ETL Testing & Data Warehouse Testing with Python and AI 2026

Develop data analytics skills and domain understanding of csv data, learn SQL, Python with pandas and numpy, Power Query, Power BI, Tableau, and Informatica basics for ETL testing and reporting.

Discover a five-point shortcut to become an ETL tester, covering software testing fundamentals, SQL, ETL and data warehouse concepts, Power Query, and Python libraries, within three months.

Explore python libraries for etl testing, including pandas for data manipulation and cleansing, PySpark for distributed processing, and SQLAlchemy for relational databases, enabling transformation and exploratory data analysis.

Install Python and Anaconda to set up a full Python data science environment with Jupyter Notebook and Visual Studio, enabling data transformation and exploratory data analysis for ETL.

Learn the pandas library for data transformation and manipulation, loading data in Jupyter notebooks, and performing exploratory analysis, data type checks, filtering, sorting, and missing value handling.

Import pandas as pd and load data from csv files in a Jupyter notebook using pd.read_csv to fetch, inspect, and display the first five records.

Explore the source schema by inspecting three columns and non-null rows, view data types, and apply sum-based checks to identify null values before etl transformations.

Filter datasets with pandas by using data frames to select rows where age is greater than 25 and gender is female, illustrating conditional criteria and result interpretation.

Explore the Pandas library cheat sheet to accelerate data wrangling and transformations for ETL testing, with quick commands for creating data frames, filtering, aggregating, and plotting.

Create a data frame in Python using pandas, by building a dictionary of columns like name, age, and city, then instantiate and print the data frame.

Create multiple data frames with pandas, define columns x and y, and concatenate them into a single data frame for console output.

Explore how pandas series is a one-dimensional labeled array that holds any data type, create a series from a list, import pandas as pd, and print the results.

Explore data inspection with pandas by loading a CSV, viewing head and tail, selecting columns, and using df.info and df.describe for data types and numeric summaries.

Explore locating specific rows and columns with loc functions in a data frame, selecting single and multiple columns by name, and slicing rows by labels for data verification.

Perform an etl style exploratory data analysis for the Titanic dataset by importing libraries, loading data from a GitHub URL, printing df, and inspecting df.info, df.shape, and data types.

Review data types and identify missing values with pandas to generate a quick statistical summary of numeric columns, including age, pclass, passenger id, survived, and embarked.

Learn to handle missing values in columns for ETL testing by identifying nulls with isnull, filling them with medians using fillna, and verifying with null sum on the Titanic data.

Explore data visualization of the Titanic dataset using python and seaborn, creating count plots of survival and survival by gender, using df and hue in the ipynb workflow.

Use histplot via seaborn to perform exploratory data analysis on the Titanic dataset, visualizing the age distribution with a matplotlib plot, setting a title, and showing the chart.

Learn how to create static, animated, and interactive plots with Matplotlib in Python, including bar plots, color customization, and exporting visuals for reports.

Create a histogram to show Titanic passengers' age distribution, using 20 bins and an RGB color, revealing peaks around ages 20 to 40 and enabling high resolution PNG sharing.

Create a scatter plot showing age on the x axis and amount paid on the y axis, using a df from the Titanic dataset, with red color and labeled axes.

Learn how to create a box plot to visualize distributions and spot outliers in data, and understand why removing outliers improves clarity in data visualizations.

Explore the Google Play Store dataset to master ETL testing through hands-on data manipulation, exploratory data analysis, and data visualization using public Kaggle sources and example code.

Explore real-time etl testing with metadata validation, data integrity checks, and initial load and incremental load validation across source to target systems.

Power query cleans data for etl by extracting from csv, text, workbook and other sources, transforming with deduping, sorting, and shaping, and loading into tables, pivot tables, or csv files.

analyze a csv data set for etl testing, review columns across tabs, apply filters in power query to extract Afghanistan and Bangladesh, then load for visualization.

Apply power query data type management hands-on by converting datasets with keyboard shortcuts, specifying string, number, and date types for columns like name, id, birthday, and children before transformations.

Create a custom column named full name in Power Query by concatenating first name and last name, then load the transformed data into the target sheet for ETL testing workflows.

Use power query to apply a custom if condition, filter candidates by portfolio and package scores to at least 13, flag for interview, and load results to CSV or cloud.

Learn to fill missing values in a column using Power Query by applying fill down, auto fill, and loading data into a new sheet, with practical workflow for ETL testing.

Remove delimiters and split a single-column data set into multiple fields using the split-by-delimiter feature in the Power Editor. Create separate columns for id, name, age, and email.

Learn to append multiple Excel sheets into a single master sheet using Power Query, selecting sheets and appending queries to consolidate datasets for business owners.

Master final hands-on ETL tasks with Power Query to clean and transform data, create tables, set data types, build conditional columns, and load results for reporting.

Learn to perform descriptive data analysis in Excel after cleaning data, using the data analysis toolpak to compute mean, median, and distribution for income, age, and gender.