Now that we’ve explored why Azure Synapse is such a powerful platform for cloud-scale analytics, it’s time to dig into one of the most important aspects of performance tuning: table design and data distribution.
Azure Synapse uses an MPP (Massively Parallel Processing) engine under the hood. That means your tables—and how their data is distributed—directly affect query performance. The goal is to minimize data movement and maximize parallel processing across the compute nodes.
Why Distribution Matters
In Synapse, large tables are split into 60 distributions, which are spread across compute nodes. When you run a query, Synapse tries to execute it in parallel on each distribution. If your data isn’t well distributed, some nodes do more work than others—this is called data skew, and it leads to slow performance.
Distribution Strategies in Synapse
There are three main distribution methods you can choose when designing a table:
1. Hash Distribution
- Rows are assigned to distributions based on a hash function applied to a column (the distribution key).
- Ideal for large fact tables that are frequently joined on the distribution key.
- Choose a key with high cardinality and an even data spread.
- Avoid columns like booleans, status codes, or dates.
2. Replicate Distribution
- A copy of the entire table is stored on each distribution.
- Great for small dimension or reference tables used in many joins.
- Eliminates data movement during joins—but use it carefully: tables should be under 2GB to avoid memory issues.
3. Round Robin Distribution
- Rows are assigned evenly but randomly across distributions.
- Default option if no distribution is specified.
- Best for staging tables or intermediate ETL steps.
- Not optimized for joins—can cause data shuffling.
Choosing the Right Strategy
| Strategy | Best For | Pros | Watch Out For |
|---|---|---|---|
| Hash | Large fact tables | Efficient joins on key | Skew if poor key choice |
| Replicate | Small dimension tables | No data movement in joins | Memory usage if too large |
| Round Robin | Staging/intermediate tables | Even distribution | Not join-aware |
Additional Design Considerations
- Partitioning: Use for very large tables with time-based data (e.g., snapshots). Helps with query pruning.
- Columnstore Indexes: Default storage format in Synapse. Compresses data and speeds up scans.
- Heap or Rowstore Tables: Use heap for fast inserts in staging; use clustered rowstore for small lookup tables with frequent point queries.
In Part 4, we’ll shift focus to query optimization techniques—covering statistics, joins, materialized views, and ways to reduce data shuffling in Synapse queries.