🎬 Overview of Data Warehousing Concepts – Understand the architecture and design of data warehouses.

• Key components of data warehouses such as staging, integration, and presentation layers.

• The role of metadata in data warehouses.

🎬 Types of Data Warehouses – Explore various types of data warehouses.

• Enterprise Data Warehouses (EDW) and how they centralize organizational data.

• Operational Data Stores (ODS) – How they differ from traditional data warehouses.

• Data Marts – How they focus on specific areas of business.

🎬 Understanding ETL Process – Learn the ETL process and its significance.

• Key steps: Extraction, Transformation, and Loading, and how they work together.

• Extracting data from various sources like databases, flat files, and APIs.

• Transformation rules, data cleansing, and validation methods.

• Loading data into different destinations such as Data Warehouses or Data Lakes.

🎬 Role of Business Intelligence in Data Warehousing – How BI helps leverage data.

• How data warehouses serve as the foundation for BI tools and reporting.

• Real-time and historical data analysis using BI tools like Power BI, Tableau.

⌨ COURSE CHEAT SHEET DOWNLOAD

🎬 Introduction to SQL and Relational Databases – Basic concepts of SQL and its importance.

• Relational database structure, tables, rows, and columns.

• Data types, primary and foreign keys in relational databases.

🎬 Writing SQL Queries for Data Extraction – Practical SQL querying techniques.

• SELECT statements, filtering data with WHERE clauses.

• Using ORDER BY and GROUP BY for sorting and grouping data.

🎬 SQL Joins for Data Validation – Combining tables for comprehensive validation.

• Understanding INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

• Practical examples for validating data across multiple tables.

🎬 Data Manipulation with SQL – INSERT, UPDATE, DELETE commands for manipulating data.

• Inserting data into tables for testing purposes.

• Updating existing data to simulate real-time scenarios.

• Deleting unwanted records to ensure data integrity.

🎬 Advanced SQL for Data Transformation – Master advanced SQL features.

• Aggregation functions (SUM, COUNT, AVG) for reporting.

• CASE statements and conditional queries for complex transformations.

🎬 SQL for Data Comparison and Validation – Techniques for validating data.

• Comparing data from source and target systems using SQL queries.

• Using COUNT, GROUP BY, and HAVING clauses for data verification.

✍ Quiz 2: SQL Practical Applications

🎬 Introduction to ETL Testing – Importance of testing in ETL processes.

• Ensuring data accuracy, completeness, and integrity in ETL pipelines.

🎬 Extracting Data from Multiple Sources – Data extraction techniques and challenges.

• Extracting data from structured (databases) and unstructured sources (text files, APIs).

• Validating extracted data for consistency and completeness.

🎬 Validating Data Transformation Rules – Verifying transformation accuracy.

• Testing business rules for transforming data.

• Handling data format changes, and data enrichment during transformation.

🎬 Testing Data Loading into Target Warehouse – Techniques to ensure accurate data loads.

• Verifying data completeness and integrity during the load process.

• Testing incremental and full data loads.

🎬 Common ETL Challenges and Solutions – Tackling ETL-specific challenges.

• Handling large data volumes, slow performance, and scheduling errors.

• Addressing data duplication and missing data during ETL processes.

✍ Quiz 3: ETL Testing Knowledge Check

🎬 Ensuring Data Accuracy, Completeness, and Consistency – Techniques to test data integrity.

• Validating that data is accurate, complete, and consistent across systems.

🎬 Data Profiling Techniques – Profiling data to identify issues.

• Profiling data to identify duplicates, missing values, and outliers.

🎬 Data Cleansing for Data Quality – Methods for cleansing data.

• Standardizing and formatting data to meet data warehouse requirements.

🎬 Identifying and Fixing Data Quality Issues – Troubleshooting data issues in ETL.

• Using tools and scripts to automatically detect and fix data quality problems.

✍ Quiz 4: Data Quality Concepts

🎬 Introduction to Automation in ETL Testing – Benefits of automating ETL testing.

• Automating repetitive ETL tasks to save time and reduce human error.

🎬 Tools for Automating ETL Testing – Industry-standard ETL automation tools.

• Using Informatica, Talend, and Apache NiFi for ETL automation.

🎬 Creating Automated Test Cases for Data Validation – Writing automation scripts.

• Automating validation of data transformation, extraction, and loading processes.

🎬 Integrating Automated Testing into CI/CD Pipelines – Continuous testing and integration.

