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Scale and Optimize Data Engineering Pipelines with Software Engineering Best Practices: Modularity and Automated Testing

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Databricks
Databricks

In rapidly changing conditions, many companies build ETL pipelines using ad-hoc strategy. Such an approach makes automated testing for data reliability almost impossible and leads to ineffective and time-consuming manual ETL monitoring.

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Scale and Optimize Data Engineering Pipelines with Software Engineering Best Practices: Modularity and Automated Testing © 2020 / Levi Strauss & Co. Qiang MENG Sr. Data Engineer, Levi Strauss & Co.
Qiang MENG • Levi Strauss & Co., Belgium • Databricks – AWS – Airflow – Spark - Dataiku • H&M Group, Sweden • Databricks – Azure – Airflow – Spark - Hive • Softbank Robotics, France • AWS – Luigi – Spark © 2020 / Levi Strauss & Co.
© 2020 / Levi Strauss & Co.
Levi’s Data, Analytics & AI © 2020 / Levi Strauss & Co. Demand Forecast Loyalty Price & Promo Recommendation 0% 10% 20% 30% 40% 50% 60% 70% Q3 Digital Sales Increase USA EU AMA NLP Allocation Computer Vision
Agenda I. Hypothesis • A Data Engineering project in Apache Airflow II. Optimization • Software Engineering best practices III. Auto-testing • Unit test, Functional test, End to End test, and Smoky test © 2020 / Levi Strauss & Co.
I. Hypothesis © 2020 / Levi Strauss & Co.
© 2020 / Levi Strauss & Co. Hypothesis Data Engineering – Cloud - Airflow - TB-level - Batch
© 2020 / Levi Strauss & Co. Hypothesis Dev Environments ▪ Local – Dev – Preprod – Prod Sample Data ▪ ETL in local / Dev Code Versioning – GIT • feature - dev - release - hotfix – master Conventions • DB, Schema, Table, Column, Pipeline Container-orchestration • Docker, K8S, … Spark clusters • Databricks, EMR, … Data Versioning • Delta Lake, … CICD
Airflow ▪ airflow • dags • operators • … ▪ utils ▪ modules ▪ docs ▪ tests ▪ others • airflow • dags • perators • … • others © 2020 / Levi Strauss & Co.
II. Optimization © 2020 / Levi Strauss & Co.
A Big Data ETL
Optimization - Airflow DAG
Optimization - Airflow DAG A simple template of tasks – Data Feeds Based © 2020 / Levi Strauss & Co.
© 2020 / Levi Strauss & Co. Optimization - Airflow Operator • Default Operators • Keep as they are • Customized Operators • Hive • Spark • Deduplication • Data Validation • Data Profiling • … Do the real work
Optimization - Modules and Utils ▪ Modules ▪ Utils
Optimization - OOP
Optimization - OOP © 2020 / Levi Strauss & Co.
Optimization - Design Patten © 2020 / Levi Strauss & Co.
Optimization - Formatting © 2020 / Levi Strauss & Co.
Optimization - Docs • Docstring - Sphinx • Data Catalog – Markdown © 2020 / Levi Strauss & Co.
Docstring © 2020 / Levi Strauss & Co.
Auto Data Catalog with Markdown © 2020 / Levi Strauss & Co.
III. Auto-testing © 2020 / Levi Strauss & Co.
Auto-Testing • Unit tests • Integration tests • End-to-End tests • Smoky tests © 2020 / Levi Strauss & Co.
Unit Tests © 2020 / Levi Strauss & Co.
Integration Tests © 2020 / Levi Strauss & Co.
End-to-End Tests © 2020 / Levi Strauss & Co.
Smoke Tests © 2020 / Levi Strauss & Co.
READY FOR THE NEXT 167 YEARS © 2020 / Levi Strauss & Co.
Sign off/Thank You/Questions? © 2020 / Levi Strauss & Co. READY FOR THE NEXT 166 YEARS THANK YOU © 2020 / Levi Strauss & Co.
Feedback Your feedback is important to us. Don’t forget to rate and review the sessions. © 2020 / Levi Strauss & Co.

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