Biography & Background

Turning complex data into production systems

Product Management Trainee at RVR Projects Pvt Ltd, Microsoft Certified Fabric Analytics Engineer (DP-600 & PL-300), Laravel developer, and IEEE-published computer vision researcher.

Who I am?

I am Sasank Mangamuri, a computer science engineer and Product Management Trainee at RVR Projects Pvt Ltd, specializing in operational telemetry, lakehouse architectures, and cloud enterprise applications.

With dual Microsoft Associate certifications in Fabric Analytics Engineering (DP-600) and Power BI Data Analytics (PL-300), I bridge the gap between low-level data transformation and executive business intelligence. I design medallion lakehouses on OneLake with streaming PySpark ingestion and Direct Lake semantic models that query tens of millions of rows in seconds.

On the software engineering side, I build modular enterprise CRM and ERP systems using Laravel, PHP, MySQL, and Nginx deployed on DigitalOcean droplets. I have integrated automated billing, Razorpay payment webhooks, and granular role-based access control (RBAC).

In research, I worked with the Department of Advanced CSE at VFSTR to author and publish a peer-reviewed computer vision paper in IEEE Xplore (ICIMA 2026) introducing Coordinate Attention to ConvNeXt for chest X-ray diagnosis.

Current Role
Product Management Trainee · RVR Projects Pvt Ltd

Education
B.Tech in CSE (Data Science Honours) · VFSTR

Core Focus
Microsoft Fabric, OneLake, PySpark, Power BI

Research
IEEE Published Author (Doc ID 11564670)

OneLake Architecture Specialization

Interactive Medallion pipeline simulation: Bronze ingestion, Silver cleansing, Gold star-schema, and Direct Lake VertiPaq caching.

MICROSOFT FABRIC DP-600

STAGE 01 · BRONZE LAYER

Raw Landing & Streaming Ingestion

Captures streaming telemetry, CRM webhooks, and raw transactional files into OneLake.
Utilizes PySpark structured streaming with Apache Kafka / Eventstream connectors.
Stores data in raw unmutated Delta Parquet format for tamper-proof audit trails.

14.2k/s

Stream Throughput

0.00%

Ingestion Loss

Delta

Raw Format

pyspark ingest_bronze.py

# OneLake Raw Ingestion Pipeline (Bronze)
from pyspark.sql import functions as F

bronze_df = (spark.readStream
    .format("cloudFiles")
    .option("cloudFiles.format", "json")
    .option("cloudFiles.schemaLocation", "abfss://onelake@fabric/schemas/bronze")
    .load("abfss://onelake@fabric/landing/")
    .withColumn("ingestion_timestamp", F.current_timestamp())
    .withColumn("source_system", F.lit("Shanix_ERP")))

(bronze_df.writeStream
    .format("delta")
    .outputMode("append")
    .option("checkpointLocation", "abfss://onelake@fabric/checkpoints/bronze")
    .toTable("lakehouse.bronze_events"))