Ensuring Data Integrity with Validation and Pipeline Testing

Ensuring Data Integrity with Validation and Pipeline Testing at PyconTW 2024

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1. Shuhsi Lin 20240922 Ensuring Data Integrity with Validation and Pipeline Testing PyConTW 2024 at Kaohsiung Calling from the harbor:Dive into Python
2. About Me Find me on sciwork member Interested in • Agile/Engineering Culture/ Developer Experience • Team Coaching • Data Engineering Shuhsi Lin Working in Smart manufacturing & AI With data and people
3. Agenda 01 Start with basic: the importance of data quality in the data-driven landscape Data Quality/Data Integrity/Data Contract 02 Type of data testing Test strategies 03 Test Frameworks 04 Data Pipeline debt & Recap Great expectation & dbt (dbt test and others) Analytics Development Lifecycle (ADLC)
4. Scenario SmartPizza (like Pizzaxxx, but smarter) • Daily work: get orders with specific recipes to make pizza • Data Tables ◦ Order ◦ Recipes ◦ Customer ◦ Inventory ◦ … ETL/ELT pipelines Data source Data store/target Data applications
5. Data Quality
6. 6 Dimensions of Data Quality The degree to which data correctly reflects the real world object/ event Accuracy Expected comprehensiveness/ are all datasets and the data items recorded Completeness Data across all systems reflects the same information and are in synch with each other across the data stores Consistency Information is available when it is expected and needed Timeliness Means that there’s only one instance of the information appearing in a database Uniqueness Refers to information that doesn’t conform to a specific format or doesn’t follow business rules Validity https://kamal-ahmed.github.io/DQ-Dimensions.github.io/ https://hub.getdbt.com/infinitelambda/dq_tools/latest/
7. Data Quality in AI Impact of poor quality data and analytics The impact of data distrust! Snaplogic “The State of Data Management – The Impact of Data Distrust” Distrust in data Data analytics challenges in organizations
8. Data Integrity vs Data Quality https://www.montecarlodata.com/blog-data-integrity-vs-data-quality/ 1. Entity Integrity 2. Referential Integrity 3. Domain Integrity 4. User-Defined Integrity
9. Entity Integrity Referential Integrity An order references a customer_id that doesn't exist in the customers table. • Tables: orders and customers • Foreign Key: orders.customer_id references customers.customer_id • Table: customers • Primary Key: customer_id Same customer_id exist in the customers table. • Confusion in tracking orders and loyalty points. • Inaccurate customer analytics. • Confusion in tracking orders and loyalty points. • Inaccurate customer analytics.
10. Domain Integrity User-Defined Integrity An ingredients of a recipes that doesn't exist in the inventory table. (out of stock! ) • Column: pizza_type • Valid Values: ◦ Margherita', 'Pepperoni', 'Veggie Supreme” An order includes a pizza_type that is not on the menu. • Issues with order fulfillment. • Inaccurate inventory forecasting. • Inability to prepare certain pizzas due to missing ingredients. • Customers may be disappointed if their preferred menu items are unavailable. • Staff may spend extra time handling stock shortages. Rule: All ingredients listed in the recipes table must exist in the inventory table with sufficient stock.
11. Data Contract
12. What is Data Contracts A data contract is a formal agreement, typically between a service (or producer) and a client (or consumer), that outlines the specifics of data exchange. Its core purpose is to ensure compatibility and clarity in the management and usage of data. producer consumer agreement data exchange The next generation of Data Platforms is the Data Mesh (from The PayPal Technology 1. interface 2. expectations 3. governed 4. explicit Driving Data Quality with Data Contracts (2023) by Andrew Jones
13. Four Principles of Data Contracts 1. Interface 2.Expectations 3. Governed 4.Explicit • Stable and supported interface to the data • Versioned, with a migration path for breaking schema • An agreement between the generators and consumers • Documented, including the structure, schema and semantics • SLOs around the reliability of the data • Well defined ownership and responsibilities • Handling personally identifiable data in-line with company policies and regulations • Controlling access to data • Automated, driven by metadata provided in the data contracts • Accessible data explicitly provided for consumption • Data products that meet business requirements • Generators know the value and are incentivised to produce the data Driving Data Quality with Data Contracts (2023) by Andrew Jones
14. An overview of data contracts in Open Data Contract Standard https://github.com/bitol-io/open-data-contract-standard Contributors to the data contract
15. Open Data Contract Standard https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/README.md different sessions to define agreement of data (data contract)
16. Schema Template for Data Contract define column https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/README.md#dataset-and-schema
17. Data Quality Rules & Parameter quality rule (content) https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/README.md#data-quality
18. (Column level) Data Quality Rules & Parameter column level https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/README.md#data-quality
19. Service-level agreement (SLA) https://github.com/bitol-io/open-data-contract-standard/blob/main/docs/README.md#service-level-agreement latency retention
20. Data(Pipeline) Testing
21. Do you test your code ?
22. Do you test your data pipeline ? Do you test your data ?
23. Assumption about Data Assertion about Code ref: Webinar on: Testing frameworks in dbt. (2023) Two Types of Testing DEV/TEST env CI Code Data Input Output data Code freezed, data changed Code Data Input Output data Prod env Data freezed, code changed Validating the code that processes data before deployed to prod. Validating the data as it's loaded into production.
24. What do we test?
25. Assumption about Data Assertion about Code Two Types of Testing Code freezed, data changed Data freezed, code changed Code: Lint -> Unit test -> Integration testing • Unexpected change from pipeline code refactoring • SQL column definition • Column rename Data: Validate data content • Many NULLs • Data not updating • Value too small/large • … https://www.getdbt.com/blog/building-a-data-quality-framework-with-dbt-and-dbt-cloud
26. Where do we test?
27. Testing in a Data LifeCycle Ingest/collect Store Process Output Source Data Pipeline Bug-specific tests Large Logic Tests Data Test Data Test Data Test https://www.telm.ai/blog/how-to-solve-data-quality-issues-at-every-lifecycle-stage/ Test Code Test Data
28. How do we test?
29. Validating Non-Empty Orders Validate by assertion Scenario: Ensure that each order contains at least one pizza item.
30. Validating Non-Empty Orders Data Validation module/pipeline Scenario: Ensure that each order contains at least one pizza item.
31. Validating Non-Empty Orders Scenario: Ensure that each order contains at least one pizza item.
32. Data Validation Framework? Frameworks Unveiled: Tools for Ensuring Data Quality
33. What is Great Expectations? • A Python based open source tool • It is for validating data, documenting data, and profiling data • It maintains the quality of data and improves communication between teams Key features • Expectations are like assertions in traditional Python unit tests. • Automated data profiling automates pipeline tests. • Data Contexts and Data Sources allow you to configure connections to your data sources. • Tooling for validation are checkpoints for data validation. • Data Docs clean, human-readable documentation.
34. Strata 2018: Pipeline Testing with Great Expectations Data updated in 2024/0922 More about GX https://medium.com/@expectgreatdata/down-with-pipeline-debt-introducing-great-expectations-862ddc46782a https://github.com/great-expectations/great_expectations
35. https://greatexpectations.io/integrations Community Integration https://greatexpectations.io/community
36. What GX Works https://docs.greatexpectations.io/docs/core/introduction/gx_overview
37. GX env Dara context Connect Define expectation Validate https://docs.greatexpectations.io/docs/core/introduction/try_gx ?procedure=sample_code Validate data in a DataFrame
38. https://greatexpectations.io/expectations/
39. dbt (data build tool) Transform data using the same practices that software engineers use to build applications. Version Control and CI/CD Deploy safely using dev environments. Git-enabled version control enables collaboration and a return to previous states. Test and Document Test every model prior to production, and share dynamically generated documentation with all data stakeholders. Develop Write modular data transformations in .sql or .py files – dbt handles the chore of dependency management. https://www.getdbt.com/product/what-is-dbt https://github.com/dbt-labs/dbt-core What is dbt?
