Why Companies in Noida Choose Uncodemy for Data Engineering

import psycopg2

def load_daily(conn, rows, day):
    """Reload one day's partition safely: delete then insert in one transaction."""
    with conn, conn.cursor() as cur:
        cur.execute("DELETE FROM fct_orders WHERE order_date = %s", (day,))
        cur.executemany(
            "INSERT INTO fct_orders (order_id, order_date, amount) VALUES (%s, %s, %s)",
            rows)

# Running load_daily twice for the same day gives the same result.

from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator

def run_load(ds, **_):
    print("loading orders for", ds)   # call load_daily(...) here

with DAG("orders_daily", start_date=datetime(2026, 1, 1),
         schedule="@daily", catchup=False,
         default_args={"retries": 3, "retry_delay": timedelta(minutes=10)}) as dag:
    PythonOperator(task_id="load_orders", python_callable=run_load)

-- models/marts/fct_orders.sql
{{ config(materialized='incremental', unique_key='order_id') }}

SELECT order_id,
       customer_id,
       order_ts,
       CAST(order_ts AS DATE) AS order_date,
       amount
FROM {{ ref('stg_orders') }}
{% if is_incremental() %}
WHERE order_ts > (SELECT MAX(order_ts) FROM {{ this }})
{% endif %}

version: 2
models:
  - name: fct_orders
    description: One row per order. Built incrementally from stg_orders.
    columns:
      - name: order_id
        description: Unique order identifier.
        tests: [unique, not_null]
      - name: amount
        description: Order value in INR after discounts.

-- Row-count anomaly: yesterday vs average of the 7 days before it
WITH daily AS (
    SELECT order_date, COUNT(*) AS n
    FROM fct_orders
    WHERE order_date >= CURRENT_DATE - 8
    GROUP BY order_date
)
SELECT d.order_date, d.n,
       ROUND(AVG(p.n), 0) AS avg_prev_7
FROM daily d
JOIN daily p ON p.order_date < d.order_date
WHERE d.order_date = CURRENT_DATE - 1
GROUP BY d.order_date, d.n
HAVING d.n < 0.5 * AVG(p.n);

Data contract — orders feed (example)
Producer: checkout service team
Consumer: data engineering
Schema: order_id (string, required), customer_id (string),
        order_ts (timestamp, IST), amount (decimal, INR)
Freshness: new orders available within 1 hour
Changes: 2 weeks' notice for breaking changes
Contacts: producer on-call, data engineering on-call

Company / team:
Main goal: pipeline skills / modern stack / data quality
Participants (roles):
Current tools (warehouse, orchestrator, BI):
Sample patterns we can share (anonymised):
Format: on-site / online / blended
Schedule constraints:
Success measures:

Before and after (60 minutes):
1. Write an idempotent daily load for a sample table
2. Convert a long query into two dbt models with tests
3. Add a freshness and row-count check
4. Explain how you would handle a failed overnight run
Score: complete / partial / not done

Programme results
Goal:
Participants:
Assessment (before -> after):
Real pipelines or models delivered by participants:
Data quality tests added:
Bad-data tickets (before vs after period):
Manager feedback after 6 weeks: