Amazon Redshift is one of the most popular cloud data warehouses used in modern big-data architectures. Whether you are building ELT pipelines, performing analytics at scale, or optimizing workloads, Redshift is a crucial skill for Data Engineers.
In this article, I’ve compiled the 25 most frequently asked AWS Redshift interview questions, along with answers.
1. What is Amazon Redshift?
Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse that stores data in a columnar format and uses MPP (Massively Parallel Processing) for fast analytics queries.
2. How is Redshift different from RDS or OLTP databases?
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Redshift → OLAP, analytical queries, columnar storage, MPP.
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RDS/OLTP → transactional workloads, row-based, optimized for reads/writes.
3. What are Redshift Clusters?
A cluster is the core compute unit in Redshift, consisting of one leader node and one or more compute nodes.
4. What is the role of the Leader Node?
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Accepts SQL queries
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Generates query execution plans
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Coordinates parallel execution across compute nodes
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Aggregates results
5. What are Redshift Compute Node types?
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DC2 → Dense Compute (SSD storage, fast performance)
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RA3 → Decoupled storage using Redshift Managed Storage (recommended)
6. What is Redshift Managed Storage (RMS)?
RMS allows storage to scale independently of compute using RA3 nodes, improving cost efficiency.
7. What is Columnar Storage in Redshift? Why is it used?
Data is stored column-wise, reducing I/O by scanning only the required columns, improving query speed drastically.
8. What are Distribution Styles in Redshift?
They determine how data is distributed across compute nodes.
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KEY – Same key goes to same node
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EVEN – Even distribution
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ALL – Full table copy to each node (used for small dimension tables)
9. What are Sort Keys?
Sort keys define how data is physically sorted on disk. They help reduce scan time for range queries, joins, and filters.
10. What is the difference between Compound and Interleaved Sort Keys?
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Compound SK → Sorting in a fixed hierarchy (best for range filters)
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Interleaved SK → Equal weight to each column (best for multi-column filters)
11. What is Vacuum in Redshift?
VACUUM reclaims space, re-sorts data, and improves performance after large DELETE/UPDATE operations.
12. What is Analyze in Redshift?
ANALYZE updates table statistics to help the query optimizer choose the best execution plan.
13. What is Redshift Spectrum?
Spectrum allows Redshift to query external data directly from S3 using external tables.
14. What is the difference between Redshift vs Redshift Spectrum?
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Redshift → stores data inside cluster; fastest queries
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Spectrum → reads data from S3; good for infrequent or huge datasets
15. What are External Tables in Redshift?
Tables stored in S3 but queried through Redshift Spectrum. Defined using Glue Data Catalog.
16. How does Redshift handle compression?
Redshift automatically applies optimal column-level compression encoding using ANALYZE COMPRESSION.
17. What is Workload Management (WLM)?
WLM allows you to define multiple queues and assign memory/slots to manage concurrent workloads.
18. What are Materialized Views in Redshift?
Pre-computed result sets stored physically. They speed up complex queries and support Automatic Refresh.
19. What is Concurrency Scaling?
It automatically adds extra clusters during peak demand to handle spikes in user queries.
20. What is Result Caching?
If the same query is repeated, Redshift returns results from cache without re-executing the query.
21. How do you load data into Redshift?
Common methods:
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COPY command from S3 (most efficient)
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Kinesis Firehose → Redshift
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Glue ETL
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S3 → Spectrum → Redshift
22. What is the COPY command? Why is it used?
COPY loads data from S3 into Redshift in parallel using multiple slices → fastest ingestion method.
23. What is UNLOAD in Redshift?
UNLOAD exports query results from Redshift into S3 in parallel as CSV/Parquet.
24. What are Late Materialization and Zone Maps?
Redshift uses zone maps & metadata to skip blocks of irrelevant data → improves scan speed.
25. What are Best Practices to Optimize Redshift Performance?
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Choose correct distribution & sort keys
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Use compression encodings
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Avoid large DELETE operations
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Use VACUUM & ANALYZE regularly
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Prefer UNLOAD/COPY for data movement
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Use RA3 nodes for better cost & scale
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Leverage Spectrum for historical/large cold data
Final Thoughts
AWS Redshift continues to be a core component of the modern data stack. If you’re preparing for a Data Engineering interview, mastering these Redshift fundamentals—distribution, sort keys, Spectrum, COPY/UNLOAD, and WLM—will give you a strong advantage.