Data Pipelines Explained: Batch, Streaming and CDC
Data Engineering & Processing Pipelines: leave with an orchestrated ELT pipeline with validation checks, schema-change handling and a backfill run.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why data engineering & processing pipelines matters — the common problem: Reports break silently when upstream data changes shape or arrives late.
- 2Core concepts in plain language: Data ingestion, ETL, ELT, batch processing, stream processing, change data capture
- 3Going further: orchestration, schema evolution, data validation, event-time processing, late-arriving data, backfills
- 4Framework walkthrough: Normalization, Dimensional Modeling, ELT Pipelines, Data Quality Dimensions
- 5Practical workflow, built live: An orchestrated ELT pipeline with validation checks, schema-change handling and a backfill run.
- 6How to measure it: Query latency, Pipeline freshness, Data-quality check pass rate, Failed job rate
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- An orchestrated ELT pipeline with validation checks, schema-change handling and a backfill run.
- A working understanding of Normalization and Dimensional Modeling
- A short list of measures to track: Query latency, Pipeline freshness, Data-quality check pass rate
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