Definition
ELT Pipeline
Extract, Load, Transform. The process of moving raw data into a warehouse before transforming it — one of the four highest-value use cases for data agents.
From the episode “Data agents you can trust in production. | Pradnesh Patil from Altimate AI”Hear it on the podcast1 episode
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Related terms in Product & Engineering
DownloadsBrett Hurt's term for insights that arrive during meditation.DPIA (Data Protection Impact Assessment)A structured risk review required before running processing activities likely to result in high risk to individuals' rights and freedoms.eBOMEngineering Bill of Materials — the full, structured list of every part in a product and how they relate. The eBOM is the source of truth for what actually gets manufactured.Edge DeviceAny computing hardware that processes data locally without sending it to a remote server or cloud. Examples include smartphones, drones, and the Key Boy. Critical for latency-sensitive and privacy-sensitive applications.Engineer vs. CoderEngineers are system architects — they decide what to build, how components fit together, what problems the architecture needs to solve. Coders implement those specifications. The distinction matters because coders can increasingly be replaced by AI that executes specifications quickly and cheaply; architects cannot. Founders who staff with coders instead of engineers get fast execution without coherent architecture. The correct hire for a technical team is engineering judgment, not implementation capacity.Enterprise Data Technical DebtThe accumulated cost of maintaining, working around, and compensating for outdated, disorganized, or incompatible data infrastructure. Analogous to software technical debt but measured at the data layer rather than the code layer. Includes the ongoing cost of maintaining legacy mainframe systems that cannot be retired without major migration, the cost of manual data cleaning and reconciliation work performed by human analysts, and the cost of enterprise AI projects that underperform because data quality cannot support the intended use case. The Wall Street Journal cites this as a $1.52 trillion annual problem in the US alone. David Carmel uses the metaphor of Victorian era plumbing: the pipes are old, they work after a fashion, but they are fundamentally incompatible with the modern infrastructure being built around them.EpicThe highest-level planning unit in the software development lifecycle: a large body of work defining a significant product capability. An epic breaks into multiple requirements; each requirement breaks into atomic tasks. In AI-native development, epics define multi-feature projects (build a multi-tenant Pilates platform with video libraries, subscriber management, and per-studio payment systems) while requirements define individual features within that project. The hierarchy matters: agents given an epic-level prompt will attempt to solve the entire thing in one shot and fail; agents given an atomic task will succeed consistently.Epic / Milestone / TaskDescending units of work — epics are big features, milestones are subfeatures with user stories and success metrics, tasks are small scoped changes.