Overview
This project simulates an enterprise data pipeline similar to systems used in financial
services for processing trade records, client data, and operational logs. It demonstrates
end-to-end data engineering: ingestion of raw files, structured parsing, validation and
cleaning, schema transformation, and automated report generation.
All data used in this project is entirely fictional and original. No proprietary information,
internal tools, or confidential systems are referenced. This project was built independently
to showcase the types of skills applied in a professional environment — not to
replicate any specific system.
Pipeline Architecture
The pipeline follows a five-stage model: Input, Parse, Clean, Transform,
Output. Each stage is independent and communicates via an in-memory data
structure, mirroring how real ETL pipelines use message queues or intermediate storage.
# Pseudocode: Pipeline Execution
def run_pipeline(source_file):
raw_data = ingest(source_file) # Stage 1: Input
parsed = parse(raw_data) # Stage 2: Parse
cleaned = validate_and_clean(parsed) # Stage 3: Clean
transformed = transform(cleaned) # Stage 4: Transform
export_to_excel(transformed) # Stage 5: Output
log(f"Pipeline complete: {len(transformed)} records exported")
Each stage logs its progress to a real-time console, providing visibility into long-running
processes — a pattern commonly used when monitoring batch jobs on virtual machines.
Data Parsing
The parser supports three input formats:
- CSV — Comma-separated values with header row detection. Handles common
issues like trailing commas and inconsistent quoting.
- JSON — Array-of-objects format. Validates structure and extracts field
names automatically.
- Text logs — Regex-based extraction of timestamps, severity levels,
components, and messages from semi-structured log files.
The parser auto-detects the format based on the selected data source or the file extension of
uploaded files.
Data Cleaning
The cleaning stage applies schema-based validation rules to every record:
- Required field checks — Missing values are either filled with defaults
or flagged as errors.
- Date normalization — Inconsistent formats (MM/DD/YYYY, YYYY/MM/DD) are
standardized to YYYY-MM-DD. Invalid dates (e.g., month 13) are rejected.
- Enum validation — String values are uppercased and checked against
allowed value lists.
- Range validation — Numeric fields are checked against minimum and
maximum bounds (e.g., quantity must be 1–100,000).
- Sentinel replacement — Values like "N/A", "null", and empty strings in
required fields are replaced with appropriate defaults.
Each issue is logged to the console with its row number, field name, and a clear description
— making the pipeline easy to audit and debug.
Transformation
The transform stage enriches valid records with computed columns:
- Total Value — Calculated as quantity × price for trade records.
- Desk Full Name — Short desk codes (e.g., "EQ-NA") are mapped to
readable names ("Equities - North America").
- Data Quality Score — A 0–100 score based on how many fields required
correction. Records that needed no fixes score 100.
Error-flagged records are excluded from the final output but remain visible in the console
log for transparency.
Export & Delivery
The output stage provides three views of the processed data:
- Table View — An interactive HTML table with sticky headers for browsing
processed records.
- JSON View — The complete dataset in structured JSON format, ready for
API consumption.
- Statistics — Summary metrics including total trade value, average data
quality, instrument counts, and error/warning breakdowns.
Two export options are available:
- CSV — Downloads a properly escaped comma-separated file.
- XLSX — Generates a real Excel workbook using the SheetJS library, with
formatted headers and auto-sized columns. Falls back to CSV if the library is
unavailable.
Real-World Parallels
This project demonstrates skills directly applicable to enterprise data engineering:
- File ingestion from shared drives and SFTP servers — mirrored by the
file drop zone and sample data selection.
- Schema validation against data dictionaries — mirrored by the cleaning
stage's type checking and null handling.
- Transformation to downstream formats — mirrored by the transform
stage's field renaming and computation.
- Automated Excel report generation for stakeholders — mirrored by the
.xlsx export with formatted headers.
- Real-time monitoring of long-running batch processes on VMs — mirrored
by the live console with progressive logging.
- Clear technical documentation for team handoffs — mirrored by this
documentation section.
The technologies used (Python-style automation logic, SQL-like data validation, structured
logging) reflect the same toolkit applied in professional financial technology environments.