Data Quality Testing Unleashed : Theory to Implementation

Explore data quality as a dynamic measure spanning completeness, consistency, timeliness, validity, and reliability, evaluated against business purpose to drive accurate CRM email marketing outcomes.

Unlock high quality data to enable informed decisions, optimize operations, and drive innovation across retail, airline, grocery, and technology sectors.

Explore data quality measurement dimensions, including accuracy, completeness, consistency, timeliness, validity, and uniqueness, to assess data usability and maintain reliable, fit-for-use information.

Maintain data accuracy by aligning values with real world entities and ensuring correctness across data life cycle with validation checks, cross-referencing, and audits to prevent errors and ensure trustworthy results.

Explore data completeness by distinguishing required and optional attributes, ensuring all essential values are populated for a dataset, illustrated with a loan application example to support analysis and decision making.

Ensure data consistency across all systems by maintaining uniform values and formats, using synchronization and validation rules to support reliable analysis, accurate reporting, and informed decisions.

Master data timeliness by ensuring data availability and up-to-date information to support swift operational and strategic decisions, using real-time vs delayed data strategies and time-window guidelines.

Validate data against defined rules to ensure type, format, range, and constraints align with expectations, preventing invalid entries and misleading analyses. Enforce realistic ranges and standard formats.

Maintain data uniqueness by preventing duplicates across customer records from multiple touchpoints through strict validation and deduplication, ensuring reliable data for accurate analysis and informed decision making.

Define data quality and its business impact, and introduce data quality measurement dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness—preparing for data quality testing.

Learn how data quality testing, guided by measurement dimensions and the data's intended use, applies automated checks to verify formats, missing values, and data relationships.

Improve risk management and regulatory compliance by testing data quality for accuracy, completeness, and reliability, ensuring trusted decision making and seamless data integration.

Clarify the distinction between data testing and data quality testing. Compare how data testing verifies flow and transformations in ETL pipelines, while data quality testing measures accuracy and timeliness.

Explore how data quality tools automate and streamline tasks to keep data accurate, consistent, and reliable through extraction, transformation, and loading pipelines, with examples like Great Expectations, Informatica, and Talend.

Navigate a data pipeline from operational data through etl to data lake, warehouse, and marts, highlighting data quality testing at each stage for schema, completeness, and rules.

Define clear data quality rules and automate testing to ensure accuracy, completeness, consistency, and validity across the data life cycle, including real-time monitoring and cross-team collaboration.

Define data quality testing as verifying that data meets accuracy, completeness, and consistency standards, and integrate quality checks in the data pipeline with real-time monitoring, automation, and Great Expectations.

Explore why Great Expectations is the go-to framework for data quality testing, highlighting its robustness, open source community, seamless pipeline integration, and comprehensive data docs with scalable cloud options.

Explore how great expectations core enables automated data quality testing within data pipelines, validating data against defined standards and safeguarding data integrity with the open source Python library.

Explore the core building blocks of Great Expectations, from data context to docs, and learn how to write data quality tests, run validations, and track quality with a checkpoint.

Install the Great Expectations and pandas libraries with pip in a Jupyter notebook to set up data quality testing workflows.

Build a gx core workflow with a pandas temperature data frame in an ephemeral data context, defining data sources, assets, batch definitions, and an expectation suite to run validations.

Create a GX core workflow in a Jupyter notebook by building an ephemeral data context and a pandas data source, then define datasets, batch definitions, validations, and expectations.

Learn to interpret the validation result object in Great Expectations, exploring its success flag, suite name, statistics, and per-expectation results, and how configuration changes impact outcomes.

Control the attributes in the validation result using a checkpoint in the data context hierarchy, and explore the four formats: boolean, basic, summary, and complete.

Explore customizing validation results with a GCS score object hierarchy by creating a checkpoint and selecting result formats such as boolean_only, basic, summary, or complete.

Explore the GCS score and its object hierarchy, install GCS score, and build a testing workflow with a pandas data frame and data context, including expectations and results.

Learn how to create parameterized expectations that adapt to different data sets by supplying runtime temperature ranges via a parameter object, enabling reusable checks across checkpoints.

