How to ensure data quality and integrity using open-source tools for observability in data pipelines.
This article explains the importance of data validation in a machine learning pipeline and demonstrates how to use TensorFlow Data Validation (TFDV) to validate data. It covers the 5 stages of machine learning validation: generating statistics from training data, inferring schema from training data, generating statistics for evaluation data and comparing it with training data, identifying and fixing anomalies, and checking for drifts and data skew.
Use cases of Reverse ETL
There are three primary use cases for Reverse ETL:
Operational Analytics — feeding insights from analytics to business teams in their usual workflows and tools so they can make data-informed decisions.
Data Automation — Automating ad-hoc data requests from other teams. For example, when the finance team requests product usage data for invoicing.
In-App Personalization — with a growing number of data sources, reverse ETL connects those sources to personalize customer experiences.
There are three primary use cases for Reverse ETL:
Operational Analytics — feeding insights from analytics to business teams in their usual workflows and tools so they can make data-informed decisions.
Data Automation — Automating ad-hoc data requests from other teams. For example, when the finance team requests product usage data for invoicing.
In-App Personalization — with a growing number of data sources, reverse ETL connects those sources to personalize customer experiences.