40. Test data content • Built-in ◦ Singular data tests ◦ Generic data tests • Packages ◦ dbt_utils ◦ dbt_expectation ◦ Dbt_elementary ◦ … Test data schema • dbt (model) contract
41. dbt.test generic data tests dbt-utils https://docs.getdbt.com/docs/build/data-tests https://github.com/dbt-labs/dbt-utils Scenario: Validate that the order_id in the orders table increments sequentially without gaps Scenario: Ensure recipe_id is unique and not null
42. dbt.test custom Test (SQL) Scenario: Verify that every ingredient listed in the recipes table exists in the inventory table. https://docs.getdbt.com/docs/build/data-tests
43. dbt-expectations Scenario: Validating Total Sales Between orders and sales Tables https://github.com/calogica/dbt-expectations
44. dbt-expectations Scenario: Validating Total Sales Between orders and sales Tables https://github.com/calogica/dbt-expectations
45. dbt contract Scenario: prevent invalid recipes data from entering your datastore https://github.com/calogica/dbt-expectations Contract: enforce contract on data model Column:define data type Constraint: specific constraint • nullness • Uniqueness • Primary keys • Foreign keys
46. Assumption about Data Assertion about Code ref: Webinar on: Testing frameworks in dbt. (2023) Two Types of Testing DEV/TEST env CI Code Data Input Output data Code freezed, data changed Code Data Input Output data Prod env Data freezed, code changed Validating the code that processes data before deployed to prod. Validating the data as it's loaded into production. ETL code ◦ pytest,... model code ◦ dbt unit testing ◦ Recce data content: ◦ pydantic ◦ great expectations ◦ dbt test ◦ dbt_utils/expectatio ns/elementary… data schemas: ◦ dbt data contracts
47. Hub for dbt test https://docs.elementary-data.com/oss/oss-introduction https://www.elementary-data.com/dbt-test-hub
48. Do less for easier adoption 1. Data source integration 2. Available assertions 3. Custom assertions (extensibility) 4. Validation execution/ Integration with existing technology stack How to choose framework https://medium.com/@brunouy/a-guide-to-open-source-data-quality-tools-in-late-2023-f9dbadbc7948
49. Data Pipeline debt
50. https://martinfowler.com/bliki/TechnicalDebtQuadrant.html https://asana.com/zh-tw/resources/technical-debt https://www.sonarsource.com/learn/technical-debt/ Technical debt
51. Pipeline debt Technical debt in data pipeline Down with pipeline debt / introducing Great Expectations. https://greatexpectations.io/blog/down-with-pipeline-debt-introducing-great-expectations • Undocumented/ unmanaged • Untested • Unstable
52. https://www.linkedin.com/pulse/shift-left-testing-revolutionizing-quality-assurance-software/ https://greatexpectations.io/blog/maximizing-productivity-of-analytics-teams-pt3 https://www.acceldata.io/blog/shift-left-data-reliability Shift Left Testing Software development life cycle (SDLC) Shift-left Data Reliability
53. Cost Team capacity Complexity With test Without test Lifecycle of a data pipeline with Tests https://www.karllhughes.com/posts/testing-matters https://greatexpectations.io/blog/maximizing-productivity-of-analytics-teams-pt3 Automation is the key
54. https://github.com/GoogleCloudPlatform/cloud-data-quality https://cloud.google.com/dataplex/docs/data-quality-tasks-overview CloudDQ
55. Auto data quality overview a reference architecture https://github.com/GoogleCloudPlatform/cloud-data-quality https://cloud.google.com/dataplex/docs/auto-data-quality-overview
56. The Analytics Development Lifecycle (ADLC) https://www.getdbt.com/resources/guides/the-analytics-development-lifecycle An integrated, iterative process Ingest/collect Store Process Output Source
57. • Data Contract may help Data Quality/Data Integrity • Data pipeline as code + Infrastructure as code • Test data, test data pipeline(code), and automate it • Try a test framework (GX, dbt-test..) • Start with priority/severity • Integrate with your development/deploy flow (CI/CD) • Integrate with data observability (monitoring, alerting) and operation • Natively/easily integrated into the data orchestrator • Data reliability Engineering (data government in a large scope) • More data testing framework ◦ Soda Core ◦ Pandera ◦ Fugue Recap and Tips
58. Q&A Thanks!