How to Get the Most Out of Amazon Redshift: A Practical Guide for Analytics Teams

1. Column Encoding Isn’t Optional — It’s Foundational

One of Redshift’s strengths is its columnar architecture. But that only pays off if you’re using proper column encoding, which compresses your data efficiently and improves I/O performance.

In many cases, teams skip this step and rely on the AUTO encoding default — which isn’t always optimal.

• Use ANALYZE COMPRESSION to inspect how your data behaves
• Rebuild large tables with better encodings using COPY + CREATE TABLE AS

Even small changes in encoding can dramatically reduce scan times and improve performance, especially on wide tables with many columns.

1. Distribution Keys: The Hidden Bottleneck

Redshift distributes data across compute nodes, and how it does that matters. If your queries constantly require data shuffling across nodes, performance drops.

Choosing the right DISTKEY helps keep related data on the same node — speeding up joins, filters, and aggregations.

• Good candidates for DISTKEY: customer_id, product_id — values used often in joins
• Avoid DISTKEY on high-cardinality columns unless necessary

Even if your data model changes, revisit distribution strategies regularly. What worked when your table had 10 million rows may no longer be efficient when you’re dealing with 500 million.

1. Sort Keys Matter — Especially for Time-Based Data

Redshift isn’t just about what data you store — it’s about how you access it.

If you’re running queries on recent data (e.g. monthly reports, user activity), use a SORTKEY that reflects that pattern. This reduces how much data Redshift needs to scan.

• For time-series data: use event_date or created_at as the leading SORTKEY
• For dimension tables: consider alphabetical or hierarchical fields

Remember: sort keys speed up performance only when your queries are aligned with them.

1. Concurrency Scaling Isn’t a Fix-All

Redshift’s concurrency scaling can help during query spikes, but it's not a license to ignore optimization. If your queries are poorly written, slow joins or full-table scans will still impact performance.

• Always benchmark before enabling concurrency scaling
• Use it for bursty, unpredictable workloads — not as a patch for slow design

Vacuuming and Analyzing: Housekeeping That Pays Off

Redshift doesn’t clean up after itself. When you delete or update data, it leaves “ghost rows” behind. Over time, this bloats your storage and slows scans.

Use VACUUM to re-sort tables and reclaim space. Use ANALYZE to update statistics so the optimizer makes smarter decisions.

• Schedule regular maintenance jobs — weekly or after heavy loads
• Prioritize vacuuming large, frequently updated tables

Skipping this step can cost you hours in query performance later.

1. WLM (Workload Management): Stop Letting Queries Compete Blindly

When multiple users or tools are hitting Redshift at once — dashboards, reports, ad-hoc queries — they all compete for the same resources. That’s where Workload Management (WLM) comes in.

WLM lets you define queues and allocate memory and concurrency slots based on priority.

• Give scheduled dashboards their own queue with fixed memory
• Separate data loads from interactive user queries
• Use short-query queues for fast, lightweight requests

If you’ve ever had a lightweight query blocked for 10 minutes by a large join running ahead of it — WLM could’ve saved you.

Bonus: Redshift’s automatic WLM has improved, but custom tuning is still worth it for high-traffic environments.

1. Query Tuning: Focus on Scan Size, Not Just Runtime

Many BI teams track runtime as the main metric — but that’s only the surface. Redshift’s EXPLAIN and SVL_QLOG views give insight into what’s happening under the hood.

📌 Look for:

• Rows scanned vs. returned — aim for low ratios
• Nested loop joins — often a sign of missing filters or join keys
• Temp table usage — can signal inefficiencies in large aggregations

👉 Consider rewriting long queries as CTE chains with limited scope 👉 Break large joins into staged steps and cache intermediate results

The goal: keep queries lean and predictable, especially as usage scales.

1. BI Tool Integration: Don’t Let Dashboards Become the Bottleneck

Even if your Redshift setup is solid, poor practices in your BI tool can kill performance.

Watch out for:

• Filters that don’t push down to SQL
• High-frequency auto-refresh (e.g. every 30 sec)
• Overuse of unindexed search or wildcards

QuickSight, Power BI, Tableau — they all let you preview query logic before it hits the warehouse. Use those features, and limit access to raw tables where possible. Tip: Use views to pre-define business logic and reduce query duplication across reports.

A global media analytics firm noticed that Redshift performance was tanking during US business hours. After some digging, the root cause was a mix of:

• Overlapping WLM queues
• Dashboards running unnecessary full-table queries
• 5-year-old tables without vacuuming or updated stats

A 3-week optimization sprint led to:

• 40% drop in scan size across key queries
• 2x faster report generation during peak hours
• 30% savings in compute usage due to smarter scheduling

Redshift is incredibly flexible — but it doesn’t optimize itself. Most of its pain points come not from the platform, but from underused features or poor implementation.

The good news? Even small changes can unlock major improvements in both speed and cost.

• Review your table design regularly
• Audit long-running queries quarterly
• Educate your analysts and dashboard users — they influence performance too

Redshift works best when there’s a clear strategy behind how it’s used. With thoughtful setup and ongoing care, it can become the backbone of a modern analytics ecosystem — supporting both nimble startups and large-scale enterprises.