Data Processing and Analytics

5 hours
Progress
4/9 lessons
9 modules

Process large datasets and extract insights using AI-powered analytics — ingestion, processing, feature engineering, monitoring, and visualization.

Course Progress
44%
4/9 lessons completed
310 min
1

Data ingestion strategies

Designing reliable ingestion pipelines: webhooks, CDC, connectors, and durable logs.

35 min
2

Batch processing

ETL/ELT patterns, orchestration, backfills, and cost-aware batch design.

40 min
3

Stream processing

Event-time semantics, windowing, watermarks, and fault-tolerant stream engines.

45 min
4

Data transformation

Schema evolution, idempotent transforms, and incremental processing strategies.

30 min
5

Storage & partitioning

Append-optimized stores, partitioning schemes, lifecycle and cost trade-offs.

30 min
6

Analytics & visualization

Building dashboards, aggregation strategies, and KPI design for streams and batches.

40 min
7

Feature engineering for ML

Online vs offline features, feature stores, and freshness/correctness considerations.

35 min
8

Monitoring & alerting

Observability for data systems: lag, data quality checks, and SLA alerts.

25 min
9

Capstone: pipeline to insights

End-to-end project: ingest, process, analyze, and publish insights from sample dataset.

30 min
Course includes practical examples, pseudo-code, and a capstone project to apply an end-to-end pipeline.

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