Data Breakthroughs Podcast - Episode 1 Show Notes
Episode Title: Preventing Data Pipeline Breaks That Impact Business Decisions
Guest: Ilya Vladimirskiy, data engineering leader with 20+ years of experience
Episode Overview
In our pilot episode, we tackle a critical challenge facing data teams: how to prevent data pipeline breaks that damage trust in data and impact decision-making. Ilya shares insights from his experience at an IoT startup where schema changes in source systems frequently broke dashboards used by key stakeholders.
Key Problem Explored
Data producers (ERP and web shop teams) constantly changed data structures without coordination
Broken pipelines led to unavailable or incorrect Tableau dashboards
Business stakeholders lost trust and reverted to manual processes
Tensions developed between data teams and data-producing engineering teams
Must-Listen Segments (If You're Short on Time)
The Trust Problem (2:34-3:28)Why pipeline breaks are devastating: "The problem is if you do it a few times, people stop trusting your data and either they get back to purely gut feelings... or they bypass your systems."
Five Actions for Reliable Pipelines (40:40-41:20)Our concrete, actionable recommendations to prevent pipeline breaks.
People vs. Technology Insight (35:41-36:24)"80% people, 20% technology" - why even perfect tools won't solve your problems if teams aren't aligned.
Our Solution Framework
People & Communication
Create transparency between data producers and consumers
Establish shared understanding of data importance and impact
Documentation & Contracts
Define and document data events and required metadata
Create formal contracts between data producers and consumers
Implementation & Validation
Set up automatic data checks and rejection events for schema validation
Separate raw data landing zones from consumption zones
Monitoring & Alerts
Implement data monitoring tools to catch issues before they reach users
Establish SLAs based on business impact
Incident Management
Create a formal data incident process with severity levels
Document learnings to prevent recurring issues
Key Insights
No Universal Solution: There's no one-size-fits-all approach to data pipeline problems
Data Mesh Relevance: Data mesh principles remain valuable, but must be adapted to your organization
People First: People and culture account for 80% of successful data operations; technology is just 20%
Links Mentioned:
Ilya's LinkedIn: https://www.linkedin.com/in/bkmy43/
Bruin: https://getbruin.com/
Data Mesh Manager: https://www.datamesh-manager.com/
Open standards for data contracts
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Whiteboard diagram from our episode
Connect & Contribute
Problem Gambler by Matt Harvey from Audiosocket
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