• Setting up automated tests in CI/CD pipelines for ongoing data validation.

✍ Quiz 5: Automating ETL Testing

🎬 Introduction to BI Report Testing – Importance of validating business reports.

• Ensuring BI reports accurately represent underlying data from the warehouse.

🎬 Validating OLAP Data for BI Reports – Testing OLAP cubes for data aggregation accuracy.

• Testing roll-ups, drill-downs, and slicing and dicing operations in OLAP cubes.

🎬 Testing BI Reports for Accuracy and Consistency – Techniques for report validation.

• Validating reports for consistency across multiple dimensions, filters, and formats.

🎬 Ensuring Real-Time Updates in BI Reports – Validating real-time data reporting.

• Ensuring that reports reflect the most current data, preventing outdated insights.

🎬 Testing Report Filters and Aggregations – Verifying the functionality of BI tool filters.

• Testing how filters and aggregations affect data views and report accuracy.

✍ Quiz 6: BI Report Testing

🎬 Defining an End-to-End ETL Testing Process – Apply all concepts to create a complete ETL process.

• Plan and define each stage of the ETL process with data validation points.

🎬 Developing Test Cases for Data Extraction, Transformation, and Loading – Building test cases for each step.

• Testing data at each stage: extraction, transformation, and loading.

🎬 BI Report Testing and Validation – Validating generated BI reports.

• Ensuring the final BI reports reflect accurate and complete data from the warehouse.

📜 Download Final Project Code and Resources

✍ Quiz: End-to-End Testing Knowledge Check

🎬 Course Overview – Automate ETL/DWH testing using Python, pytest as the test framework, and Jenkins for CI/CD.

• Build repeatable, versioned automated tests for source → transformation → target validation.

• Integrate tests into CI/CD pipelines (Jenkins) to run on every commit, PR or schedule.

Learning Objectives

• Write pytest tests for row counts, checksums, column-level comparisons and business-rule validations.
• Use fixtures for DB connections, test data setup/teardown and parametrized tests for multiple tables.
• Create reproducible test environments with Docker for CI runs.
• Configure Jenkins pipelines to run tests, publish HTML reports and fail builds on test failures.

Recommended Python Libraries

psycopg2 / mysql-connector-python / sqlalchemy, pandas, pytest, pytest-xdist, pytest-html, python-dotenv, requests (for APIs).

Project layout (recommended)

etl-testing/
├─ tests/
│  ├─ test_row_count.py
│  ├─ test_checksum.py
│  ├─ test_transformations.py
│  └─ conftest.py
├─ requirements.txt
├─ Dockerfile
└─ Jenkinsfile
  

Example: conftest.py (DB connections & fixtures)

# tests/conftest.py
import os
import pytest
import psycopg2
import pandas as pd
from sqlalchemy import create_engine

def _get_env(name, default=None):
    return os.environ.get(name, default)

@pytest.fixture(scope="session")
def pg_engine():
    # Example for Postgres - connection details read from env (CI injects them)
    user = _get_env("PG_USER")
    pwd  = _get_env("PG_PASSWORD")
    host = _get_env("PG_HOST", "localhost")
    port = _get_env("PG_PORT", "5432")
    db   = _get_env("PG_DB")
    url = f"postgresql://{user}:{pwd}@{host}:{port}/{db}"
    engine = create_engine(url)
    yield engine
    engine.dispose()

@pytest.fixture
def fetch_df(pg_engine):
    def _fetch(query):
        return pd.read_sql_query(query, pg_engine)
    return _fetch
  

Example pytest: row count validation

# tests/test_row_count.py
def test_row_count_source_to_target(fetch_df):
    src_q = "SELECT COUNT(*) as cnt FROM source_schema.orders WHERE load_date = CURRENT_DATE"
    tgt_q = "SELECT COUNT(*) as cnt FROM target_schema.orders WHERE load_date = CURRENT_DATE"
    src = fetch_df(src_q)["cnt"].iloc[0]
    tgt = fetch_df(tgt_q)["cnt"].iloc[0]
    assert src == tgt, f"Row count mismatch: src={src} tgt={tgt}"
  

