Build Real-Time Data Pipelines with Python, SQL & Power BI

Build Real-Time Data Pipelines with Python, SQL & Power BIBuild Real-Time Data Pipelines with Python, SQL & Power BI

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Umair Nawaz

• 1d

In many real-world scenarios like sports, marketing, or investment analytics, the requirement is not just dashboards — it is continuous, reliable insights.

The challenge is handling live data from multiple sources without increasing storage load or affecting performance.

This project focuses on building a structured data pipeline:

• Live API data ingestion using Python • Filtering only relevant data before storage • Structured storage in SQL Server • Transformation and modeling using SQL • Real-time reporting through Power BI

The result is a scalable system that delivers real-time insights without unnecessary data load or manual effort.

Full implementation: https://github.com/mumairnawaz/football-analytics-warehouse

Taqwah - Top Rated UX UI Design Agency

pro

• Apr 7

Most dashboards stop at visualization. KODO goes further.

This Dashboard is designed to help users not just see financial data - but understand and act on it.

With a focus on clarity, structured insights, and real-time visibility, it turns complex information into something usable and decision-ready.

This is where dashboard design moves from display to impact.

Open to new projects. Building a data-heavy product?

👉 Let’s collaborate and design something that truly works.

finance fintechdesign moneymanagement UI Design User Research UX Design Adobe Illustrator Adobe Photoshop Figma

David Akinlosotu

• 2d

Really cool

Axel L. Lifschitz

• 2d

dbt Models / Transformation Layer

Transformation layer built in dbt on BigQuery. Models handle data cleaning, normalization of inconsistent labels, and aggregation logic for energy consumption metrics by household segment, time of day, and weather conditions.

dbt Data Analysis Data Engineering Web Scraping Docker Python

Edin Selimovic

• Apr 7

Built a production pipeline on AWS Bedrock that ingests app store reviews at scale, classifies sentiment, clusters themes, and surfaces actionable product insights in near real-time.

Before this, the team relied on a manual weekly reporting process — slow, inconsistent, and always lagging behind what users were actually saying. The pipeline replaced that entirely with automated ingestion, NLP-driven analysis, and structured output product teams could act on immediately.

Result: manual weekly reporting replaced with automated, near-real-time insight delivery.

AWS Bedrock · Python / FastAPI · S3 · NLP · LLM