learn to return what was borrowed to you. sometimes holding on to what belongs to someone else keeps you tied to a chapter you’re supposed to move beyond. return what isn’t yours, settle your debts and make room for your own growth. you can’t fully move forward while carrying what you were only meant to borrow.

Lean is fucking law! I’m entering the lowest BF% I’ve ever been at since I started lifting. Started bulking but stopped tracking calories and reduced my cardio over the years. I ended up putting unnecessary size on due to that. This year I took back control! > tracking calories >10 to 20k steps daily > more zone 2 cardio > intense hypertrophy lifting 5x a week > clean food and peak hydration This has led to: > no brain fog > better sleep quality > improved quality of life > peak performance in the gym and outside in life > hitting flow state in all aspects of life > getting fucking shredded Getting lean improves all aspects of life.

# Learning Palantir Foundry 🚀 Answer "where did this dashboard number come from?" in an instant. Data Lineage is an exploration tool that visualizes the entire flow of your data. 📌 Title and Feature URL Title: Data Lineage URL: 📝 Overview Data Lineage is an interactive visualization tool that comprehensively shows how data flows through the Foundry platform. It helps you understand data movement, dependencies, and transformations across your entire data ecosystem. Because you can trace the lineage from sources through pipelines, the Ontology, and apps as a graph, it sharply reduces the cost of incident response and audit explanations. 🔧 How It Works It represents data dependencies through a graph-based visualization. - Find datasets using project names, table identifiers, or row labels, and browse data directly from Foundry Projects - Expand or collapse ancestor (upstream) and descendant (downstream) relationships for any dataset - View multiple table attributes at once, down to schema details, build timestamps, and source code - Apply custom color schemes to highlight pipeline characteristics such as stale datasets - Create shareable pipeline snapshots to communicate within the team 🛠 Practical Usage - Trace upstream from a dashboard or output dataset to pinpoint the source of a number - When an upstream schema changes, trace downstream to map the blast radius - Color-code stale datasets to discover neglected pipelines - Drill down from a high-level overview into granular technical details like transformation code and execution history - Share pipeline snapshots to document data workflows across functions 🎯 Use Cases - Instantly answering "what is the origin of this dashboard's number" - Identifying the impact scope of upstream schema changes in advance to prevent incidents - Presenting data lineage during audits to cut explanation costs - Finding stale or unused datasets to tidy up pipelines ⚠️ Caveats - This overview page does not explicitly discuss performance or graph-complexity constraints with extremely large pipelines - Lineage covers data flow within the Foundry platform; processing outside the platform is out of visualization scope - The accuracy of lineage depends on transforms and pipelines being properly configured within Foundry #PalantirFoundry# #DataLineage#