Example pytest: checksum / hash validation

# tests/test_checksum.py
import hashlib

def _row_checksum(row, cols):
    s = "|".join(str(row[c]) for c in cols)
    return hashlib.md5(s.encode("utf-8")).hexdigest()

def test_checksum_column_level(fetch_df):
    cols = ["id","amount","status"]  # columns to include in checksum
    src_q = "SELECT id, amount, status FROM source_schema.orders WHERE load_date = CURRENT_DATE ORDER BY id"
    tgt_q = "SELECT id, amount, status FROM target_schema.orders WHERE load_date = CURRENT_DATE ORDER BY id"
    src_df = fetch_df(src_q)
    tgt_df = fetch_df(tgt_q)
    assert len(src_df) == len(tgt_df), "Row count mismatch before checksum"
    for i in range(len(src_df)):
        assert _row_checksum(src_df.iloc[i], cols) == _row_checksum(tgt_df.iloc[i], cols)
  

Example pytest: transformation/business rule

# tests/test_transformations.py
def test_amount_positive(fetch_df):
    q = "SELECT amount FROM target_schema.orders WHERE load_date = CURRENT_DATE AND amount < 0 LIMIT 1"
    df = fetch_df(q)
    assert df.empty, f"Found negative amounts: {len(df)} rows"
  

Run tests locally

Install dependencies and run pytest with HTML report generation:

pip install -r requirements.txt
pytest -n auto --maxfail=1 --disable-warnings --html=report.html --self-contained-html
  

Dockerfile for CI test runner

# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
ENTRYPOINT ["pytest", "-n", "auto", "--maxfail=1", "--html=report.html", "--self-contained-html"]
  

Jenkinsfile (Declarative) — build, run tests, archive reports

pipeline {
  agent any
  environment {
    # Bind Jenkins Credentials (set these up as "Username with password" / "Secret text" in Jenkins)
    DB_USER     = credentials('jenkins-pg-user')   // username/password pair
    DB_PASSWORD = credentials('jenkins-pg-password')
    PG_HOST     = credentials('jenkins-pg-host')   // or use secret text
    PG_PORT     = '5432'
    PG_DB       = credentials('jenkins-pg-db')
  }
  stages {
    stage('Checkout') {
      steps {
        checkout scm
      }
    }
    stage('Build Docker image') {
      steps {
        script {
          dockerImage = docker.build("etl-tests:${env.BUILD_NUMBER}")
        }
      }
    }
    stage('Run tests (Docker)') {
      steps {
        script {
          dockerImage.inside("--network host -e PG_USER=${DB_USER_USR} -e PG_PASSWORD=${DB_USER_PSW} -e PG_HOST=${PG_HOST} -e PG_PORT=${PG_PORT} -e PG_DB=${PG_DB}") {
            sh 'pytest -n auto --maxfail=1 --disable-warnings --html=report.html --self-contained-html || true'
            sh 'ls -lah'
          }
        }
      }
    }
    stage('Archive & Publish') {
      steps {
        archiveArtifacts artifacts: 'report.html', fingerprint: true
        junit allowEmptyResults: true, testResults: '**/pytest*.xml' // if producing junit xml
        publishHTML (target: [
          allowMissing: false,
          alwaysLinkToLastBuild: true,
          keepAll: true,
          reportDir: '.',
          reportFiles: 'report.html',
          reportName: 'PyTest HTML Report'
        ])
      }
    }
  }
  post {
    always {
      echo "Cleaning workspace"
      cleanWs()
    }
    failure {
      mail to: 'team@example.com',
           subject: "Build ${env.BUILD_NUMBER} failed",
           body: "See Jenkins build ${env.BUILD_URL}"
    }
  }
}
  

Notes on Jenkins credentials usage

• Store DB credentials in Jenkins Credentials store and inject into the build using `credentials()` or the Credentials Binding Plugin.
• Never hardcode secrets in repo. Use environment variables or Vault for production pipelines.

Best practices & extras

• Parametrize tests to cover multiple tables and partitions rather than duplicating code.
• Use test data factories or snapshot data for predictable test runs; keep one environment for integration tests and another for smoke tests.
• Add negative tests: null-handling, duplicate keys, late-arriving data.
• Generate machine-readable test output (JUnit XML) for Jenkins and human-friendly HTML via `pytest-html`.
• Parallelize long-running tests with `pytest-xdist` and fail-fast for early feedback.
• Consider data-migration testing tools (dbt for transformations) and integrate dbt tests into the same pipeline if applicable.

📥 COURSE CHEAT SHEET: include the conftest snippet, pytest commands and Jenkins file for quick download in your course materials.