Azure Data Engineer - Part 22 - Optimize Data Warehouse Query Performance in Azure Synapse Analytics

Join us in this weekly series and learn how to become an Azure Data Engineer, how to integrate, transform, and consolidate data from various structured and unstructured data systems into structures that are suitable for building analytics solutions.

Responsibilities for this role include helping stakeholders understand the data through exploration, building, and maintaining secure and compliant data processing pipelines by using different tools and techniques. You will learn how to use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.

An Azure Data Engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. This professional deals with unanticipated issues swiftly and minimizes data loss. An Azure Data Engineer also designs, implements, monitors, and optimizes data platforms to meet the data pipeline needs.

A candidate for this certification must have a solid knowledge of data processing languages, such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns. Specifically, you should have the knowledge equivalent to the Azure Data Fundamentals certification.

This is part 22: Optimize Data Warehouse Query Performance in Azure Synapse Analytics. In this module, we will learn the techniques that you can use to optimize query performance within Azure Synapse Analytics.

In this module you will:

• Understand performance issues related to tables
• Understand table distribution design
• Use indexes to improve query performance
• Create statistics to improve query performance
• Improve query performance with Materialized Views
• Use read committed snapshot for data consistency
• Optimize common queries with result-set caching