Workshop - If you wonder how to apply Data Observability… Come over!

Hi All,

We are excited to invite you to another informative and coding Saturday morning.

Live coding examples of Data Observability Driven Development (DODD) reusable in your pipelines (Python/Pandas) and your Data Warehouse (GCP/BigQuery).

Abstract
“Data pipelines are growing in size, volume, and complexity, with multistage processing and dependencies between various data assets. Today, data engineering comprises 80% to 90% of the work organizations do with data.”
(Gartner, Data Engineering Essentials, Patterns, and Best Practices Published, 27 May 2021)

In other words, data engineering is a fantastic job that is crucial to enabling an efficient data strategy. This is why it is relevant to embrace efficient observability practices (e.g. data quality monitoring) to ensure the stability and sustainability of data.

In this talk, we’ll demonstrate what are the 3 key principles to follow to achieve this essential goal using a Data Observability Driven Development approach.

The main takeaways will be “how” to apply these principles easily in your Python script/notebook or in the Data Warehouse with live real case examples as a workshop.

Bio:
Andy is an entrepreneur with a Mathematics and Distributed Data background.

In the data community, Andy is known as an early evangelist of Apache Spark and the Spark Notebook creator. He is also an O'Reilly author and trainer (e.g. What is Data Observability, What is Data Governance, Distributed Data Science, and Machine Learning Model Monitoring).

Andy is also the founder and CEO of kensu.io , a data observability solution that comes with a specific method: Data Observability Driven Development.