Performance Tuning in Synapse

Azure Cloud Data Engineering Training in Hyderabad – Quality Thoughts

Quality Thoughts offers one of the best Azure Cloud Data Engineering courses in Hyderabad, ideal for graduates, postgraduates, working professionals, or career switchers. The course combines hands-on learning with an internship to make you job-ready in a short time.

Our expert-led training goes beyond theory, with real-time projects guided by certified cloud professionals. Even if you’re from a non-IT background, our structured approach helps you smoothly transition into cloud roles.

The course includes labs, projects, mock interviews, and resume building to enhance placement success.

Why Choose Us?

     1. Live Instructor-Led Training

     2. Real-Time Internship Projects

     3.Resume & Interview Prep

    4 .Placement Assistance

    5.Career Transition Support

Join us to unlock careers in cloud data engineering. Our alumni work at top companies like TCS, Infosys, Deloitte, Accenture, and Capgemini.

Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps

Performance Tuning in Synapse 

Performance tuning in Azure Synapse focuses on optimizing query execution, resource usage, and data access speed. Key strategies include:

Table Design: Use proper distribution methods (hash, round-robin, replicate) to reduce data movement.

Indexing: Create clustered columnstore indexes for large tables (default) to improve compression and query speed; use clustered/non-clustered indexes for small lookup tables.

Partitioning: Split large tables into partitions for faster reads and maintenance.

Statistics: Keep table statistics updated for better query plans.

Resource Classes: Assign appropriate resource classes to balance workload and concurrency.

Materialized Views: Precompute and store aggregated data for repeated queries.

CTAS (Create Table As Select): Use CTAS for staging and transforming data efficiently.

Avoid Over-Querying: Select only required columns, filter early, and minimize joins on skewed data.

Concurrency Scaling: Monitor workload groups and adjust DWUs (Data Warehouse Units) for peak times.

Monitoring & Troubleshooting: Use Query Performance Insight, DMVs, and Azure Monitor to identify bottlenecks.

These practices ensure Synapse delivers high performance for analytics workloads while controlling costs.

Read More

Indexing in Azure SQL

Partitioning in Data Lake

Normalization vs Denormalization

Star and Snowflake Schemas

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