Global Market Data Platform Blueprint
Architecture for market, stock, ETF and index data using streaming plus daily batch patterns.
Architecture and project examples connecting data engineering to financial-data ingestion, analytics and AI/ML workflows.
Architecture for market, stock, ETF and index data using streaming plus daily batch patterns.
ETL and analytical preparation for customer behavior, churn features, data quality and BI reporting.
Transaction analytics for segmentation, cohort trends, conversion and anomaly monitoring.
A roadmap for structured notes, technical references, interview preparation and hands-on learning labs.
These diagrams are learning/architecture material; they should not be interpreted as proof of production deployment unless explicitly stated.
Initial streaming and batch design using Python ingestion, Kafka, Spark, PostgreSQL, Airflow and analytics tooling.
AWS-managed patterns for ingestion, streaming, processing, S3 lake storage, serving and analytics.
Retries, idempotency, checkpointing, timezone-aware scheduling, DLQ and reprocessing controls.
Source control, build/test, encrypted artifacts, deployment approvals and rollback patterns.
Schema validation, governance, API rate limits and production safeguards.
Quota management, access governance, data controls and artifact security.
Event-driven ingestion patterns for file arrival, retries and operational notifications.
Multi-layer data lake, serving, analytics, orchestration, monitoring, security and DR.
A production-oriented global market-data platform blueprint with real-time and batch processing.