Task 59:

Batch processing vs stream processing

Explain the difference between batch processing and stream processing. In what business scenarios would you choose one over the other, and what tradeoffs would you consider around latency, complexity, and cost?

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Who is a Data Engineer?

A Data Engineer designs and maintains systems that collect, transform, and deliver data for analytics and business decisions.

What does a Data Engineer do?

Builds reliable ETL/ELT pipelines from multiple data sources.

Prepares clean and structured datasets for analysts and data scientists.

Designs data warehouse schemas (fact and dimension tables).

Monitors data quality, freshness, and pipeline failures.

Optimizes query performance and data processing costs.

What should a Data Engineer know?

Strong SQL: joins, aggregations, window functions, and query tuning.

Data modeling: normalization, denormalization, star and snowflake schemas.

Pipeline orchestration and scheduling principles.

Batch and streaming concepts, incremental loading, and idempotency.

Cloud storage, data warehouses, and observability basics.

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Batch processing vs stream processing