Why Companies in Delhi Choose Uncodemy for Data Engineering

-- A simple star schema for sales reporting
CREATE TABLE dim_customer (
  customer_key SERIAL PRIMARY KEY,
  customer_id  TEXT UNIQUE NOT NULL,
  city         TEXT,
  segment      TEXT
);

CREATE TABLE dim_date (
  date_key  DATE PRIMARY KEY,
  month     INT NOT NULL,
  year      INT NOT NULL
);

CREATE TABLE fact_sales (
  order_id      TEXT PRIMARY KEY,
  customer_key  INT REFERENCES dim_customer(customer_key),
  date_key      DATE REFERENCES dim_date(date_key),
  amount        NUMERIC(12,2) NOT NULL CHECK (amount >= 0)
);

-- Each customer's latest order and running spend
SELECT customer_key, order_id, date_key, amount,
       SUM(amount) OVER (PARTITION BY customer_key ORDER BY date_key) AS running_spend,
       ROW_NUMBER() OVER (PARTITION BY customer_key ORDER BY date_key DESC) AS recency_rank
FROM fact_sales
ORDER BY customer_key, date_key;

import logging
import sqlite3
import pandas as pd

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")

def extract() -> pd.DataFrame:
    return pd.DataFrame({"order_id": ["A1", "A2", "A2"], "amount": ["100", "250", "250"],
                         "order_date": ["2026-03-01", "2026-03-02", "2026-03-02"]})

def transform(df: pd.DataFrame) -> pd.DataFrame:
    df = df.drop_duplicates("order_id").copy()
    df["amount"] = pd.to_numeric(df["amount"], errors="coerce")
    df["order_date"] = pd.to_datetime(df["order_date"]).dt.date
    return df.dropna(subset=["amount"])

def load(df: pd.DataFrame, db: str = "warehouse.db") -> None:
    with sqlite3.connect(db) as conn:
        df.to_sql("fact_sales", conn, if_exists="replace", index=False)
    logging.info("loaded %d rows", len(df))

if __name__ == "__main__":
    load(transform(extract()))

# Airflow DAG (Airflow 2.x) wrapping the same steps
from datetime import datetime
from airflow import DAG
from airflow.operators.python import PythonOperator

import etl  # the module above

with DAG("daily_sales", start_date=datetime(2026, 1, 1),
         schedule="@daily", catchup=False) as dag:
    PythonOperator(task_id="run_etl",
                   python_callable=lambda: etl.load(etl.transform(etl.extract())))

import pandas as pd

def check_sales(df: pd.DataFrame) -> list[str]:
    errors = []
    if df.empty:
        errors.append("no rows loaded")
    if df["order_id"].duplicated().any():
        errors.append("duplicate order_id values")
    if df["amount"].isna().any():
        errors.append("missing amounts")
    if (df["amount"] < 0).any():
        errors.append("negative amounts")
    return errors

df = pd.DataFrame({"order_id": ["A1", "A2"], "amount": [100.0, -5.0]})
problems = check_sales(df)
print("OK" if not problems else "FAILED: " + "; ".join(problems))

-- Each query should return zero rows if the data is healthy
SELECT order_id, COUNT(*) FROM fact_sales GROUP BY order_id HAVING COUNT(*) > 1;
SELECT * FROM fact_sales WHERE amount IS NULL OR amount < 0;
SELECT f.order_id FROM fact_sales f
LEFT JOIN dim_customer c ON c.customer_key = f.customer_key
WHERE c.customer_key IS NULL;   -- orphan facts

Team: ______________   Location: Delhi / NCR / remote
Current tools: ______________
Main data sources: ______________
Top gaps: SQL modelling / pipelines / cloud / quality
Goal: e.g., daily sales pipeline with tests running on schedule
Format: classroom / online / hybrid
Success measure: pipeline in use; checks passing; fewer report disputes

1. Does the course include SQL modelling and window functions?
2. Will learners build a scheduled pipeline?
3. Which cloud data tools are practised hands-on?
4. Are data quality tests part of projects?
5. Can we see a sample project?
6. What are the current options for teams, and fees in writing?

If a course covers foundations only, add:

Weeks 1-2  Data modelling: star schemas, slowly changing dimensions
Weeks 3-4  Orchestration basics with Airflow or cron
Weeks 5-6  Cloud object storage and one warehouse trial
Weeks 7-8  Data quality tests and a monitored pipeline project