The Knowledge Tree
Recursive Elaboration: Moving from the "Whole" to the "Parts"
🛠️ Data Systems
Systems where data (quantity, complexity, rate of change) is the primary bottleneck, not CPU power.
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Evaluate a system's architecture based on the trade-offs between Reliability, Scalability, and Maintainability.
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Continuing to work correctly even when things go wrong (Faults).
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The system's ability to cope with increased load without performance degradation.
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Breaking a large dataset into smaller subsets (partitions) assigned to different nodes to enable unlimited horizontal scaling.
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Scaling a system by adding more machines to the resource pool, rather than increasing the capacity of existing machines.
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The metrics used to describe the pressure on a system, such as requests per second or the ratio of reads to writes.
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The ease with which different people can work on the system over time (Operability, Simplicity, Evolvability).
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The ease with which operations teams can keep a system running smoothly.
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The reduction of accidental complexity to make a system easy for new engineers to understand.
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The ease with which a system can be modified to adapt to changing requirements.
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Translating in-memory data structures into bytes for storage or transmission, handling backward/forward compatibility.
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The interface layer. How data is structured (Relational, Document, Graph) vs. how it is stored.
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How we ask for data. Imperative tells the computer *how* to do it (loops); Declarative tells it *what* we want (SQL).
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Choose the right underlying storage engine (LSM vs B-Tree) for a specific workload.
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An append-only sequence of records. The simplest and fastest way to write data.
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Sorted String Tables. Immutable on-disk files where keys are sorted, merged in the background.
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The industry standard for storage. Fixed-size pages on disk updated in place using a wide tree structure.
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Storing data by column rather than by row to optimize for analytical (OLAP) queries.
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Scale out across multiple machines using Replication (copying) and Partitioning (splitting).
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Keeping a copy of the same data on multiple nodes.
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Navigate the dangerous waters of distributed state, transactions, and consensus.
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A mechanism to group several reads and writes into a logical unit.
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The problem of getting several nodes to agree on something (e.g., who is the leader) in an unreliable network.
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The assumption that in a distributed system, packets will be lost, clocks will drift, and nodes will pause.
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The difficulty of ordering events in a distributed system due to clock drift.
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Stop-the-world events (like Garbage Collection) that cause a node to stop responding for seconds.
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A consistency model that ensures operations that are causally related are seen by every node in the same order.
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The strongest consistency model: ensuring the system behaves as if there is only one copy of the data and all operations are atomic.
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Integrate disparate systems by deriving new datasets from a source of truth via Batch and Stream processing.
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A programming model for processing large datasets with a parallel, distributed algorithm.
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Batch processing engines that optimize workflows by keeping intermediate state in memory rather than writing to disk.
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Processing unbounded data as it arrives (event-driven), rather than in fixed-size batches.
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Composing disparate systems (OLTP, Cache, Search) into a coherent whole using Change Data Capture.
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Understanding the responsibility of managing data that affects people's lives (Privacy, Fairness, and Democracy).
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🧠 Pedagogy
The foundational principle that learning is deeper and more durable when it requires cognitive effort.
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Design a study session that builds muscle (memory) rather than just watching others lift weights (reading).
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Defining the two metrics of success for all learning: how long it stays (Durability) and how well you can use it (Transfer).
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The ability to recall knowledge over long periods of time, resisting the natural decay of the forgetting curve.
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The natural biological decay of memory over time if not reinforced.
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The ability to apply knowledge in new, varied, and unfamiliar contexts.
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Understanding how the act of recalling information physically modifies the brain's ability to access it later.
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Cognitive biases that lead learners to mistake familiarity with text for mastery of content.
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Kahneman's model: System 1 is fast/intuitive (recognition), System 2 is slow/analytical (retrieval).
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The debunked theory that instruction must match a student's preferred mode (VARK).
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Mistaking the ease of reading or recognizing a text for actual mastery of the underlying concepts.
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The phenomenon where the act of taking a test produces better long-term retention than an equivalent amount of time spent studying.
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The biological process where recalling a memory makes it plastic and subject to strengthening before being stored again.
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The physical connections in the brain that are strengthened during retrieval.
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Designing learning conditions that slow down the process to improve long-term retention and transfer.
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Distributing study sessions over time with intervals long enough to cause some forgetting.
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Mixing different types of problems or skills within a single session to improve discrimination.
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Moving beyond rote memorization to build complex, interconnected knowledge structures.
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Elaboration: Explaining new material in your own words. Generation: Solving a problem before being taught the method.
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The act of taking a few minutes to review what has been learned and asking oneself questions (Retrieval + Elaboration).
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Existing structures of knowledge that new information 'hooks' into.
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The ability to extract key rules and underlying principles from new information to build a mental framework.
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Internal representations of external reality that allow for inference and prediction.
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A hierarchical model classifying learning objectives from recall (Knowledge) to creation (Synthesis).
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Using objective tools to measure actual competence versus perceived competence.
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Thinking about one's own thinking; monitoring accurate judgment of learning.
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External tools (quizzes, peer review, simulation) used to verify mastery.
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Explicitly explaining the 'why' and 'how' of learning difficulties to students to prevent discouragement.
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Understanding that intelligence is not fixed and that learning physically changes the brain.
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The biological process (mostly during sleep) where temporary memory traces are stabilized into long-term storage and connected to prior knowledge.
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The belief that intelligence can be developed (Dweck).
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Using artificial structures to organize and retrieve unruly information.
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Triggers (images, locations, rhymes) that hook onto a memory trace to pull it out.
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