Q1. Explain the difference between a data lake and a data warehouse.
A data warehouse stores structured, modelled data optimized for analytics and SQL. It uses schema-on-write: data is cleaned and conformed before loading, typically into s...
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Q2. What is the difference between ETL and ELT?
ETL extracts data from sources, transforms it on a separate processing engine, then loads the clean result into the target. ELT extracts and loads raw data into the targe...
Q3. What is the difference between a star schema and a snowflake schema?
Both are dimensional models. A star schema has one central fact table surrounded by denormalized dimension tables. Each dimension is a single table, so joins stay simple ...
Q4. What is partitioning and why does it matter for big data?
Partitioning splits a large dataset into smaller physical chunks so queries and maintenance target only relevant data. Common strategies are range (by date), list (by reg...
Q5. How does an orchestration tool like Airflow schedule data pipelines?
Airflow models a workflow as a DAG: a directed acyclic graph of tasks with dependencies. A scheduler parses DAG files, creates DAG runs per schedule interval, and queues ...
Q6. What is the difference between batch and stream processing?
Batch processing handles bounded datasets on a schedule. It is simple, high-throughput and easy to reprocess, but results are delayed by the batch interval. Examples incl...
Q7. How do you handle late-arriving data in a pipeline?
Late data breaks the assumption that all events for a time window have arrived. Common strategies: - Watermarks: define how long to wait for late events before closing a ...
Q8. How do you enforce data quality in a data pipeline?
Data quality checks should run as first-class pipeline steps, failing or quarantining bad data rather than silently loading it. Dimensions to test: - Completeness: no une...
Q9. What does idempotency mean in data pipelines and why does it matter?
An idempotent operation produces the same result whether it runs once or many times. Pipelines retry on failure, run backfills and get replayed, so non-idempotent logic c...
Q10. Explain slowly changing dimensions and SCD Type 2.
Dimensions change over time: a customer moves city, a product changes category. A slowly changing dimension strategy decides how history is preserved. - Type 1: overwrite...