Performance Optimization for OLAP Workloads in SQL Server: Designing Efficient Processes for Collecting and Managing Large Analytic Data Sets

Video description

Performance Optimization for OLAP Workloads in SQL Server: Designing Efficient Processes for Collecting and Managing Large Analytic Data Sets

Video description

This video helps you to design databases and processes that can manage millions or billions (or more) rows of data for analytics and reporting. Analytic workloads follow different architectural and query patterns than standard transactional systems, and the associated data stores grow differently over time as compared to online transaction-processing systems. This video covers the purpose, usage, and effects of growth on analytic data stores with a focus on storing the data directly in SQL Server.

Topics covered in this video include design issues such as compression and advanced indexing techniques for online analytical processing (OLAP) data. Also covered are performance optimization and efficient query patterns. Watching this video helps you learn to better retrieve data with the ultimate goal of answering organizational questions using your analytics data store.

What You Will Learn

Understand the nature of OLAP data, its structure, and its usage
Store OLAP data natively in SQL Server using best-practice options
Minimize resource consumption by compressing your data
Enhance query performance through columnstore indexing
Scale your OLAP query performance to account for future growth
Add new data elements to existing analytic data stores

Who This Video Is For

For data and analytics professionals who create, manage, and query large analytic data stores. For database professionals who expect to do so in the future, who want to know the options that are available in Microsoft SQL Server.

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Overview

Introduction to Analytic Data

OLAP vs. OLTP - How Do They Differ

Assigning Business Goals and Meaning to Data

Classifying Data - Dimensions vs. Facts

Optimizing Data Load Processes

Indexing - B-Tree Versus Columnstore Indexes

ETL, Validation, and Analyzing Data Sources

Caching Challenging Metrics for Quick Retrieval

Scaling Analytic Data

Conclusion

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