API / S3 / RDS / Mongo)
    |
    Fabric Notebook (Python ingest + transform, remove PII)
    |
    Lakehouse Tables (Silver Layer only)
    |
    SQL Views (Semantic Layer)
    |
    Power BI (Direct Lake / DirectQuery)

    Ex: fact_usage_metrics, dim_client, dim_product and dim_date

    2. Motivation + Risks: Replace Azure Function ($150) and Potential DB / ETL tools with single Fabric capacity.

    Note: Reusable data for reports, Centralized semantic layer and Shared across consulting + clients. Risks you must call out, Capacity limits (F2) as Small compute (2 CUs) Can slow if too many users and large joins

    Solution: Pre-aggregate data and Small models

    3. Medallion — Do you need it?

    Try below architecture.

    API --> Python (clean + aggregate + remove PII) --> Silver tables
    |
    SQL Views (Gold)

    Note: No Bronze (save cost), Silver = your storage and Gold = SQL views (no extra storage)

    4. Orchestration: Best option is Fabric Pipelines, You can Schedule notebook runs, Retry handling and No extra cost (included in F2).

    5. Query using SSMS: YES , this is supported, Please refer below steps.

    Go to Lakehouse
    Open SQL Endpoint
    Copy connection string
    Connect via SSMS (SQL Auth / AAD)

    Note: Fabric exposes T-SQL endpoint over Lakehouse

    6. Dev Access + Power BI Connectivity:

    Development: You can use Fabric web portal (primary), VS Code (optional for notebooks) and Git integration available.

    Power BI: Direct Lake Connect to Lakehouse tables and No gateway needed.

    Note: Gateway only needed if you connect to on-prem data.

    7. Why NOT AWS RDS:

    AWS RDS --> Operational DB not analytics, Power BI , RDS --> Security risk (prod exposure), Scaling --> Expensive + manual, Transformation -->No built-in ETL.

    Note: Fabric contains Separate analytics layer, No impact on prod, Built-in ETL + BI and Lower total cost.

    8. Copilot usage: Works in Power BI (narratives, DAX, Q&A) and Fabric notebooks (code assist)

    Note: It is not full pipeline automation and Not replacing ETL. It is a“Enhancement layer, not core architecture”.

    9. Python vs SQL roles: Python Ingest APIs, Transform, Aggregate and Remove PII. SQL contains Views, Joins and Semantic layer.

    10. Ingestion Pattern (overwrite vs CDC): Simple approach (recommended for F2) Overwrite or append and Use timestamps. Better approach: Incremental loads (by date) and Partition tables.

    Note: CDC Overkill for your case and Requires more complexity. You do NOT need Azure Data Factory. No extra infra needed Everything is SaaS and no hidden infra costs.

    11. Backup Plan (Power BI only approach): Yes you can, but No central data store, Repeated API calls, Not reusable and Not scalable.

    Note: It acceptable only for Very small MVP only. Power BI-only creates datasets and is not a reusable data platform.

     

    I hope this information helps. Please do let us know if you have any further queries.

     

    Regards,

    Dinesh

v-dineshya's avatar
v-dineshya
Community Support
1 month ago

Hi icassiem ,

1. Can Q&A generate a full client-specific 6‑month analysis?

Yes, but not reliably as a full “consulting-grade essay”. Power BI Q&A (LLM) is an LLM-powered semantic query layer. It can Understand natural language, Generate measures/visuals and Summarize trends. It can produce a response, but not structured like a proper report/essay, Output varies (not deterministic), Not multi-paragraph consulting-quality narrative and works best for quick insights and short summaries.

 

2. Can Copilot generate full-page narrative (your slicer-based report)?

This is closer to what you want but still not perfect. Power BI has “Smart Narrative” visual, it reads entire page (all visuals). Responds to slicers and filters. It generates multi-sentence summaries, trend descriptions and KPI insights.


Yes, your slicer-based idea WILL work with Smart Narrative, Copilot and Q&A. but it will give summaries, not full essays. If you want true client-level narrative reports, the best solution is Generate narratives in Fabric (Python) and serve them dynamically in Power BI.

 

Please refer below practical setup.

Phase 1 (F2 setup): Use smart Narrative visual, Simple Copilot Q&A. It show value fast.

Phase 2 (upgrade your “wow factor”): Add Python-generated narratives and
Stored narrative tables.

 

You want Client-level, 6-month, detailed narrative (essay style). Please refer below workaround.

Combine Fabric + Python + Power BI.

1. Generate narrative in Notebook. Please refer below sample python code.

def generate_summary(df):
growth = df["revenue"].pct_change().mean()
trend = "growth" if growth > 0 else "decline"

summary = f"""
Over the past 6 months, the client shows a trend of {trend}.
Key drivers include product usage increases and seasonal variation.
Peak performance occurred in {df['month'].max()}.
"""

return summary

2. Store in table:

client | narrative | date_range

3. Show in Power BI: Use table/text visual and filter by slicer. Now you get Stable, Repeatable, Client-specific and “Essay-like”.

 

I hope this information helps. Please do let us know if you have any further queries. If you face any issues or need any help in future. please create a thread in Fabric community . we are happy to help you .

Home - Microsoft Fabric Community

 

Regards,

Dinesh

 

 

icassiem's avatar
icassiem
Post Prodigy
1 month ago

v-dineshya Thank You Very Much, really appreciate your help ❤️🙏

 

Wish me luck, i will post in the fabric in the near future