Data Reliability Automated Liang-Bin Hsueh (hlb), Cofounder and COO of InfuseAI PipeRider https://www.piperider.io/

About Me • Cofounder of InfuseAI • ❤ Open Source • Reach me • Twitter: hlb • Facebook: iamhlb • LinkedIn: iamhlb

About InfuseAI Data Reliability Automated Streamline MLOps Our Products trusted by research institutes and leaders in sectors including FSI, manufacturing, and healthcare projects our product 
 manage for clients GPUs our product
 manage for clients 500+ 100+

Unity Software Inc. (NASDAQ:U) Q1 2022 Earnings Call Transcript “We lost the value of a portion of our data, training data due in part to us ingesting bad data from a large customer. We estimate the impact to our business of approximately $110 million in 2022 …”

Data Reliability • data is complete and accurate, and it is a crucial foundation for building data trust across the organization • Types of Data • Operational Data: data produced operationally. customer impressions, transaction records, … • Analytical data: data used analytically. Marketing churn, clickthrough rates, impressions by global region, … • Operational data runs your business; Analytical data manages your business

Fighting with Bad Data • Duplicate records • Incomplete fi elds like missing values • Inaccurate data entries like “InfuseAI”, “Infuse AI”, ”InfuseBI”, “Infuse CI” • Incompatible software migration like from one database to another • Schema or semantic changes

Data Analysis in Reality :(

Data Pipeline Thank to Karen Hsieh [Link to deck]

Thank to Karen Hsieh [Link to Miro board] 😍

Data Quality Issue across Data Pipeline

Data Quality Issue across Data Pipeline 💣 👻 👻 👻 ⚠ errors happened here data a ff ected data a ff ected data a ff ected business impact

Data Quality Issue across Data Pipeline ⚠ ✅ ✅ ✅ ✅ ✅ ❌ ❌ ❌ ✅ ✅ ❌

Data Reliability Automated https://www.piperider.io/

PipeRider: Data Reliability Automated https://www.piperider.io/ • PipeRider ensures data reliability through automating data testing and monitoring • Key Features • Instant quality assessment in HTML report • Report comparison • Extensible custom assertions • Works with existing dbt projects • Automatic test recommendations

How to use # install 
 $ pip install -U piperider # init piperider inside your project 
 $ piperider init # run and generate report 
 $ piperider run # compare different reports over time 
 $ piperider compare - reports

Assertions Column Assertions - assert_column_exist - assert_column_in_types - assert_column_min_in_range - assert_column_max_in_range - assert_column_not_null - assert_column_null - assert_column_type - assert_column_unique Table Assertions - assert_row_count_in_range Customize assertions by writing plugins: 
 https: / / docs.piperider.io/data - quality - assertions/custom - assertions

Integrate with dbt https://github.com/dbt-labs/dbt-core dbt enables data analysts to custom-write transformations through SQL statements. piperider integrates with your dbt project so you can init with zero setup. dataSources: - name: my_dbt_project type: postgres dbt: prof i le: my_dbt_project projectDir: . # the path to dbt_project.yml prof i lesDir: ~/.dbt # the path to the directory of dbt prof i les.yml …

Report

Comparison

CI Integration GitHub Actions as example https://docs.piperider.io/how-to/github-action

No content

DEMO Data Source: Video Games Sales Dataset

Roadmap https://github.com/infuseai/piperider • better report formats • better assertion recommendations • serve command to run a local web server • more integrations like AWS redshift

Thank You [email protected] https://piperider.io