2025-10-14

reasoning based projects

🧩 level 1: foundational (core cs, python, sql, os, networking)

goal: build muscle in core cs and fundamental data flow understanding.

1. heap-based ranking system (data structure fundamentals)
2. in-memory key-value store (hashmap + persistence simulation)
3. tcp chat system (socket + concurrency + message delivery)
4. python job scheduler (multiprocessing + process sync)
5. normalization and er diagram case study (manual modeling)
6. simple query planner visualizer (sql cost estimation demo)

⚙️ level 2: data modeling + dbms basics

goal: understand relational modeling, transactions, and schema design.

7. postgres analytics schema (star/snowflake model)
8. nosql replication comparison (mongodb vs postgres)
9. mini db engine in python (sql parser + file i/o)
10. query performance analyzer (indexing and joins)

🚀 level 3: etl + batch pipeline

goal: understand batch processing, transformations, orchestration.

11. weather pipeline (api → s3 → parquet → postgres)
12. data quality framework (mini great-expectations clone)
13. data profiler tool (statistics + nulls + schema validation)
14. incremental etl with airflow (scheduler + retries)
15. airflow dag monitoring dashboard (metrics extraction)

🌐 level 4: api ingestion + external data

goal: learn data ingestion, scraping, api rate control, websockets.

16. crypto price ingestion (websocket → kafka → postgres)
17. news/web scraper (fetch → parse → store → visualize)
18. ingestion framework (retry/backoff logic, monitoring)
19. websocket stream visualizer (flow diagram and stats)

☁️ level 5: cloud + containerization fundamentals

goal: deploy small pipelines using docker, ci/cd, aws services.

20. airflow on ec2 with s3/rds backend
21. lambda-based etl automation
22. spark job on emr reading parquet from s3
23. dockerized data pipeline with docker-compose
24. github actions ci/cd for etl pipelines

💾 level 6: data warehousing + lakehouse design

goal: implement real lakehouse with partitioning and query layer.

25. data lakehouse pipeline (kafka → iceberg → trino → superset)
26. warehouse benchmarking (redshift vs bigquery vs trino)
27. schema evolution demo (iceberg schema change handling)
28. custom partitioning simulator (read-time comparison)

🔥 level 7: big data processing and streaming

goal: handle distributed data, spark, kafka, flink, consistency models.

29. spark batch analytics (millions of rows to parquet)
30. kafka streaming analytics (trade data → spark stream → redis)
31. flink fraud detector (windowed anomaly detection)
32. hdfs simulator (namenode replication logic)
33. kafka-lite broker (python simulation of partitions and offsets)

🧠 level 8: performance and scalability optimization

goal: understand partitioning, caching, vectorization, backpressure.

34. spark tuning benchmark (shuffle vs broadcast join)
35. query optimization analyzer (compare explain plans)
36. vectorized dataframe benchmark (pandas vs polars vs spark)
37. kafka consumer lag visualizer (real-time lag chart)

🏗️ level 9: orchestration + governance + monitoring

goal: build full control layer for metrics, logging, lineage, alerts.

38. airflow monitoring dashboard (grafana + prometheus)
39. etl log ingestion to elk stack (elastic, logstash, kibana)
40. prometheus alerting system with slack webhook
41. pipeline health checker (freshness and row count validation)
42. data catalog / schema registry demo (metadata management)

📡 level 10: system design + hybrid architectures

goal: integrate batch + stream pipelines and event-driven microservices.

43. hybrid data architecture (api → kafka → spark stream → iceberg)
44. fault tolerance simulator (kafka consumer crash → auto replay)
45. event-driven microservice system (notification + accounting)
46. data mesh simulation (domain-based data ownership)

🤖 level 11: ml pipeline preparation

goal: integrate data engineering with ml feature systems.

47. feature pipeline (postgres → cleaning → feast feature store)
48. retraining dag (automated airflow-based ml retraining)
49. feature drift detection (distributional shift alerting)

💰 level 12: decentralized + fintech engineering

goal: combine blockchain, streaming, and analytics into real fintech systems.

50. blazpay transaction analytics (hyperledger + kafka + spark + postgres)
51. bks mygold tokenized asset pipeline (solidity + iceberg analytics)
52. vault reconciliation system (ledger vs vault validation)
53. transaction intelligence (fraud/anomaly scoring)
54. accounting microservice (double-entry with kafka replay)
55. rwa lakehouse (multi-asset data warehouse for on-chain data)
56. oracle connector (price feed + external event integration)

🧾 level 13: documentation + communication

goal: communicate and visualize complex systems effectively.

57. project documentation repo (markdown + mermaid diagrams)
58. data contract generator (schema validation registry)
59. reproducible jupyter pipeline (papermill automation)
60. whiteboard interview exercises (architecture explanation practice)

🧠 learning gradient (conceptual slope)