Why has Amperity's Customer Data Platform seen a 15% drop in data ingestion rates over the past week?

Introduction

Amperity's Customer Data Platform has experienced a 15% drop in data ingestion rates over the past week, raising concerns about the platform's performance and potential impacts on customer operations. This analysis will systematically investigate the root cause of this issue, considering both internal and external factors that could contribute to the decline.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to recent platform changes. Have there been any significant updates or deployments to the CDP in the past two weeks?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, a new feature was deployed last week. Impact on approach: If confirmed, we'd focus on the new feature's impact on data ingestion.

• Considering the scale of the drop, I'm wondering about data source changes. Have any major data providers altered their APIs or data formats recently?

Why it matters: External data changes can significantly affect ingestion rates. Expected answer: No major changes reported by data providers. Impact on approach: If true, we'd shift focus to internal systems and processes.

• Given the specific 15% drop, I'm curious about user segments. Is this decline uniform across all customers or concentrated in specific industries or data types?

Why it matters: Segmented impact could indicate specific data handling issues. Expected answer: The drop is more pronounced in retail sector customers. Impact on approach: We'd investigate retail-specific data sources or processing pipelines.

• Thinking about system health, I'm concerned about potential infrastructure issues. Have there been any notable changes in system resource utilization or error rates?

Why it matters: Infrastructure problems can directly impact ingestion performance. Expected answer: Some increased error rates in data processing jobs. Impact on approach: We'd prioritize investigating the data processing pipeline and error logs.