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1‑Day Data Engineering: A Practical, Systematic Guide
(A step‑by‑step playbook for building, testing, and deploying an end‑to‑end data pipeline in one working day.)
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1. Executive Overview
Item What it means
Data Engineering Designing and implementing the architecture that moves raw data from sources to analytics or production systems.
One‑Day Sprint A tightly scoped, timeboxed effort (≈ 8 hrs) aimed at delivering a functional pipeline for a single business need.
Outcome Working code that extracts data, transforms it into a useful format, and loads it into the target system, ready to be used by analysts or applications.
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2. Success Metrics
Data Completeness – All required records from source are present in the destination.
Data Accuracy – No transformation errors; data passes validation checks.
Performance – Load time ≤ 15 min (or per business SLA).
Reliability – Pipeline can be re‑run without manual intervention; idempotent design.
Maintainability – Code passes code‑review, follows style guidelines, includes unit tests.
3. Key Design Principles
Principle Description Practical Example
Single Responsibility Each component does one thing well. Separate classes for data extraction, transformation, and loading.
Idempotence Re‑running the pipeline yields same result. Use upsert operations or delete‑and‑insert with a unique key.
Declarative Data Model Define what to store, not how. ORM models mapping tables to classes.
Schema Evolution Handle changes in source data gracefully. Map source columns to model fields; use optional/nullable types.
Immutable Domain Objects Prevent accidental mutation. Use dataclasses with frozen=True or immutable structures.
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3. The Repository Pattern
What is a Repository?
A repository mediates between the domain (business logic) and data mapping layers, providing an abstraction over persistence mechanisms.
Interface: Exposes methods like `add`, `remove`, `find_by_id`, `list_all`.
Implementation: Uses ORM or raw SQL; hidden from the rest of the codebase.
Benefit: Decouples domain logic from database specifics, eases unit testing (mock repositories), and centralizes data access concerns.
Repository Interface Example
from typing import Protocol, Iterable, TypeVar, Generic import uuid
T = TypeVar('T')
class IRepository(ProtocolT): def add(self, entity: T) -> None: ...
Inversion of Control: Dependencies are injected into constructors rather than instantiated internally.
Dependency Inversion Principle: High-level modules (UI, Domain) depend on abstractions; low-level modules (Data Access) depend on the same abstractions.
4. Illustrative Pseudocode
Below is a concise illustration of how these patterns interoperate in code. The focus is on structure rather than language-specific syntax.
4.1. Domain Model: `Invoice`
class Invoice: id: Guid number: String issueDate: Date dueDate: Date items: ListInvoiceItem totalAmount: Decimal
private method recalculateTotal(): sum = 0.0 for each item in this.items: sum += item.quantity * item.unitPrice this.totalAmount = sum
class InvoiceItem: property description: string property quantity: int property unitPrice: float
constructor(description, quantity, unitPrice): set properties accordingly
Explanation of the Pseudocode
The `Invoice` class models an invoice, with properties such as `invoiceNumber`, `dateIssued`, `customerName`, and a list of `items`.
It includes methods to add items (`addItem`) and calculate totals (`calculateTotal`).
The `Item` class represents individual line items in the invoice, including quantity, unit price, and tax rate.
The `TaxRate` enumeration defines possible tax rates (e.g., 0%, 10%).
This design demonstrates object-oriented modeling of a financial document.
Section: Data Structures for Financial Records
In this section, we outline the data structures that will be used to store financial records, including accounts, transactions, and balances. We describe how these structures are related and how they can be accessed and manipulated in the system.