Define parameterized expectations for temperature range and city names using variables, then run a checkpoint with a runtime object to supply values dynamically, reusing the same suite across data sets.

Explore conditional expectations that apply to subsets of a data set, using row_condition and condition_parser to enforce different constraints for personal versus commercial vehicles.

Define conditional expectations that evaluate the temperature range only for Mumbai records using pandas syntax. Verify the subset contains 31 Mumbai records out of 62, matching the Delhi count.

Create custom expectations in great expectations by defining a new metric and tying it to domain-specific validation across pandas, SQL, and spark, integrated with the standard validation workflow.

Create a custom expectation to validate Indian PAN formats using a regex-based metric, integrate with column map, and demonstrate with an in-memory data frame.

Explore table level expectations from the Great Expectations core library to validate data structure and completeness, including row count, column count, and column order for reliable pipelines.

Explore column level expectations to validate data quality and integrity across columns, ensuring value ranges, data types, and uniqueness, with not null, in set, and regex checks.

Apply column aggregate expectations to validate summary statistics like mean, sum, and max, ensuring the column's distribution and range meet predefined standards and reveal outliers without inspecting every record.

Explore column distribution expectations to verify a column’s statistical properties align with patterns for accurate analysis, using z score thresholds, standard deviation ranges, and KL divergence.

Apply set based expectations to validate categorical data against predefined sets and exclude unwanted entries, such as ensuring column values are in set and values not in set.

Explore advanced data validation with parametrized expectations, custom expectations, and conditional expectations in the Great Expectations library, guided by the Expectation Gallery to build a flexible, precise data quality framework.

Explore how actions automate responses in Great Expectations, triggering results after validation, notifying data users via email or the data engineering team, and pausing pipelines when needed.

Integrate the built-in send email action into a Great Expectations validation workflow to auto notify teams after a validation run, with triggers for all, failure, success, or warnings.

Explore built-in actions in GX Core, including API notification, Microsoft Teams notification, Slack notification, SNS notification, and update data docs, to automatically notify, document, and integrate validation results into workflows.

Data quality actions in Great Expectations are integrated into validation pipelines to automate follow-up tasks, send mail actions, notify stakeholders, store validation results for audits, and update documentation.

Explore data docs, a powerful Great Expectations feature that automatically reports data quality through expectations, validation results, and profiling. These centralized reports improve transparency and track data quality over time.

Generate data docs by configuring the data context, data source, asset, batch, and expectations. Run a checkpoint with the update data docs action to refresh the docs and inspect results.

Review generated data docs from our previous demo, examining two checkpoint validation runs, one failing and one succeeding, with per‑expectation details for city and temperature and overall data quality insights.

Explore data docs and their role in data quality management, featuring automated generation, interactive visualizations, documenting expectations, and enabling regular updates to communicate quality insights across teams.

Learn how to scale testing by processing data in batches from a SQLite database using Great Expectations, partitioning by city to run per-batch validations and checkpoints.

Combine SQLite and pandas data sources in a single workflow, sharing an expectation suite, and create assets, batch definitions, validations, and checkpoints to run cohesive data quality checks.

Learn to build a customized SQL expectation in Great Expectations to validate data directly in the database using an unexpected row query for a temperature range.

Explore the file data context, a persistent alternative to ephemeral contexts, storing configurations, expectations, and validation results on the local file system for production data quality aligned with GCS core.

Switch to the file-based data context and generate a GPS folder with YAML and subfolders for expectations, validations, and checkpoints. Learn how metadata and outputs are stored for reuse.

Reload the file data context, load and reuse configurations with get_context(mode='file'), reload checkpoints, and run validations to preserve results while exploring data sources, data assets, expectations, and validation definitions.

Manage secrets securely in Great Expectations by avoiding embedded credentials, and implement environment variables, secrets management tools, and YAML substitution to protect data and systems.

Define clear expectations, organize them into suites, standardize batch definitions, and integrate great expectations into CI/CD, checkpoints, and monitoring to maintain robust, secure data quality testing.

Scale data quality testing across large data sets with batches and SQLite, integrate multiple data sources, and apply customized SQL expectations with file data contexts for metadata, Great Expectations framework.

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