In the previous post, we explored the foundational difference between SMP and MPP systems, and why MPP is better suited for large-scale analytics.
Now, let’s dive into how Azure Synapse Analytics builds on that MPP architecture to become one of the most powerful and flexible cloud data platforms available today.
What is Azure Synapse?
Azure Synapse is Microsoft’s modern analytics platform that brings together data integration, big data, and data warehousing into a single unified environment. It offers both:
- Dedicated SQL Pools (for MPP-style performance)
- Serverless SQL Pools (for on-demand querying)
Key Highlights:
- MPP-based compute engine for scalable analytics
- Separation of storage and compute for cost efficiency
- Deep integration with Azure Data Lake, Power BI, and Azure Machine Learning
- Supports SQL, Spark, Pipelines, and tools like dbt and Synapse Studio
Why Not Just Use Azure SQL Database?
Azure SQL Database is fantastic for transactional workloads and smaller-scale analytics—but it’s built on SMP architecture.
That means:
- It’s vertically scalable
- You’ll hit limits sooner with large datasets or complex queries
When to Choose Synapse Over Azure SQL:
- You're dealing with billions of rows or terabytes of data
- You need parallel processing for faster ETL or reporting
- You want tight integration with big data or ML workflows
Serverless vs. Dedicated SQL Pool
Azure Synapse gives you two flavors of compute:
| Feature | Dedicated SQL Pool | Serverless SQL Pool |
|---|---|---|
| Compute | Provisioned | On-demand |
| Performance | High | Moderate |
| Ideal For | Repeatable workloads | Ad hoc exploration |
| Pricing | Per DWU/hour | Per TB scanned |
| Infra Mgmt | Required | None |
Many teams use both:
- Serverless for exploration and discovery
- Dedicated for production pipelines and BI dashboards
How Synapse Compares to Snowflake, BigQuery & Redshift
All of these are solid modern cloud DWH solutions, but each shines in different contexts:
| Platform | Pros | Cons |
|---|---|---|
| Synapse | Full Azure ecosystem integration, hybrid support (SQL/Spark), choice of serverless or provisioned | Less cross-cloud flexibility |
| Snowflake | True multi-cloud support, strong separation of compute/storage, great ecosystem | Proprietary, can get pricey at scale |
| BigQuery | Fully serverless, fast on Google-native workloads | Less customizable, BigQuery SQL dialect |
| Redshift | Integrates well with AWS, good for traditional BI | Slower evolution, less flexibility than Synapse/Snowflake |
If you’re already in the Azure ecosystem or need a unified platform with Spark and SQL side-by-side—Synapse is hard to beat.
When Not to Use Synapse
Azure Synapse is a great platform, but it's not the perfect choice in every scenario. You may want to look elsewhere if:
- You're fully committed to another cloud provider (e.g., AWS or GCP)
- You're building a lightweight app with small, transactional workloads (consider Azure SQL or Cosmos DB)
- You need fast, serverless analytics with minimal setup (BigQuery might be simpler)
- You want multi-cloud support or broader vendor neutrality (Snowflake excels here)
- High availability is mission-critical and the difference between 99.9% (Azure) and 99.99%+ (AWS) SLAs is a key factor — in this case, something like Amazon Redshift may be a better fit
Designed for the Modern Data Stack
Synapse isn’t just a data warehouse—it’s a full analytics workspace:
- Orchestrate data flows with Synapse Pipelines (or ADF)
- Transform with SQL, Spark, or Data Flows
- Connect natively to Power BI, Azure ML, Azure Purview, and dbt
Whether you're:
- Building ELT pipelines
- Serving interactive dashboards
- Prepping data for ML
Synapse gives you the cloud-native, unified platform to do it all.
Coming Up in Part 3...
In the next post, we’ll take a deeper look at table design and data distribution strategies in Synapse—and why those choices are crucial for performance at scale.